Standardized extraction of quantitative phenotypes from images is increasingly important across plant biology, from ecological and evolutionary studies to genetics, breeding, and functional genomics. However, as large image datasets are increasingly used for trait analysis, many biologically relevant traits, including size, shape, color, and spatial patterning, are still measured manually or using fragmented semi-automated workflows. These limitations reduce throughput, reproducibility, and accessibility, especially for researchers without computational expertise. Here, we present BioIMA, an open-source desktop tool for rapid and standardized phenotyping from biological images. BioIMA integrates foundation model-based segmentation with automated trait computation, allowing users to extract quantitative measurements from images through an intuitive graphical interface and without model training. To validate its performance, we quantified a set of knot morphological traits in two Populus species, as these measurements are typically time-consuming to perform manually. Automatic measurements showed strong agreement with manual ImageJ-based measurements (R2 > 0.95), while reducing per-image processing time by approximately 75% (from ~15 s to ~4 s). BioIMA was further applied to diverse plant datasets, including Helianthus and Rhododendron images with varying morphologies and background conditions. Although developed for plant phenotyping, BioIMA may also be extended to other biological samples where region-based size, shape, or color traits are of interest. By combining accessibility and standardization in a lightweight local application, BioIMA provides a practical community resource for image-based phenotyping in ecological and evolutionary studies.
Why it matches plant phenotyping methods植物画像から形態形質を自動抽出するツールの開発と、手動測定との性能検証が中心であるため。
abstractHere, we present BioIMA, an open-source desktop tool for rapid and standardized phenotyping from biological images.
Reproduction assets foundThe paper's own phenotyping tool BioIMA (source code, documentation, example datasets, and user manual) is publicly available on the authors' GitHub repository, directly supporting the paper's image-based trait extraction and validation analyses.Code · publicis powered by embedded models
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currently including SAM (Kirillov et al., 2023) and mobile SAM (Zhang et al., 2023),
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which are executed locally through ONNX Runtime for efficient inference without
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internet connectivity. Source code, documentation, example datasets, and a user manual
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are publicly available on GitHub (https://github.com/jingwanglab/BioIMA).101
preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this
this version posted September 3, 2026.
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https://doi.org/10.64898/2026.08.30.747465
doi:
bioRxiv preprintOpen asset ↗jingwanglab/BioIMApdf-raw-page:4 lines:1-60Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
High-throughput phenotyping of lettuce seedlings is highly prone to background confusion because the seedlings are small, have weak textural features, and exhibit spectral reflectance similar to that of the substrate. Traditional single-visual-modality approaches struggle to achieve reliable structural and physiological characterization simultaneously under the repetitive backgrounds and dense arrangements typical of greenhouse tray cultivation. To address these challenges, we establish a multimodal 3D phenotyping framework tailored for controlled agriculture environments, enabling the quantification of structural and physiological characteristics of lettuce seedlings. This framework is based on an unmanned ground vehicle (UGV) platform integrating a RGBD camera and a quad-band multispectral sensor which are rigidly coupled and synchronously triggered. An alignment module based on established feature matching algorithm is introduced to register the misalignment between source multispectral and RGBD images. Subsequently, we design a novel dual-backbone instance segmentation network, MS-SegNet, to enhance segmentation accuracy by hierarchically fusing geometric information with multispectral features. A robust 3D metric pose estimation pipeline, incorporating standard SfM initialization, scale recovery, and generalized ICP refinement, is constructed to generate 3D point clouds with spectral attributes and semantic labels. Finally, key structural and physiological phenotype parameters of each seedling are calculated based on the 3D semantic multispectral point clouds. Experiments demonstrate that MS-SegNet achieves significant advantages in instance segmentation of lettuce seedlings with mAP@50:95 = 0.854. The metric 3D pose estimation pipeline exhibits reliable performance under complex controlled conditions. The quality of the 3D reconstructions is indirectly validated through downstream structural trait extraction. The estimated seedling height and crown width show high correlation with manual measurements, achieving R 2 values of 0.8379 and 0.918, and RMSE values of 10.94 mm and 11.56 mm, respectively. Overall, by systematically integrating these adapted components with the novel segmentation architecture, this framework achieves stable performance improvements in 3D reconstruction, instance segmentation, and phenotypic analysis under greenhouse conditions. It provides a scalable, integrated technical solution for non-destructive, high-throughput phenotyping of crop seedlings in controlled environments.
Why it matches plant phenotyping methodsRGBD・マルチスペクトル・UGVを統合した3Dフェノタイピング基盤を開発し、分割・再構成・構造／生理形質抽出を検証しており、フェノタイピング手法が研究の中心である。
abstractwe establish a multimodal 3D phenotyping framework tailored for controlled agriculture environments, enabling the quantification of structural and physiological characteristics of lettuce seedlings.
LiDAR / point cloudFlowerArchitecture / morphology / geometry
The mechanical dynamics of poricidal stamens play a crucial role in buzz pollination, but they have only been studied in a few species showing comparatively less curvature. This paper presents an integrated experimental-computational approach to characterize the dynamic behaviour of the morphologically more complex stamens of Medinilla magnifica. An experimental set-up employing a custom-built shaker and a laser triangulation sensor is developed to measure the stamen motion with high spatial and temporal resolution. The resulting displacement transmissibility reveals three distinct resonances, with the second one falling within the reported frequency range of pollination buzzes. Finite-element and multi-body models are developed and validated against experimental results. The models further reveal how the stamen morphology gives rise to its complex dynamic behaviour. The developed method enables precise parametric modelling of stamens and provides new insight into the mechanical basis of pollen release, thereby offering a framework for studying buzz pollination dynamics across plant taxa.
Why it matches plant phenotyping methods植物の雄しべ運動をレーザー計測し、有限要素・マルチボディモデルで検証する実験計算手法が研究の中心であるため。
abstractAn experimental set-up employing a custom-built shaker and a laser triangulation sensor is developed to measure the stamen motion with high spatial and temporal resolution.
Salt stress can markedly alter seedling architecture, creating a need for non-destructive three-dimensional (3D) phenotyping methods capable of resolving fine plant structures. However, organ-level segmentation of plant point clouds remains challenging because of leaf overlap, slender stems, ambiguous stem–leaf boundaries, and severe class imbalance. In this study, we developed PTV-SegCo, a task-adapted Point Transformer model for organ segmentation and structural phenotyping of coriander seedlings under salt stress. PTV-SegCo integrates efficient channel attention, gated shallow–deep feature fusion, and a combined cross-entropy–Dice loss to improve representation of fine and minority organ structures. The dataset comprised 60 manually annotated 3D point-cloud samples from 12 cultivation trays repeatedly observed over five acquisition dates under six NaCl concentrations (0, 50, 100, 150, 200, and 250 mmol L −1 ). Because the earliest acquisition represented a particularly challenging developmental stage, these 12 samples were used as a fixed early-stage model-selection set, while samples from the remaining four dates were organized into four date-blocked training–validation configurations. Under this internal model-development protocol, PTV-SegCo achieved mean mAcc and mIoU values of 93.05% and 89.05%, respectively, and showed numerically higher performance than its direct backbone PTV-Seg50. These values should be interpreted as internal comparative results rather than as an unbiased estimate of generalization to unseen cultivation trays, and the present results should not be interpreted as establishing the broad competitiveness of PTV-SegCo against other point-based, convolution-based, graph-based, transformer-based, or plant-specific segmentation architectures. After semantic segmentation, reconstructed scenes were metrically calibrated using the known cultivation-tray dimensions, followed by individual-plant separation and quality control. Four reconstruction-derived structural descriptors—plant height, projected area, voxel occupancy volume, and leaf point ratio—were extracted to characterize temporal structural variation under different NaCl treatments. For treatment-level inference, individual-plant measurements were aggregated within each cultivation tray at each acquisition time, with the cultivation tray treated as the independent experimental unit. Independent manual validation showed close agreement for plant height and projected area, with R 2 values of 0.9969 and 0.986, respectively. Overall, the proposed workflow provides a feasible approach for organ-level segmentation and automated 3D structural analysis of small coriander seedlings under salt stress. The extracted descriptors primarily represent reconstruction-derived spatial characteristics and should not be interpreted as direct indicators of physiological status; voxel occupancy volume and leaf point ratio remain without direct external validation.
Why it matches plant phenotyping methods3D点群による器官セグメンテーションと構造形質抽出の手法開発・内部比較・手動検証が研究の中心であり、塩ストレスは適用対象である。
abstractwe developed PTV-SegCo, a task-adapted Point Transformer model for organ segmentation and structural phenotyping of coriander seedlings under salt stress.
Abstract Tree crowns are complex, three‐dimensional structures whose morphology varies among species, individuals and environments. Although light detection and ranging (LiDAR) provides high‐resolution, single‐tree point clouds that advance species discrimination and the assessment of intraspecific variation in situ, crown shape is still commonly reduced to low‐dimensional metrics (e.g. crown diameter or crown base height), losing much of its three‐dimensional geometric complexity. We introduce a fully 3D geometric morphometric framework that captures crown shape directly from LiDAR point clouds at both species and individual levels. Pre‐segmented LiDAR single‐tree point clouds of eight temperate forest species were converted into three‐dimensional shape representations using radial bounding volumes (RBVs), which partitioned each crown into a standardized set of vertical layers and radial sectors. Surface points automatically digitized from each RBV formed geospatially aligned, 3D pseudolandmark configurations representing geometric morphometric crown shapes. These configurations served as the input data for multivariate analyses of crown shape variation within and between species. Twelve structural traits, including crown and stem dimensions, were extracted from the same RBVs and integrated into analyses of trait–shape associations. The morphospace of crown shape was structured along different axes of variation in broadleaf species than in conifers. Within these groups, species pairs—such as Fagus versus Quercus and Picea versus Pinus —exhibited contrasting intraspecific morphological gradients, with different structural traits driving shape variation in each. Crown base height and total crown height emerged as the strongest predictors of crown shape. Differences in crown shape among species were primarily captured by symmetric components, with asymmetry providing a negligible signal. Interspecific differentiation was largely driven by architectural variation rather than pure size differences. Morphological differences derived from pseudolandmarks and convolutional neural network features exhibited stronger correlations in conifers than in broadleaf species. We present a reproducible, LiDAR‐native framework for quantifying and comparing 3D crown morphology within and across species. Using the RBV approach, geospatially aligned pseudolandmarks can be derived from any pre‐segmented, single‐tree LiDAR point cloud, enabling scalable, multi‐regional analyses of intraspecific variability. This framework provides a robust foundation for integrating crown shape into ecological, evolutionary, silvicultural and modelling studies, including assessments of environmental effects and architectural constraints.
Why it matches plant phenotyping methodsLiDAR点群から樹冠形状を抽出・定量化する3D幾何形態計測フレームワークを開発し、再現可能な植物形態計測手法として検証・適用しているため。
abstractWe introduce a fully 3D geometric morphometric framework that captures crown shape directly from LiDAR point clouds at both species and individual levels.
Abstract Purpose Evaluate how soil and canopy sensing can map within-block variability in tart cherry orchards and identify indicators robust enough for repeatable management decisions. Methods Soil apparent electrical conductivity (ECa) was mapped in spring 2022 across four commercial tart cherry blocks (8.5–10.5 ha; approximately 3,500 trees per block), followed by canopy sensing in 2023–2024. Canopy structure was measured using unmanned aerial vehicle (UAV) photogrammetry and mobile terrestrial laser scanning (MTLS) using light detection and ranging (LiDAR), and canopy density using mobile ceptometry. Spatial layers were aligned to per-tree grid cells. An August 2025 campaign compared UAV- and LiDAR-derived tree height with ground-truthed height. Results Soil-to-canopy relationships were weak to moderate but consistent within blocks ( r = 0.10–0.40), with strength and direction varying by site conditions. Canopy density was more strongly associated with UAV-derived volume than height. UAV-derived 90th-percentile height best predicted ground-truthed height ( R ² = 0.89; RMSE = 0.34 m), whereas LiDAR showed a weaker relationship and greater error ( R ² = 0.70; RMSE = 0.52 m). Cross-sensor agreement was moderate to strong ( r = 0.41–0.65). Per-tree rankings were stable between years for UAV height and volume. Conclusion Whole-block sensing revealed persistent spatial patterns that could support management-zone delineation. UAV photogrammetry provided accurate canopy metrics, MTLS offered measurements suited to routine orchard operations, ceptometry added seasonal canopy-density information, and ECa provided soil context. Occasional ECa mapping combined with strategically timed UAV surveys and other sensors as needed could reduce redundant sensing while supporting fertilizer evaluation, pruning, and labor allocation.
Why it matches plant phenotyping methodsUAVフォトグラメトリ、LiDAR、セプトメトリーによる樹冠の高さ・体積・密度の取得を中心に、地上実測との検証とセンサー間比較を行っているため、植物フェノタイピング手法の実質的な適用・検証に該当する。
abstractCanopy structure was measured using unmanned aerial vehicle (UAV) photogrammetry and mobile terrestrial laser scanning (MTLS) using light detection and ranging (LiDAR), and canopy density using mobile ceptometry.
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:1724-1761Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
Structural failure of cereal stems during late-season climate extremes is a critical determinant of yield stability. In barley, breakage of the stem below the spike, known as head loss, leads to major yield losses, particularly in hot and dry regions where the crop is widely grown. Despite a predicted increase in head loss risk due to global warming, current understanding of the genetic, physiological, anatomical, and environmental factors that control head loss remains limited. Overcoming these knowledge gaps is essential to providing a systems-level strategy for barley breeders to develop climate-ready cultivars that are resilient to stem breakage and suitable for industry adoption. Here, we review present knowledge and highlight opportunities for innovation to mitigate head loss through interdisciplinary approaches that combine precise phenotyping through mechanical testing of stem strength and flexibility, high-throughput phenotyping through drone-based spike counting, and genetic modification strategies informed by studies on hormonal regulation and cell wall composition. Coupled with genotypic data, these efforts will enable the development of a genomic selection platform to facilitate future breeding programs. The framework and tools discussed here are broadly applicable to improving stem resilience in other cereal crops.
Why it matches plant phenotyping methods茎の強度・柔軟性や穂数を対象とする表現型計測手法をレビューし、機械試験とドローンによる高スループット計測を育種基盤として論じているため、表現型手法が中心的です。
abstractHere, we review present knowledge and highlight opportunities for innovation to mitigate head loss through interdisciplinary approaches that combine precise phenotyping through mechanical testing of stem strength and flexibility, high-throughput phenotyping through drone-based spike counting
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:103-119Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Field / plotLiDAR / point cloudStem / branchWhole plant / canopy / plot / fieldClassification2D/3D reconstructionSegmentationArchitecture / morphology / geometry
The separation of a forest plot into individual trees and the automatic extraction of their stem structures from terrestrial laser scanning data are complicated by dense stands, occlusions, and the diversity of biomorphological forms. Existing algorithms usually solve scene partitioning, voxel classification, and tree growing as independent tasks, which leads to error accumulation at subsequent processing stages. This paper proposes a unified model for spatial fragmentation and biomorphological forest segmentation comprising three interrelated stages: scene partitioning by estimated stem coordinates using a Voronoi diagram, probabilistic voxel- or point-level classification, and bottom-up tree growing guided by spatial connectivity and stem membership criteria. For the semantic module, tabular, volumetric, and point-based approaches are compared: gradient boosting with layer-by-layer inference, TabNet, a three-dimensional convolutional neural network, PointNet2, and two-stage pipelines in which gradient boosting builds an initial stem mask for subsequent neural segmentation. The experiment was conducted on 567 mixed-species trees. Considering both quality and computational performance, the {CatBoost; CNN3D} pipeline was selected as the preferred solution, achieving AUC = 0.9966 and IoU = 0.9831. The obtained results show that combining interpretable layer-by-layer classification with subsequent spatial analysis improves the quality of stem structure reconstruction, which is important for automatic forest inventory tasks.
Why it matches plant phenotyping methods地上レーザースキャンから個体樹木と幹構造を自動抽出する統合セグメンテーション手法を開発・比較しており、植物形態の取得が研究の中心である。
abstractThe separation of a forest plot into individual trees and the automatic extraction of their stem structures from terrestrial laser scanning data are complicated by dense stands, occlusions, and the diversity of biomorphological forms.
To establish an accurate and interpretable prediction framework for soybean lodging grade and clarify the core regulatory traits and differentiated driving mechanisms of soybean lodging under high-density drip irrigation cultivation, 356 spring soybean germplasm accessions were used as experimental materials in this study. Morphological and mechanical traits including plant height (PH), stem pulling force (SPF), internode number (IN) and petiole length (PL) were measured over two consecutive years of field phenotyping. Two composite evaluation indices, plant height/stem pulling force ratio (PH/SPF) and plant height/internode number ratio (PH/IN), were further constructed. Four machine learning algorithms were adopted to develop multi-classification models for soybean lodging grade prediction. SHAP analysis combined with three global sensitivity approaches (perturbation analysis, Sobol’ method and Morris screening) was applied to decipher the regulatory patterns of key traits. The results showed that lodging grade significantly affected soybean grain yield and explained 25–28% of the phenotypic yield variation; yield reduction tended to plateau under severe lodging. Compared with single indicators such as SPF and PL, the two derived composite indices could stably distinguish soybean accessions with different lodging grades and exhibited stronger discriminatory power. Model comparison revealed that the XGBoost model achieved optimal prediction accuracy and generalization stability for lodging grade, with a weighted F1-score of 95.34% on the test set, significantly outperforming the conventional linear model. Interpretability analysis demonstrated that the PH/IN, PH, and PH/SPF acted as the primary positive traits promoting lodging, while SPF was the sole protective trait. Driving factors of lodging presented obvious gradient heterogeneity: mild lodging was dominated by the imbalance of plant architecture ratio, whereas severe lodging was governed by the cumulative effects of PH and IN. Strong interactions existed among all measured traits. The interpretable machine learning framework established in this study can provide theoretical support and technical references for lodging-resistant germplasm screening and targeted plant architecture regulation for densely planted soybean under drip irrigation systems.
Why it matches plant phenotyping methods大豆の倒伏状態を形態・力学形質から機械学習で推定し、モデル性能比較と解釈性解析を行う枠組みが研究の中心であり、単なる生物学的実験の routine 測定ではない。
abstractTo establish an accurate and interpretable prediction framework for soybean lodging grade and clarify the core regulatory traits and differentiated driving mechanisms of soybean lodging
Maize ( Zea mays L.) is a highly adaptable crop grown worldwide across diverse climates and management practices, with uses across multiple sectors. Consequently, the traits prioritized in maize research and breeding programs vary depending on the specific objectives. Core traits, however, such as flowering time, plant and ear height, and stalk and root lodging, which are important for evaluating and improving the performance and stability of maize genotypes, are routinely evaluated across breeding programs, regardless of their goals. Standardized measurement of these core traits is essential to ensure data reliability and comparability, enabling the integration of phenotypic data across different experiments. Such efforts ultimately support better decision-making and accelerate the development of improved maize genotypes. This is particularly important in public sector programs, where large-scale evaluations, critical for assessing the value of specific genotypes, are often only feasible through collaboration across programs. Here, we provide a protocol for the standardized collection of phenotypic data, specifically focusing on how to measure core traits in maize field trials. These methods promote consistency and accuracy in the evaluation of these traits, and support communication and coordination among groups in the public sector and other research settings. Further, such standardization facilitates the integration and comparison of data across programs, enabling robust longitudinal and multienvironment analyses.
Why it matches plant phenotyping methodsトウモロコシの主要形質を対象に、圃場試験での表現型データ収集を標準化するプロトコル自体を提示しており、測定方法が研究の中心である。
abstractHere, we provide a protocol for the standardized collection of phenotypic data, specifically focusing on how to measure core traits in maize field trials.
Lodging is a major yield-limiting factor in soybean, but efficient large-scale phenotyping and genetic dissection of this complex trait remain challenging for breeding programs. To bridge this gap, this study developed an integrated, breeding-oriented framework that links UAV-based high-throughput phenotyping with candidate gene identification. Field experiments involving 741 diverse soybean genotypes were conducted over two years, with UAV remote sensing performed at key reproductive stages (from R5 to R7). We identified UAV-derived structural (relative plant height), textural (homogeneity, dissimilarity, correlation), and spectral (NDVI, EVI, NDRE) features as the most sensitive indices for retrieving lodging severity. The fusion of these complementary features, coupled with the XGBoost algorithm, achieved high classification accuracy (0.81–0.92) across genotypes, growth stages, and years. This reliable phenotyping pipeline enabled the precise selection of contrasting genotypes (lodging-resistant vs. lodging-prone) for transcriptomic analysis. Transcriptome sequencing revealed 13,447 differentially expressed genes, with significant enrichment in phenylpropanoid and starch–sucrose metabolic pathways. Moreover, the haplotype analysis within a natural population identified superior allelic variants of two candidate genes ( Glyma.19G249100 and Glyma.05G142200 ) significantly associated with soybean lodging resistance. This work can effectively bridge the gap between scalable field phenotyping and the discovery of functionally validated breeding targets, providing an efficient and translational framework to accelerate the development of lodging-resistant soybean varieties.
Why it matches plant phenotyping methodsUAV画像・リモートセンシング特徴量とXGBoostを統合し、ダイズの倒伏重症度を大規模に推定・検証する育種向け表現型解析パイプラインが研究の中心である。
abstractthis study developed an integrated, breeding-oriented framework that links UAV-based high-throughput phenotyping with candidate gene identification
Canopy 3D architecture is a critical determinant of light interception, photosynthetic efficiency, and final yield in cotton, yet its rapid and accurate characterisation remains challenging in field conditions. To achieve efficient, non-destructive, and quantitative monitoring of the canopy structure of field-grown cotton, this study proposes a 3D structure-based technology stack for high-efficiency, low-cost, and high-precision phenotyping extraction. This stack directly addresses the technical bottlenecks of traditional 3D data acquisition, namely high cost, long processing time, and low operational efficiency, which have hindered large-scale application. We developed a pipeline that integrates a fast reconstruction algorithm with a scale-recovery mechanism using ground control points (GCPs), enabling the generation of true-scale 3D point clouds from UAV aerial images in a cost- and time-effective manner. Using only 141 UAV images and with a reconstruction time of approximately 20 min, we efficiently reconstructed high-quality, scale-accurate point clouds of two 5.5 m × 5.5 m cotton plots, significantly outperforming SfM-MVS and Instant-NGP in terms of both reconstruction efficiency and point cloud completeness. This method, whose current validation is confined to a single season, one growth stage, and two experimental plots, not only achieves a breakthrough by using fewer input images with high efficiency, but also ensures point cloud accuracy and completeness, showing strong potential for rapid field monitoring and real-time management. Based on the high-quality reconstructed point clouds, we further quantitatively evaluated canopy characteristics at harvest, analyzing the coefficient of variation of canopy height, porosity distribution, and canopy volume fraction. The core shortcomings and optimization strategies for the existing canopy structure were identified, providing scientific data support and practical technical references for precision cultivation management and mechanization-compatible planting in cotton.
Why it matches plant phenotyping methodsUAV画像からの3D点群再構成とスケール復元パイプラインを開発・比較検証し、綿花キャノピー形質を定量化することが研究の中心であるため含める。
abstractthis study proposes a 3D structure-based technology stack for high-efficiency, low-cost, and high-precision phenotyping extraction.
Accurate estimation of the cotton seedling Leaf Area Index (LAI) is essential for yield prediction and precision crop management. Traditional manual methods are limited by low throughput, while two-dimensional remote sensing techniques often struggle with sparse canopy cover and soil background interference. Although three-dimensional LiDAR presents a promising alternative, existing deep learning approaches typically depend on costly fully supervised point-wise annotations. To address this challenge, this study proposes an end-to-end framework that integrates UAV-based LiDAR, weakly supervised segmentation, and physical parameter inversion. A weakly supervised network, termed CogNet, was developed—incorporating self-distillation and structure-aware label propagation—to achieve precise segmentation of cotton plants using only 10% sparse annotations. Following instance segmentation via Density-Based Spatial Clustering of Applications with Noise (DBSCAN), individual plant phenotypic traits were extracted. A nonlinear Extreme Gradient Boosting (XGBoost) model was then constructed to invert LAI by leveraging allometric relationships between 3D structural parameters and leaf area. Experimental results showed that CogNet achieved an Intersection over Union (IoU) of 85.34%, effectively mitigating overfitting to label noise and achieving performance competitive with the fully supervised RandLA-Net (82.13%). Notably, under the specific conditions of this cotton seedling dataset characterized by strong geometric priors, the weakly supervised model demonstrated enhanced robustness against annotation inconsistencies. The framework attained a plant detection rate of 96.2%, and the XGBoost model delivered high estimation accuracy (R² = 0.879, RMSE = 0.138). This study demonstrates that weakly supervised learning can substantially reduce annotation costs while maintaining model performance, providing an efficient and cost-effective solution for field-scale crop phenotyping.
Why it matches plant phenotyping methodsUAV-LiDAR、弱教師ありセグメンテーション、個体形質抽出、LAI推定を統合した作物フェノタイピング手法の開発・評価が研究の中心である。
abstractthis study proposes an end-to-end framework that integrates UAV-based LiDAR, weakly supervised segmentation, and physical parameter inversion.
Live imaging of plant subcellular structures is key to deciphering the spatiotemporal bases of cellular processes, and their functional impact on growth and morphogenesis at various biological scales. Live imaging of plant cells essentially relies on expression of fluorescent markers labeling cells or subcellular structures of interest. Simultaneous multi-channel imaging of several markers is still not routine practice in plant cell biology, owing to issues linked to genetic or spectral compatibility of markers, differences in expression levels, silencing, toxicity, etc. Here we designed a three-color marker in Arabidopsis thaliana and Capsella rubella , enabling high-resolution live imaging of plant morphogenesis, including labeling of the cell membrane, the nucleus and the microtubule cytoskeleton. Detection of MT arrays involved the development of a MAP4-MBD-based microtubule marker optimized for plant cells. The three- color marker allows visualization of the three-dimensional organization and dynamics of plant microtubules within the intracellular space with unprecedented precision, in various organs including the root and shoot meristems, the leaf, anther, and gynoecium. Our results demonstrate the potential of such single-construct strategy for cell biology studies in plants.
Why it matches plant phenotyping methods植物細胞の形態形成を可視化する三色ライブイメージング法と、植物細胞用に最適化した微小管マーカーの開発が研究の中心である。
abstractDetection of MT arrays involved the development of a MAP4-MBD-based microtubule marker optimized for plant cells.
Functional-structural plant models simulate plant responses to environmental conditions, but their development and evaluation are often limited by the lack of datasets combining detailed architectural and physiological measurements. Here, we present a comprehensive dataset acquired from four oil palm plants ( Elaeis guinnensis) grown under controlled and contrasting climate scenarios. The dataset includes (i) three-dimensional reconstructions of plant architecture derived from terrestrial lidar point clouds, (ii) leaf-level gas exchange measurements used to parameterize photosynthesis and stomatal conductance models, and (iii) continuous plant-scale measurements of CO 2 and H 2 O fluxes obtained in a microcosm under precisely monitored and manipulated environmental conditions (light, temperature, humidity, and CO 2 concentration) across height climate scenarios. By combining detailed structural data with physiological measurements at both leaf and whole-plant scales, this database has been designed to build and evaluate digital twins (or shadows) of plants functioning under controlled conditions. It provides a valuable resource for calibrating biophysical models (light interception and photosynthesis), benchmarking model predictions across scales, and investigating the consistency between leaf-level parameterization and plant-level fluxes. All data and processing workflows are openly available, facilitating reuse for model development, evaluation, and intercomparison in plant and crop modelling communities.
Why it matches plant phenotyping methods3D LiDARによる植物構造計測と生理計測を統合したデータセットで、モデルの較正・ベンチマーク・評価を主目的としており、植物フェノタイピング手法と再利用可能なワークフローが中心である。
abstractthree-dimensional reconstructions of plant architecture derived from terrestrial lidar point clouds
Soil salinity is a major constraint on rice ( Oryza sativa L.) production, particularly during the yield-determining reproductive stage. Utilizing a high-throughput RGB platform, we non-destructively phenotyped a diverse panel of 294 rice accessions over two consecutive years. By extracting 60 dynamic image-based traits (i-traits) reflecting canopy architecture and stay-green capacity, and four seed-setting rate-related traits, our genome-wide association study (GWAS) identified 95 significant loci, 35.8% of which precisely co-localized with previously reported QTLs. We further prioritized OsSLT1 ( LOC_Os01g05790 ) as a candidate gene at a reproducible suggestive locus associated with leaf-rolling-related image traits. Transgenic evaluations confirmed that it acts as a positive regulator of salt tolerance at the seedling stage. OsSLT1 was mainly detected in the nucleus, and no significant changes in Na + or K + accumulation were observed in flag leaves under the tested salt-stress condition, suggesting that OsSLT1 may regulate salt tolerance through mechanisms beyond classical shoot ion accumulation. Natural variations in the OsSLT1 promoter were associated with transcriptional divergence. The Hap2 promoter haplotype showed significantly higher stress-induced transcriptional activity. Hap2 was rare in modern indica accessions, suggesting that it may represent a potentially useful genetic resource for future salt-tolerance improvement. Supplementary information The online version contains supplementary material available at 10.1007/s11032-026-01704-2.
Why it matches plant phenotyping methodsRGB高スループット基盤による非破壊画像計測と、60種の動的画像形質の抽出が研究の主要なデータ取得・解析手法として明示されているため、GWAS中心の応用研究でも植物フェノタイピング手法の実質的応用に該当する。
abstractUtilizing a high-throughput RGB platform, we non-destructively phenotyped a diverse panel of 294 rice accessions over two consecutive years.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Abstract Probe-based spatial transcriptomics platforms use predefined oligonucleotide panels to detect selected RNAs in tissue sections while preserving transcript spatial coordinates. Accurate cell segmentation is required for reliable transcript-to-cell assignments. This analytical process is affected in plant tissues by cell walls, large vacuoles, and strong autofluorescence, which often reduce boundary contrast and elevate background. Nucleus-only segmentation with fixed-distance expansion can be an alternative approach, but it underestimates cellular area and morphology and reduces the number of assignable transcripts per cell. Here, we present a practical workflow for segmentation and downstream processing in plant probe-based spatial transcriptomics. Using the soybean nodule, soybean seed, rice root, and wheat inflorescence, we demonstrate the applicability of our workflow across species, tissues, and technological platforms. In brief, candidate cell masks are generated from available fluorescence signals and then selected and corrected using two napari plugins. Transcript-informed refinement with Baysor is included as an optional step. Upon benchmarking our approach using a collection of metrics (assignment yield, background/negative controls, and per-cell transcript/gene distributions) and linked segmentation choices to expression-matrix quality and downstream clustering, we demonstrate the potential of our workflow to support the analysis of plant probe-based spatial transcriptomics.
Why it matches plant phenotyping methods植物組織の細胞セグメンテーションとトランスクリプト割当てを改善する実用ワークフローを開発し、複数種・組織でベンチマークしている。植物形態そのものの測定ではないが、細胞レベルの空間状態を抽出する解析手法が中心である。
abstractHere, we present a practical workflow for segmentation and downstream processing in plant probe-based spatial transcriptomics.
Abstract Cross-sectional analysis is considered the reference method for measuring cotton fiber fineness and maturity; however, its widespread use has been limited by labor-intensive sample preparation and manual image analysis. The objective of this study was to develop and validate a reproducible deep-learning-based workflow for automated segmentation and quantitative analysis of cotton fiber cross-sections. A total of 249 composite light microscopy images of cotton fiber cross-sections were collected and manually annotated to generate training and validation datasets. A YOLO11m instance segmentation model was developed to identify cotton fiber and lumen regions and automatically extract quantitative traits, including fiber area, lumen area, fiber perimeter, and lumen perimeter. The workflow integrates automated image segmentation, post-processing, quantitative trait extraction, and data export to facilitate reproducible cotton fiber phenotyping. Model performance was evaluated using mean Average Precision (mAP), and workflow outputs were validated against Adobe Photoshop using descriptive comparisons of six cross-sectional traits. The model achieved Box mAP50 scores of 0.984 for cotton fiber regions and 0.789 for lumen regions, demonstrating high segmentation accuracy. The automated workflow substantially reduced manual analysis time while producing measurements with central tendencies comparable to those obtained using Adobe Photoshop. To facilitate reproducibility and adoption, the workflow, trained model weights, and supporting documentation are publicly available through GitHub and a Hugging Face web application. The workflow substantially increases analytical throughput while providing a reproducible and publicly accessible method for automated cotton fiber cross-sectional phenotyping, facilitating quantitative analysis for cotton genetics and breeding research.
Why it matches plant phenotyping methods綿繊維横断面の画像分割、形質抽出、検証を目的とした再現可能な深層学習ワークフローの開発であり、植物フェノタイピング手法が研究の中心である。
abstractThe objective of this study was to develop and validate a reproducible deep-learning-based workflow for automated segmentation and quantitative analysis of cotton fiber cross-sections.
Reproduction assets foundThe paper's cotton fiber cross-section phenotyping workflow (YOLO11m segmentation pipeline, trained model weights, example images/outputs) is explicitly stated as publicly available via a GitHub repository and a Hugging Face web application, with URLs matching the allowed list.Code · publicThe complete source code, training scripts, dataset configuration, example input images, example outputs,
and supporting documentation are publicly available through the GitHub repository:
https://github.com/RifeLab/cotton-lumen-microOpen asset ↗RifeLab/cotton-lumen-micropdf-page:11 lines:1-47Code · publicThe cotton fiber image analysis workflow is publicly available through a web-based application hosted on
Hugging Face at: https://huggingface.co/spaces/chaneylc/cotton_fiber_microscopy_measureOpen asset ↗pdf-page:11 lines:1-47Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
This study presents a multi-scale framework for reconstructing snow avalanche (SA) frequency and assessing vegetation structural responses in data-scarce mountain environments. The approach integrates dendrogeomorphological reconstructions, satellite-based spectral disturbance detection, and UAV-based Structure-from-Motion (SfM) photogrammetry, complemented by field data, and was applied to two avalanche paths in the Piatra Craiului Mountains (Southern Carpathians, Romania). Tree ring analyses allowed reconstruction of spatially explicit minimum avalanche chronologies for the 1980–2025 period. These reconstructions were combined with a DEM-based upslope algorithm to derive spatially variable avalanche return periods, revealing the highest frequencies in release and upper-track sectors and progressively longer return periods toward lower-track zones. Sentinel-2 imagery was used to assess the surface footprint of a reconstructed avalanche event in 2018. Among the tested spectral indices, the Moisture Stress Index (MSI) showed the most spatially coherent response, while the combined MSI-NDMI-NBR approach reduced index-specific noise. UAV-SfM photogrammetry supports high-resolution mapping of vegetation structure and surface states. Vegetation was classified using a machine-learning-based object-oriented approach (Random Forest) integrating spectral, geometric, structural, and textural parameters. The multi-parameter feature set yielded very high classification accuracy (Cohen’s Kappa ≈ 0.95). Across avalanche return-period gradients, both UAV-derived and field-based metrics showed a systematic associations between tree height and avalanche frequency, whereas tree age and stem diameter exhibited more variable, path-dependent responses. The proposed framework provides a transferable basis for linking avalanche disturbance regimes with vegetation structure and surface stability in mountain landscapes lacking long-term observational records.
Why it matches plant phenotyping methodsUAV-SfMと機械学習による植生構造・樹高の高解像度推定が研究枠組みの主要部分であり、分類精度も評価しているため、植物状態の画像ベース表現型計測として含める。
abstractUAV-SfM photogrammetry supports high-resolution mapping of vegetation structure and surface states.
Abstract In peanut ( Arachis hypogaea L.), plant stand establishment, seedling vigor, and canopy growth are key determinants of crop performance; yet traditional ground‐based assessment methods can be destructive, labor‐intensive, and limited in throughput. This study evaluated the potential of vegetation metrics derived from unmanned aerial vehicle (UAV)‐based red‐green‐blue (RGB) and multispectral (MS) imagery for high‐throughput, nondestructive assessment of plant stand establishment, seedling vigor, and light interception in peanut. Six runner‐type peanut cultivars were evaluated in 2024 and seven in 2025, with each cultivar represented by two seed size classes (small and large), to generate variation in these traits. Within‐row vegetation discontinuity‐based plant stand ratings for estimating plant stand count ( R 2 = 0.81–0.90), together with canopy coverage for assessing seedling biomass ( R 2 = 0.77–0.82) and light interception ( R 2 = 0.96–0.98), were the best‐performing vegetation metrics. These vegetation metrics provided similar or greater cultivar separation compared with ground‐based measurements. In contrast, several vegetation indices exhibited strong correlations with ground‐based measurements but provided inconsistent cultivar rankings and statistical groupings. MS imagery outperformed RGB imagery for plant stand and seedling biomass assessment. Overall, these results demonstrate that UAV‐derived canopy metrics provide reliable, high‐throughput tools for early‐ to mid‐season crop assessment and offer scalable alternatives to traditional ground‐based approaches for agronomic, crop physiological, and plant breeding research.
Why it matches plant phenotyping methodsUAV画像から植物体の出芽・苗勢・バイオマス・光 interception を推定する植 phenotyping 手法を開発・評価しており、取得指標の性能検証が研究の中心です。
abstractThis study evaluated the potential of vegetation metrics derived from unmanned aerial vehicle (UAV)‐based red‐green‐blue (RGB) and multispectral (MS) imagery for high‐throughput, nondestructive assessment of plant stand establishment, seedling vigor, and light interception in peanut.
Accurate tree volume estimation is central to forest management and carbon accounting. Allometric equations are widely used but limited in transferability across species, regions, and environmental conditions. Mobile Laser Scanning (MLS) offers a promising alternative through direct measurement of tree geometry; however, the influence of tree shape on MLS accuracy remains poorly understood. This study evaluated MLS-derived estimates of stem diameters, total tree height, and merchantable stem volume against destructive reference measurements from 176 trees spanning eight species (four hardwood, four softwood) in Wallonia, Belgium. A Zeb Horizon RT scanner was used; tree architectural descriptors extracted from the point cloud were tested for associations with measurement error. Across 7,824 stem diameter measurements, MLS achieved a mean error of 0.46 cm, with precision declining above 15 m. MLS-derived height outperformed Vertex IV clinometer measurements for hardwood species (RMSE% = 6.88 vs. 8.78) but performed slightly less well for softwoods (RMSE% = 7.36 vs. 6.14). QSM-based volume estimates systematically underestimated reference values, while taper-based reconstruction produced nearly unbiased estimates with an RMSE of 15.72%. Correlation analyses and PCA showed that tree architectural variables explained only a small fraction of MLS error variability. Diameter and height errors were largely independent of structural attributes, while volume errors showed moderate associations with tree size and crown density. These findings indicate that tree architecture is not a primary source of MLS measurement uncertainty. Future MLS-based forest inventory efforts should prioritize acquisition and processing optimization, as scanning conditions and forest structure appear more influential than tree shape.
Why it matches plant phenotyping methodsMLSによる樹木形状・直径・樹高・幹材積の非破壊推定を、破壊測定と比較検証し、測定誤差の要因を評価した方法検証研究である。
titleNon-destructive tree volume estimation using mobile laser scanning: Impact of the tree shape on measurement error.
Automated plant identification based on leaf morphology has gained significant attention in recent years due to its wide range of applications in precision agriculture, biodiversity conservation, environmental monitoring, and botanical informatics. Advances in digital image processing and machine learning have enabled the development of intelligent systems capable of identifying plant species from leaf characteristics with minimal human intervention. Despite these advancements, achieving reliable and accurate classification remains challenging because leaf images are often affected by variations in illumination, complex backgrounds, image noise, differences in orientation and scale, as well as natural leaf deformation. These factors can obscure important morphological features, reduce the effectiveness of feature extraction, and ultimately decrease the accuracy and robustness of automated plant classification systems. Consequently, there is a growing need for intelligent frameworks that can effectively handle these challenges while preserving critical leaf morphology and venation information for reliable plant identification. This study proposes an Intelligent Morphology-Driven Framework that integrates advanced digital image processing and machine learning for robust leaf venation analysis and plant classification. The proposed framework integrates multiple digital image processing and machine learning techniques to enable accurate and automated leaf venation analysis and plant classification. Initially, leaf images undergo preprocessing using grayscale conversion, histogram equalization, Contrast Limited Adaptive Histogram Equalization (CLAHE), Gaussian filtering, Laplacian sharpening, Gabor filtering, and homomorphic filtering to improve image quality and enhance venation and structural details. The enhanced images are then processed through threshold-based segmentation followed by morphological operations, including erosion, dilation, opening, closing, convex hull generation, and skeletonization, to accurately isolate leaf regions while preserving their geometric structure.To characterize leaf morphology, the framework extracts a comprehensive set of features, including geometric descriptors such as area, perimeter, circularity, aspect ratio, solidity, eccentricity, and vein density, together with Hu invariant moments that provide rotation-, translation-, and scale-invariant shape representation. In addition, the framework investigates the influence of image compression by comparing lossless PNG and lossy JPEG formats to evaluate their impact on preserving morphological features and venation details. The extracted feature vectors are subsequently classified using a Random Forest classifier to categorize leaf venation patterns into parallel, reticulate-pinnate, and reticulate-palmate classes.Experimental evaluation demonstrates that the proposed framework achieves an overall classification accuracy of 93.2%, while effectively preserving important morphological characteristics and maintaining computational efficiency. The combination of adaptive image enhancement, morphology-preserving segmentation, comprehensive feature extraction, and robust machine learning classification makes the proposed approach reliable, interpretable, and scalable. Consequently, the framework has significant potential for applications in digital herbarium systems, automated plant identification, biodiversity monitoring, botanical informatics, and precision agriculture.
Why it matches plant phenotyping methods葉画像から形態・葉脈形質を抽出し分類する画像処理・機械学習フレームワーク自体が研究の中心であり、植物表現型の取得・解析手法として適格。
abstractThis study proposes an Intelligent Morphology-Driven Framework that integrates advanced digital image processing and machine learning for robust leaf venation analysis and plant classification.
Conventional methods of plant distance and volume measurements are limited by low efficiency, limited spatial coverage, and high measurement error. LiDAR and RGB-D imaging offer cost-effective, precise, and non-destructive techniques for plant distance and volume measurements. This study aimed to measure cabbage height, volume, and distance using LiDAR and RGB-D imaging. The sensors were mounted on a 1.6 kW electric field scouting platform (EFSP) for data collection. Point cloud (PCD) data were collected using LiDAR, whereas data processing, visualization, and measurements were done using commercial software and open-source programming scripts. A total of 20 cabbage plants were analyzed. LiDAR data processing included data frame screening, outlier removal, denoising, voxelization, and generation of 3D PCD density maps. Depth image processing included importing raw data and metadata shaping using intrinsic camera parameters, visualization, extraction of depth points, and pixel-level measurements of distances and volume. RGB image processing involved image conversion, segmentation, normalization, binary masking, mask cleaning, region extraction of cabbages, separation of ROI and preparation of contours, Delaunay triangulation and convex hull preparation, ROI overlay, bounding box preparation, sharing boundary between two boxes, conversion to pixel distances, and for visualization, plant height, volume measurements, and center to center distance measurement for measuring the plant distance. LiDAR demonstrated higher measurement accuracy for cabbage plant height, circumferential volume (geometric canopy volume), and plant distance, followed by RGB-D imaging, while RGB imagery showed comparatively lower performance under the study field conditions. Overall, LiDAR and RGB-D imaging provided reliable and non-destructive approaches for cabbage geometric characterization under field conditions, although accurately capturing complex plant geometry remains challenging. Positive and negative values of bias represent the over- and under-estimated results, respectively. Future studies should include larger and more diverse plant datasets exhibiting diversified size, shape, and geometric structure to further improve the robustness and general applicability of the proposed sensing approaches.
Why it matches plant phenotyping methodsLiDAR、RGB、RGB-Dを用いてキャベツの高さ・体積・株間距離を取得し、処理手順と測定精度を比較評価する手法研究であり、植物表現型取得が中心である。
abstractThis study aimed to measure cabbage height, volume, and distance using LiDAR and RGB-D imaging.
Field / plotMultimodalNeRF / 3D Gaussian SplattingLiDAR / point cloudRGB / grayscaleStereoFruitWhole plant / canopy / plot / fieldMorphology / geometry measurementObject detection
Binocular stereo vision is a low-cost and scalable 3D perception technology that shows strong potential in agricultural phenotyping and smart agriculture. By estimating depth from multi-view RGB images, it enables non-contact, high-precision sensing of crop structure, canopy morphology, growth dynamics, and livestock traits, providing essential support for digital and intelligent agricultural production. With recent advances in deep learning-based stereo matching, multimodal sensor fusion, and 3D reconstruction, its robustness and accuracy in complex field environments have been significantly improved. This paper systematically reviews recent progress in agricultural applications of binocular stereo vision, covering system architectures, traditional and deep learning-based stereo matching methods, point cloud reconstruction techniques, and emerging supervision strategies such as 3D Gaussian splatting. It further summarizes key applications, including high-throughput phenotyping, fruit localization and robotic harvesting, weed detection and precision spraying, autonomous navigation, and livestock body condition assessment, highlighting its role in multi-task agricultural perception systems. Finally, the paper discusses major challenges, including low-texture matching difficulty, occlusions in complex environments, cross-domain generalization, real-time lightweight deployment, and limited dataset availability. Future directions are outlined in foundation model-based visual perception, self- and weakly supervised learning, multimodal fusion, and edge-efficient model design, aiming to support large-scale deployment in smart agriculture.
Why it matches plant phenotyping methods農業における双眼ステレオビジョンのシステム、ステレオマッチング、3D再構成を体系的にレビューし、作物構造・群落形態・生育動態の非接触計測とハイスループット表現型解析を主要対象としているため。
abstractThis paper systematically reviews recent progress in agricultural applications of binocular stereo vision, covering system architectures, traditional and deep learning-based stereo matching methods, point cloud reconstruction techniques
LiDAR (Light Detection and Ranging) and RGB-D camera imaging have emerged as essential tools in agricultural applications, particularly for plant size and distance measurements, enabling non-destructive, cost-effective, and precise estimation. The objective of this study was to measure the plant canopy dimensions and distance between apples using commercial LiDAR, and an RGB-D camera with a speed sprayer platform was used to determine whether LiDAR provides a higher measurement accuracy under field conditions. Data were collected in an apple orchard in Muju, Republic of Korea. Commercial 3D LiDAR, a terminal box, an RGB-D camera, a microcontroller, a power supply, and individual display monitors were integrated into a customized data acquisition (DAQ) box for LiDAR point cloud (PCD), RGB, and depth imagery data collection. Commercial software was used for data acquisition, data conversion (pcap to PCD), segmentation of regions of interest (ROI), and pre-processing of data. PCD processing and measurement consisted of data frame selection, data conversion, outlier removal, downsampling, denoising, ground point removal by filtering, voxelization, and density map generation using an open access programming language script. Depth image processing included importing raw data, shaping metadata using intrinsic camera parameters, visualizing depth images, extracting depth points, and measuring the plant canopy at the pixel level. RGB image analysis involved grayscale conversion, thresholding, segmentation of ROI, contour preparation, noise removal, and binary masking for eliminating the background. Estimated results were compared to measured results. LiDAR measurements showed the closest agreement with the measured results for plant height, canopy volume, plant spacing, and row distance, outperforming both RGB and depth imaging. Under field conditions, plant spacing and row distance were estimated with accuracies of 97.5% and 94.7%, respectively, exhibiting higher measurement accuracies than RGB and depth imagery data results. Despite some discrepancies due to complex plant geometry and dynamic data collection, the results support data collection strategies critical for precision horticulture.
Why it matches plant phenotyping methodsLiDARとRGB-D画像を用いたリンゴ樹の樹冠寸法・体積・樹間距離・列間距離の取得と精度比較が研究の中心であり、植物表現型計測手法の開発・検証に該当する。
abstractThe objective of this study was to measure the plant canopy dimensions and distance between apples using commercial LiDAR, and an RGB-D camera with a speed sprayer platform was used to determine whether LiDAR provides a higher measurement accuracy under field conditions.
Monitoring continuous agricultural canopies is fundamentally limited by the geometric constraints and computational bottlenecks of traditional 3D reconstruction. This study presents a 3D volumetric phenotyping pipeline integrating semantic mask generation with 3D Gaussian Splatting (3DGS) to quantify greenhouse cucumber canopy architecture. To drive component-specific optimization, we evaluated custom-trained convolutional networks (YOLO11) against a zero-shot foundation model (SAM3), determining that SAM3 provided the necessary boundary precision for accurate spatial isolation. The optimized 3DGS model outperformed implicit NeRF baselines, preserving fine-scale morphological details at real-time rendering speeds ( ≈ 48 FPS). To enable actionable measurement, a uniform voxelization protocol was applied to the point cloud, successfully neutralizing algorithmic densification bias. This technical framework yielded highly accurate physical geometry, achieving a Root Mean Square Error (RMSE) of ≤ 0.59 cm against in situ leaf measurements. Transitioning to agronomic interpretation, the pipeline was deployed to quantify complex canopy architecture. It mathematically mapped structural congestion zones and provided a temporal validation of a standard pruning intervention, explicitly capturing the geometric increase in lower-canopy porosity and the upward translation of biomass. This framework provides a robust, scale-accurate tool for monitoring plant architecture and guiding dynamic canopy management.
Why it matches plant phenotyping methods植物キャノピーの3D形状を取得・定量化する画像ベースの表現型解析パイプラインを開発し、実測葉寸法で精度検証しているため、方法が研究の中心である。
abstractThis study presents a 3D volumetric phenotyping pipeline integrating semantic mask generation with 3D Gaussian Splatting (3DGS) to quantify greenhouse cucumber canopy architecture.
Understanding the spatial structure of tree communities is fundamental for evaluating ecological interactions and management dynamics in agroforestry systems. However, the structural complexity and small spatial scale of traditional agroecosystems often limit the use of conventional remote sensing approaches. Recent advances in drone-based photogrammetry offer new opportunities to reconstruct the three-dimensional structure of vegetation at high spatial resolution and to quantify tree-level structural attributes. In this study, we applied aerial photogrammetry from unmanned aerial vehicles (UAVs) to characterize the spatial structure of agroforestry systems in traditional home gardens (THGs) in the Yucatan Peninsula, Mexico. The immediate neighborhood structure of the tree community of 20 THGs distributed along a south–north precipitation gradient was analyzed using two focal species as anchor references: Spondias purpurea and Annona muricata. High-resolution orthomosaics and three-dimensional point cloud models were generated to estimate structural attributes, including tree height, crown area, crown surface area, and canopy volume, which were combined with field measurements of diameter at breast height. Spatial indices describing aggregation, dominance, and neighborhood diversity were calculated to evaluate tree spatial organization and potential interaction patterns. The UAV-derived structural metrics revealed significant differences in canopy architecture across regions and between focal species. Regardless of the focal species, trees in the southern region exhibited greater height, crown diameter, and canopy volume than those in the northern region. Moreover, the spatial arrangement of tree communities also differed depending on which focal species was considered as the anchor, suggesting contrasting strategies of canopy dominance and spatial coexistence. Finally, our results validate the use of drone-based photogrammetry as an effective approach for capturing fine-scale spatial structure in complex agroforestry systems. By enabling detailed three-dimensional reconstruction of tree canopies, UAV remote sensing offers an affordable, simple approach to investigate neighborhood interactions, management effects, and structural dynamics in traditional agroecosystems that are difficult to assess using conventional field- or satellite-based methods.
Why it matches plant phenotyping methodsUAV空撮フォトグラメトリと3D点群から樹高・樹冠面積・樹冠表面積・林冠体積を推定し、その有効性も検証しており、植物形質取得法が研究の中心である。
abstractHigh-resolution orthomosaics and three-dimensional point cloud models were generated to estimate structural attributes, including tree height, crown area, crown surface area, and canopy volume
Introduction This study evaluates the applicability of digital morphometric analysis to images of archaeological carbonized olive endocarps as a proof-of-concept initial approach for archaeobotanical investigations. Conventional morphometric analyses of olive endocarps largely rely on manual measurements, limiting reproducibility and quantitative comparison. Methods Ten archaeological endocarps were selected from previously published archaeological assemblages based on the integrity of their outlines, apex-base morphology and overall preservation quality. Quantitative descriptors describing endocarp size, symmetry, curvature and contour geometry were extracted using the OliveID software and compared with a modern morphometric reference database comprising Greek and international olive cultivars. Results Reliable contour extraction and quantitative descriptor computation were successfully achieved for all archaeological specimens despite carbonization. Preliminary comparison of representative morphometric descriptors showed that the archaeological specimens were positioned within the morphometric variation observed among the modern reference collection. Hierarchical clustering consistently associated the archaeological endocarps with the modern Throumbolia morphotype, while distinguishing them from elongated, globular and mucro-bearing cultivars. Discussion These findings demonstrate the feasibility of applying digital image-based morphometric analysis to sufficiently preserved archaeological carbonized olive endocarps and indicate a similar morphometric affinity between the analyzed archaeological material and the modern Throumbolia cultivar. This proof-of-concept study highlights the potential of digital morphometric approaches for quantitative archaeobotanical investigations of archaeological olive remains, while emphasizing the need for larger archaeological datasets and standardized image acquisition to further validate the observed morphometric similarity.
Why it matches plant phenotyping methodsOliveIDを用いて炭化オリーブ内果皮の輪郭からサイズ、対称性、曲率、形状記述子を抽出し、デジタル画像形態計測の適用可能性と再現性を評価している。植物器官の形質抽出法が研究の中心である。
abstractThis study evaluates the applicability of digital morphometric analysis to images of archaeological carbonized olive endocarps as a proof-of-concept initial approach for archaeobotanical investigations.
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 quantitative measurements of the archaeological specimens are presented in Supplementary Table 2 , whereas the corresponding mean values and standard errors for the modern cultivars are provided in Supplementary Table 3 .Open asset ↗lines:311-320Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Phenotyping high-biomass perennial crops is laborious and the rate of genetic gain in conventional perennial crop breeding programs is typically low. So, it is especially important to identify methods that produce efficiency gains in the breeding process. Miscanthus is a C4 perennial grass with favorable characteristics for producing biomass as a feedstock for biofuels and diverse bio-based products. Increasing biomass yield will increase profitability and environmental benefits, so it is a key target for Miscanthus breeding. In addition, the identification of well-adapted genotypes across a wide range of environmental conditions requires the establishment of multi-environment trials (METs). Sparse testing is a genomic prediction-based strategy that reduces the phenotyping costs in METs by selecting a subset of genotypes to evaluate in a subset of environments and then predicts the performance of the unobserved genotype-environment combinations. A Miscanthus sacchariflorus (MSA) population comprising 336 genotypes observed across three environments was analyzed implementing sparse testing designs. Three prediction models considering main effects (environments, genotypes, genomic) and interaction effects (genotype-by-environment; G×E interaction) were implemented for forecasting dry biomass yield (YDY), total culm (TCM), average internode length (AIL), and culm node number (CNN). Multiple calibration sets based on different compositions and sizes were considered to evaluate performance in terms of the predictive ability (PA) and the mean square error (MSE) for a fixed testing set size. The training set size ranged from 52 to 112 to predict a fixed set of 224 unobserved genotypes across all three environments. The results showed that the model accounting for G×E interaction consistently presented the highest PA and the lowest MSE: for CNN (PA: ~0.77, MSE: ~0.5) and YDY (PA: ~0.70, MSE: ~1.3) while for TCM and AIL these ranged from ~0.28 to 0.41 and ~1.3 to 4.3, respectively. Overall, varying training sets and allocation strategies did not affect PA and MSE, with 52 non-overlapping and 0 overlapping genotypes per environment as the optimal cost-effective allocation framework. This suggests that implementing sparse testing designs could significantly reduce phenotyping costs by fivefold, without compromising PA in breeding programs for perennial crops such as Miscanthus .
Why it matches plant phenotyping methodsスパーステスト設計とゲノム予測を用いて、複数環境での植物形質予測と表現型測定コスト削減を評価しており、表現型取得・予測手法が研究の中心である。
abstractSparse testing is a genomic prediction-based strategy that reduces the phenotyping costs in METs by selecting a subset of genotypes to evaluate in a subset of environments and then predicts the performance of the unobserved genotype-environment combinations.
Reproduction assets foundThe paper's data availability statement points to a public figshare deposit (DOI 10.6084/m9.figshare.31796794) containing the datasets analyzed in this Miscanthus sparse-testing genomic prediction study, including the phenotypic and genotypic data used for the models.Dataset · publicThe datasets analyzed for this study can be found in the figshare repository at https://doi.org/10.6084/m9.figshare.31796794 .Open asset ↗figshare · 10.6084/m9.figshare.31796794lines:603-621Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · checked 15 Sept 2026
Annotation scarcity, poor model generalization and lagged data processing remain key bottlenecks hindering the practical deployment of phenotyping robots. To address these issues, we developed a novel phenotyping robot capable of online 3D reconstruction and zero-shot segmentation directly on the edge. Diverging from conventional semantic SLAM, our core contribution is RT-ZSDR, a framework featuring two key methodological novelties. First, we introduce the ForeCut pipeline for target extraction, which innovatively fuses DINO features with 3D geometric spatial information, leveraging multi-view semantic-spatial consistency to achieve annotation-free, zero-shot dense segmentation and reconstruction. Second, we designed a hardware-coupled loop closure strategy utilizing the robotic arm's kinematic feedback as prior constraints to significantly improve loop closure recall. Supported by edge computing Jetson Orin NX, the tracking and segmentation process takes approximately 0.24 s per frame after an initialization period of 1.82 s. RT-ZSDR's phenotypic measurements demonstrated strong correlations with reference baseline in both laboratory settings (n=90, PlantEye measurements as reference baseline; R 2 =0.990, 0.939, 0.725, and 0.861 for plant height, projected leaf area, surface area, and volume) and practical greenhouse environments (n=48, manual measurements as reference baseline; R 2 =0.965, 0.862 for plant height and stem diameter). Additionally, evaluated against COLMAP benchmarks (n=24), the system achieved a mean 3D reconstruction F1-score of 0.816.
Why it matches plant phenotyping methods植物フェノタイピングロボット向けに、ゼロショット分割・3D再構成・エッジ処理を開発し、植物形質を基準測定およびベンチマークと比較検証しており、取得・抽出手法が研究の中心である。
abstractwe developed a novel phenotyping robot capable of online 3D reconstruction and zero-shot segmentation directly on the edge.
As a critical structural component that connects almost all other types of plant organs, the stem system not only supports the weight of the total plant, but also serves as a vital channel for nutriment transportation. Accurate phenotypic measurement of stem instances is of practical significance for assessing crop growth dynamics and predicting yield. To address current 3D phenotyping challenges of crops such as the difficulty in separating stem segments from the stem system and the low accuracy in stem length measurement, we propose a Hierarchical-annotation-guided Phenotypic Framework (HagPF) for Stem Instance Segmentation and Length Measurement in plant point clouds. Specifically, a hierarchical leaf-stem organ instance annotation strategy is devised to effectively train a PSegNet network for leaf and stem instance segmentation. The segmentation is then followed by a shape-adaptive measurement algorithm to automatically measure the length of stem segments that are morphologically diverse in space. On a 3D dataset comprising four crop species, the proposed framework achieved an Intersection over Union (IoU) of 95.45% for organ semantic segmentation and a Mean Weighted Coverage (mWCov) of 87.87% for instance segmentation (both stem and leaf). Regarding to the stem length measurement, the method obtained an average Root Mean Square Error (RMSE) of 1.044 cm and a relative error of 11.907%, outperforming 7 mainstream methods. The relevant dataset and source code can be found at: https://github.com/Jinx00/stem-length-measurement .
Why it matches plant phenotyping methods植物点群から茎のインスタンスを分割し、茎長を自動測定する3D表現型解析フレームワークの開発・比較検証が中心である。
abstractwe propose a Hierarchical-annotation-guided Phenotypic Framework (HagPF) for Stem Instance Segmentation and Length Measurement in plant point clouds.
Finger millet is an important cereal crop widely cultivated worldwide for food and fodder. Breeding programs aim to select genotypes with desirable architectural traits to develop new varieties with higher yields. In this effort, accurate high-throughput plant phenotyping is essential for accelerating crop improvement. To overcome the time-consuming and labor-intensive process of manual measurements, this study presents a comprehensive 3D imaging pipeline that leverages neural radiance fields (NeRF), 3D gaussian splatting (3DGS), and its advanced extensions (e.g., Feature 3DGS and Gaussian Grouping) to reconstruct, segment, and analyze finger millet yield component traits using multi-view 2D images. First, multiple-view RGB images of a single finger millet plant were captured, and COLMAP was then utilized to estimate the camera poses of the images and reconstruct the sparse point cloud, followed by advanced 3D reconstruction through 3DGS and NeRF. Second, feature 3DGS and gaussian grouping models were used to generate the 3D gaussian representation of finger millet panicles. This single-process framework enabled the generation of high-fidelity 3D point clouds and semantic feature fields without the need for expensive depth sensors or manual annotations. Our results demonstrated the effectiveness of these models in capturing morphological variations across different panicle phenotypes, including compact versus open panicle architectures. In addition, the 3D point clouds of the panicles were utilized to extract structural traits for yield prediction, achieving biologically meaningful correlations with grain productivity. This work highlights the potential of 3DGS-based phenotyping pipelines as a low-cost, near real-time, photorealistic solution for trait quantification, segmentation, and yield estimation in real-world agricultural settings.
Why it matches plant phenotyping methods3D画像再構成・セグメンテーション・形質抽出を統合した植物フェノタイピング手法の開発が中心であり、収量関連形質の定量と予測まで技術的に評価している。
abstractthis study presents a comprehensive 3D imaging pipeline that leverages neural radiance fields (NeRF), 3D gaussian splatting (3DGS), and its advanced extensions (e.g., Feature 3DGS and Gaussian Grouping) to reconstruct, segment, and analyze finger millet yield component traits using multi-view 2D images.
Shoot apical meristem (SAM) homeostasis integrates environmental and genetic cues to regulate growth dynamics that drive biomass accumulation and crop yield; however, no robust, non-destructive, quantitative proxy has been established for modeling or monitoring SAM-homeostasis-associated dynamics. Here, we developed a novel robot-based 3D imaging system and a custom pot-chamber gas exchange system to non-destructively measure plant occupation volume (POV) and whole-plant photosynthetic rate in wild-type Arabidopsis plants and nine mutants with disrupted SAM homeostasis. We demonstrate that POV robustly captures 3D plant architecture, whereas whole-plant photosynthetic rate serves as a superior proxy for optimal growth dynamics and final biomass associated with SAM homeostasis, outperforming conventional traits such as leaf number, leaf size, total leaf area, and rosette diameter. The strong positive correlations among POV, whole plant photosynthesis, and biomass accumulation establish a powerful new framework for quantitative studies of SAM homeostasis and data-driven evaluation of plant architecture.
Why it matches plant phenotyping methodsロボット3D画像とカスタムガス交換による非破壊的な植物形態・光合成表現型測定系を開発し、従来形質との比較検証も行っており、方法が研究の中心である。
abstractwe developed a novel robot-based 3D imaging system and a custom pot-chamber gas exchange system to non-destructively measure plant occupation volume (POV) and whole-plant photosynthetic rate
Reproduction assets foundThe paper's authors explicitly state that the Python source code for whole-plant leaf-area segmentation, 3D point cloud processing, POV calculation, and Mask3D-based segmentation is publicly available on GitHub at https://github.com/songqingfeng/AtPOVcalculator. This is a paper-specific, public, actionable analysis/PhDCode · publicThe Python source code for whole-plant leaf-area segmentation and calculation is publicly available on GitHub ( https://github.com/songqingfeng/AtPOVcalculator ).Open asset ↗songqingfeng/AtPOVcalculatorlines:224-233Code / dataset availability 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:97-113Plant phenotyping relevance match · UnverifiedCrossref · checked 5 Sept 2026
Field / plotLiDAR / point cloudRGB / grayscaleRootStem / branchWhole plant / canopy / plot / fieldMorphology / geometry measurement2D/3D reconstructionVisualization / data managementArchitecture / morphology / geometry
Diameter at breast height (DBH) is a crucial indicator for obtaining tree phenotypes in orchard management, plantation monitoring, and agroforestry systems. LiDAR technology has high measurement accuracy, but it is costly and difficult to deploy flexibly in outdoor scenarios, while smartphones have emerged as a viable alternative due to their portability and low cost. In this paper, we propose a DBH estimation method based on monocular depth estimation, supported by a mobile application for algorithm deployment and result visualization. To address the limited computing resources on mobile devices, we design HR-DiffusionDepth, a lightweight diffusion-based monocular depth estimation network for smartphones, which generates pixel-wise 3D coordinates from a single image using camera intrinsics, thereby replacing LiDAR for DBH calculation. Experiments on the KITTI and SPREAD datasets show that HR-DiffusionDepth achieves the best depth estimation accuracy among similar lightweight models, reducing Abs Rel by up to 25.3% relative to the state-of-the-art (SoTA) lightweight baseline, with only 6.26 M parameters. The validation results show that the root mean square error (RMSE) of DBH estimation is 3.10 cm and the mean absolute error (MAE) is 2.25 cm, demonstrating the potential of this approach for agricultural scenarios such as orchards and plantations.
Why it matches plant phenotyping methodsスマートフォン画像と軽量深度推定ネットワークにより樹木DBH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 propose a DBH estimation method based on monocular depth estimation, supported by a mobile application for algorithm deployment and result visualization.
Soybean pod-related traits are important for seed development, yield formation, and cultivar evaluation. However, conventional measurements are inefficient and have limited ability to characterize complex pod features such as curvature, local enlargement, and continuous color variation. In this study, mature pod images of 187 cultivated soybean accessions collected across two successive years were analyzed using a deep learning-assisted image phenotyping approach. Eight pod-related traits related to size, morphology, and color were extracted from pod images. A genome-wide association study (GWAS) was performed using 61,541 high-quality SNP markers to dissect the genetic basis of these image-derived pod traits. A total of 16 stable loci associated with pod size, morphology, and color traits were identified across 11 chromosomes. Among these loci, eight were not reported in the previous image-based soybean pod GWAS study. Based on SoyBase gene annotation and Gene Ontology biological process information, 32 biologically relevant candidate gene records were prioritized within the corresponding candidate genomic intervals, while pod-related expression profiles and SoyBase association information were used as supporting evidence for candidate gene evaluation. These findings indicate that refined image-derived traits can provide complementary genetic information beyond conventional pod measurements and offer additional opportunities for dissecting soybean pod development, morphology, and mature pod color variation.
Why it matches plant phenotyping methods深層学習画像解析によるダイズ莢形質の抽出が研究の中心であり、8種類の形態・色・サイズ形質を画像から定量化している。
abstractmature pod images of 187 cultivated soybean accessions collected across two successive years were analyzed using a deep learning-assisted image phenotyping approach.
Introduction: Habitat restoration is necessary for the conservation and management of plant and animal species, especially in rare ecosystems. Drones may be well-suited to monitor changes in plant and animal communities in response to restoration efforts. The objective of the study was to examine whether drone imagery can detect differences in vegetation across multiple contexts. Materials and methods: Using a commercially available drone, I captured and processed aerial imagery with an open-source photogrammetric processing program. Point cloud data were processed to generate a vegetation density index, which was quantified across four cover types and compared between disturbance histories. In addition, using automated radio tracking, I compared vegetation density between used and available locations for Eastern Whip-poor-wills during the day and at night. Results: In August 2024, a drone flight covering a 3.05 km2 area of pine barrens captured 3372 images. Vegetation density differed by cover type (p = 0.001) and was greater in recently disturbed sites (p = 0.002). Scrub oak and recently burned sites had ~30% and ~12% greater vegetation density than deciduous forests and plots > 2 years post-disturbance, respectively. Vegetation density was lower at Eastern Whip-poor-will used locations than at available locations (151.0 vs. 159.7 points/m2, p < 0.001). Conclusions: Analysis of fine-scale differences in vegetation structure was important in discriminating subtle differences in habitat selection for Eastern Whip-poor-wills. This study demonstrated that drones and relatively simple image processing can be practical tools for restoration when quantifying and monitoring vegetation differences in dynamic ecosystems.
Why it matches plant phenotyping methodsドローン画像と点群処理により植生密度・植生構造を定量化する手法を中心に、異なる植生条件での適用性を評価しているため、植物表現型計測の方法適用研究に該当する。
abstractPoint cloud data were processed to generate a vegetation density index, which was quantified across four cover types and compared between disturbance histories.
Reproduction assets foundThe paper's data availability statement points to a public Zenodo deposit containing the study's drone-derived vegetation density data and related measurements.Dataset · publicThe data supporting the findings of this publication has been made available within a publicly accessible
repository at https://doi.org/10.5281/zenodo.20398090.Open asset ↗Zenodo · 10.5281/zenodo.20398090pdf-page:11 lines:1-49Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Background: Rice breeding requires faster development of high-yielding, climate-resilient, resource-efficient, and high-quality cultivars for production systems exposed to environmental variability and increasing input constraints. Genomic selection offers an opportunity to predict breeding value before extensive field evaluation, although its effectiveness depends on the integration of genomic, phenotypic, and environmental information. Methods: This narrative review critically examines recent advances in genomic selection for rice and its integration with high-throughput genotyping, high-throughput phenotyping, machine learning, multi-environment prediction, and speed breeding. Results: Genome-wide marker data can support early ranking of breeding materials for grain yield, grain quality, disease resistance, drought tolerance, salinity tolerance, and nutrient-use efficiency. Prediction performance is influenced by trait architecture, marker density, training-population size, genetic relatedness between training and candidate populations, phenotypic data quality, and genotype-by-environment interaction. Red-green-blue, multispectral, hyperspectral, thermal, and light detection and ranging platforms can generate temporal traits associated with plant architecture, biomass, water status, nutrient status, and stress responses, which may improve prediction under suitable population and validation designs. Speed-breeding systems shorten generation intervals and facilitate rapid advancement, recurrent selection, and recycling of superior parental lines. Conclusions: Integrated breeding pipelines that combine genomic prediction, high-throughput phenotyping, environmental data, and speed breeding can improve selection efficiency and shorten rice improvement cycles. Wider adoption will require affordable technology platforms, standardized data systems, multi-environment validation, breeder capacity development, and collaborative data-sharing frameworks for smart and greener agriculture.
Why it matches plant phenotyping methods高スループット表現型解析をゲノム選抜との統合という方法論的主題の一部として批判的にレビューしており、各種画像・センサープラットフォームと形質抽出を扱うため、表現型手法レビューに該当する。
abstractThis narrative review critically examines recent advances in genomic selection for rice and its integration with high-throughput genotyping, high-throughput phenotyping, machine learning, multi-environment prediction, and speed breeding.
Abstract. Urban vegetation is essential for mitigating the Urban Heat Island effect, yet its cooling performance depends on its three-dimensional structure. This study combines high-resolution Unmanned Aerial Vehicle - based LiDAR (Zenmuse L2) and thermal imaging (Zenmuse H20) to analyze vegetation structure and surface temperature across 4 urban parks in San Nicolás de los Garza, Mexico. LiDAR data were processed to generate Digital Terrain Model, Digital Surface Model and Canopy Height Model models, enabling the segmentation of individual trees and extraction of structural metrics such as canopy height, crown area and point density. Thermal orthomosaics were co-registered with LiDAR models to quantify temperature contrasts between vegetated and impervious areas. Results reveal consistent cooling effects in all parks, with vegetated zones showing 8–15 °C lower surface temperatures depending on canopy density and maturity. Larger parks with continuous canopies displayed the strongest thermal regulation. This integrated LiDAR–thermal approach provides a precise and scalable framework for assessing microclimatic benefits of urban vegetation, supporting climate-resilient planning in rapidly urbanizing regions.
Why it matches plant phenotyping methodsUAV LiDAR・熱画像を用いて個体樹木の樹冠高や樹冠面積などの植物構造形質を抽出する手法と統合ワークフローが中心であり、単なる環境測定ではない。
abstractLiDAR data were processed to generate Digital Terrain Model, Digital Surface Model and Canopy Height Model models, enabling the segmentation of individual trees and extraction of structural metrics such as canopy height, crown area and point density.
The present study validates a custom multi-sensor system for high-resolution, non-phenotyping. The photogrammetric workflow was optimised by evaluating image density, algorithms, and camera calibration. Beyond validation, a case study demonstrated the system’s capacity to monitor early plant development following biostimulant treatment. Technical assessment revealed that prior internal camera calibration was unnecessary for the optics used. A reduced dataset of 120 images yielded reconstruction accuracies statistically comparable to full 360-image sets ( p > 0.05), confirming potential for maximised throughput efficiency by reducing processing time by approximately 66% without compromising data integrity. Analysis demonstrated that algorithmic settings determined reconstruction accuracy. Active parameter tweaks were essential for maximising surface area precision across all plant species ( R 2 ≥ 0.98). Conversely, volumetric accuracy required deactivation of these tweaks to maintain mesh consistency. While surface area estimation remained robust, volumetric precision showed species-specific variability driven by morphological complexity. Baseline parameters for accurate plant health scaling were established by defining species-specific vegetation index ranges (e.g., 0.38–0.82 for C. sativus ). The platform’s robustness was validated through a longitudinal study evaluating four treatments: yeast autolysate (A), a fungal biostimulant (F), their combination (AF), and a control (C). The system captured distinct morpho-physiological responses, demonstrating that autolysate-based treatments (A and AF) significantly enhanced biomass growth. The developed multi-sensor system recorded surface area expansions of 122% and 102% relative to the control ( p < 0.001), alongside a 110% increase in biological height. Fidelity of these 3D reconstructions was substantiated by a strong correlation ( R 2 = 0.96) between the 3D-derived leaf area index and ground-truth measurements. A key innovation of the pipeline is the integration of vertical distribution metrics as descriptive statistical tools, enabling high-resolution characterisation of canopy architecture. The plant health status metric evidenced enhanced physiological resilience in variants A and AF. Gravimetric analysis corroborated the non-destructive findings, confirming significant increases ( p < 0.001) in dried shoot weight of 77% (A) and 79% (AF). The validated system decoupled structural biomass from physiological health, offering broad utility across diverse phenotyping tasks. Such functionality streamlines the valorisation of industrial by-products into biopreparations, driving progress in sustainable agriculture.
Why it matches plant phenotyping methodsフォトグラメトリとマルチスペクトル計測による植物表現型取得システムの最適化・技術検証が研究の中心であり、植物形態・生理状態の測定性能を評価している。
abstractThe present study validates a custom multi-sensor system for high-resolution, non-phenotyping.
Rice stem internal structure is a critical micro-phenotype influencing lodging resistance and yield; however, its analysis remains constrained by labor-intensive manual methods. Here, we present a publicly available dataset of 686 rice stem cross-sections, with 21,027 large vascular bundles (LVBs) and 19,342 small vascular bundles (SVBs) manually annotated. Five deep learning architectures were systematically evaluated, among which UNet-VGG16 achieved the best performance with a mean intersection over union (mIoU) of 87.4% (82.49% for LVBs and 74.41% for SVBs). An improved model, UNet-ECA-Bio, further raised mIoU to 89.32% and SVB IoU to 78.97% by integrating Efficient Channel Attention (ECA) and biologically informed class weighting using an image-level dataset. Leveraging these high-accuracy phenotypic predictions, genome-wide association studies (GWAS) indicated concordance between annotated and predicted traits, with SNP overlap rates of 96% (LVB count: 1,217/1,262), 43% (SVB count: 6/14), 98% (stem area: 122/124), and 100% (cavity area: 3/3) at −log10(p) ≥ 6. Meanwhile, compared with manual annotation (estimated 10–30 minutes per image), the proposed approach processed all 686 images within 10 minutes, representing a >600-fold increase in throughput. We further developed a user-friendly software tool, “Rice_Stem_Pre_V1.1.exe,” for automated phenotyping of 14 stem traits, providing a cost-effective platform for genetic studies of lodging resistance and yield improvement.
Why it matches plant phenotyping methodsイネ茎維管束の画像から複数の表現型形質を自動抽出する深層学習モデル、データセット、検証、ソフトウェアを中心的に開発しており、明確な植物フェノタイピング手法研究である。
abstractwe present a publicly available dataset of 686 rice stem cross-sections, with 21,027 large vascular bundles (LVBs) and 19,342 small vascular bundles (SVBs) manually annotated.
Reproduction assets found本文中に内容が明示された植物フェノタイピング関連の補足表と、その公開リンクを確認しました。Supplement · publicSupplementary Table 6
Description of annotated and predicted stem internal structural traits.Open asset ↗lines:510-582Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 11 Sept 2026
Blackberry micropropagation enables the rapid production of pathogen-free and genetically uniform plant material, although the evaluation of in vitro shoot development still relies on destructive and time-consuming measurements. This study investigated a low-cost smartphone-based 3D imaging approach for the non-destructive characterization of in vitro blackberry shoots (cultivar ‘Thornfree’) grown under different sucrose concentrations in the media (0, 7.5, 15, and 30 g L−1). Explants were cultured for 30 days under controlled environmental conditions in ventilated vessels containing 15 explants. Three-dimensional reconstructions generated using the viDoC RTK rover system coupled with an Apple iPhone 15 Pro Max were used to extract geometric traits, including shoot height, projected area, and shoot volume estimated through three complementary approaches, together with voxel-derived structural descriptors of shoot spatial organization and compactness. The proposed approach enabled the quantitative assessment of shoot architectural responses to sucrose availability, revealing differences in volumetric development and internal structural organization among treatments that would not be detectable by conventional measurements. The results highlight the potential of smartphone-based 3D phenotyping as a rapid, low-cost, and non-destructive tool for monitoring structural traits in micropropagated plant material and for supporting the optimization of in vitro culture conditions.
Why it matches plant phenotyping methodsスマートフォン由来の3D再構成を用いてシュート形態・構造形質を抽出する手法が研究の中心であり、非破壊植物フェノタイピングへの実質的応用である。
abstractThree-dimensional reconstructions generated using the viDoC RTK rover system coupled with an Apple iPhone 15 Pro Max were used to extract geometric traits, including shoot height, projected area, and shoot volume estimated through three complementary approaches, together with voxel-derived structural descriptors of shoot spatial organization and compactness.
Abstract Architectural analysis provides a powerful analytical framework for understanding the ontogenetic trajectories of tree species and their adaptive responses to environmental constraints. Despite their ecological, economic, and cultural importance in West African agroforestry systems, the architectural development of Khaya senegalensis (Desr.) A. Juss. (Meliaceae) and Pterocarpus erinaceus Poir. (Fabaceae), two overexploited taxa classified as Vulnerable on the IUCN Red List, had never been formally described. This study presents the first complete characterization of their architectural development, from the seedling stage to senescence, based on architectural and retrospective analyses conducted on 360 individuals per species across seven localities along a south-north bioclimatic gradient in Côte d'Ivoire, covering contrasting vegetation zones ranging from dense humid forest to dry Sudanian savanna. Both species display well-defined ontogenetic trajectories comprising four phases: juvenile establishment, architectural construction, reproductive transition, and crown restructuring associated with ageing. Distinct architectural models were identified: K. senegalensis conforms to Rauh's model, characterized by a monopodial orthotropic trunk with indefinite growth and rhythmic acrotonic branching; P. erinaceus follows Troll's model, in which the orthotropic trunk progressively gives rise to a sympodial plagiotropic system with mixed terminal and lateral flowering. Architectural units, defined as the minimal structural organization enabling a species to reach reproductive maturity, were established at the adult stage: that of K. senegalensis comprises four axis categories and five branching orders, while that of P. erinaceus comprises three axis categories and up to six branching orders in old trees. Significant variation in growth-unit morphology among habitats and localities ( P ) revealed the architectural plasticity of both species in response to ecological gradients. The calculated Favourable Development indices ( FDi ) identified Bouaké and Katiola as optimal zones for K. senegalensis , and Bouaké and Toumodi for P. erinaceus , providing objective spatial criteria for reforestation planning. These findings demonstrate that architectural traits are robust indicators of development, adaptive strategies, and crown functioning. By linking structural organization to productivity, resilience, and regeneration potential, this study provides a scientific basis for integrating architectural analysis into reforestation programmes, sustainable forest management, and the design of agroforestry systems for threatened African tree species facing growing climatic and anthropogenic pressures. Complementary regression analyses further showed that phytomer number, rather than internode elongation, primarily governs growth-unit length in both species, and that growth unit diameter scales positively with growth unit length; a multivariate analysis of variance (MANOVA) confirmed that ontogenetic stage and locality, but not habitat alone, robustly structure growth-unit morphology.
Why it matches plant phenotyping methods樹木の成長段階・分枝構造・成長単位形態を対象に、建築学的および回顧的解析を中心的手法として適用し、植物構造形質と発達状態を定量・比較しているため。
abstractThis study presents the first complete characterization of their architectural development, from the seedling stage to senescence, based on architectural and retrospective analyses conducted on 360 individuals per species across seven localities
1. Morphological traits such as floral area and body size are fundamental to ecological research, serving as inputs for studies of pollinator–plant interactions, habitat quality, and biodiversity monitoring. However, accurately measuring these traits from images remains challenging, particularly in complex field conditions where existing tools exhibit reduced accuracy and limited generalizability across taxa. 2. We present EcoMorph, a modular morphological measurement system that leverages the Segment Anything Model 3 (SAM3) to quantify traits across diverse ecological contexts. Unlike task-specific segmentation models requiring domain-specific training data, SAM3's prompt-based architecture enables segmentation of arbitrary biological structures from natural-language prompts, using the same underlying model across flowers, insects, and other targets without retraining. From the resulting segmentations, EcoMorph extracts three classes of measurement: area, linear dimensions, and object count. 3. We validated EcoMorph across two ecological scales. At the intermediate scale, EcoMorph-derived floral area agreed closely with manual ImageJ measurements (R² = 0.935, n = 74) under simple-background conditions and (R² = 0.928, n = 58) under complex-background conditions, with valid predictions for 95% of images. At the fine scale, EcoMorph-derived insect body area was strongly correlated with hand-measured intertegular distance (r = 0.810, n = 349), capturing body-size variation across species from the small Bombus impatiens to the large Xylocopa virginica. Object counts matched manual counts almost exactly for well-separated insects in an insect box (R² = 1.000, n = 12). 4. By combining prompt-based segmentation with modular measurement, EcoMorph enables high-throughput quantification of area, size, and count from heterogeneous image sources without taxon-specific training. This generality supports a broad range of ecological applications, including pollinator and plant trait research, biodiversity and abundance monitoring, and allometric biomass estimation.
Why it matches plant phenotyping methods植物の花の面積を画像から定量する汎用手法を開発し、手動測定と検証しているため、植物フェノタイピング手法が中心である。
abstractWe present EcoMorph, a modular morphological measurement system that leverages the Segment Anything Model 3 (SAM3) to quantify traits across diverse ecological contexts.
Published23 Jul 2026The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciencesCited by 0 · OpenAlex ↗
Abstract. Accurate estimation of tree volume is essential for precision forestry and sustainable forest management. Traditional forest inventory methods rely on manual measurements of tree height and diameter, which are time-consuming and costly to conduct over large areas, and difficult to perform efficiently in dense forest stands. This study presents a data-driven approach for estimating tree volume from partial tree stem profiles derived from high-resolution datasets. While the study relies on harvester production data (Sweden) and field-measured tree stem profiles (Brazil), the framework is designed to support the estimation of tree volume from close-range remote sensing techniques, such as terrestrial photogrammetry using handheld cameras. Three modelling approaches were evaluated, including two machine learning models (XGBoost and Random Forest) using partial tree stem profile measurements as predictors, and one baseline model (XGBoost) using diameter at breast height and tree height as predictors. The models were developed using two independent datasets: harvester production data of Norway spruce (Picea abies (L.) H. Karst.) from Sweden and field-measured tree stem profiles of Slash pine (Pinus elliottii Engelm.) and Loblolly pine (Pinus taeda L.) plantations from Brazil. The results show that tree volume can be predicted with reasonable accuracy using partial tree stem profiles, although models incorporating tree height achieved the lowest prediction errors. The findings demonstrate that partial tree stem profiles provide valuable structural information for machine learning-based tree volume estimation. This framework supports the future integration of close-range remote sensing techniques into modern forest inventory systems.
Why it matches plant phenotyping methods樹幹プロファイルから樹木体積という植物形質を推定する機械学習・近距離リモートセンシング手法が研究の中心であり、複数モデルと独立データセットで評価している。
abstractThis study presents a data-driven approach for estimating tree volume from partial tree stem profiles derived from high-resolution datasets.
Abstract Plants live in a physical world governed by a multitude of mechanical processes which vary over time. The unique features of plant cells, which are turgor-inflated objects surrounded by the cell wall, present an intricate perception and response system for mechanical forces. A powerful tool to investigate how plants adapt and react to these cues is Atomic Force Microscopy (AFM), which can provide information about surface morphology as well as mechanical properties. In the context of cell wall biomechanics, there remains some controversy on appropriate AFM measurement practices and suitable use of common terminologies. Specifically, the interpretation of plant cell indentation curves and derivation of the wall elasticity modulus can be challenging and continues to spark debate. In this Expert View, we discuss recent advances of AFM in plant science as well as best practices for the use of AFM and considerations for data interpretation with a focus on mechanical probing by indentation.
Why it matches plant phenotyping methods植物細胞の表面形態と力学特性をAFMで測定・解釈する実践と標準化を扱うレビューであり、植物形質取得法が中心です。
abstractAtomic Force Microscopy (AFM), which can provide information about surface morphology as well as mechanical properties.
Precision agriculture increasingly relies on detailed structural information, such as canopy height and canopy volume, to enhance crop health monitoring and operational safety. However, existing methods based on costly LiDAR or RGB-D sensors are often impractical for large-scale deployment in dynamic and unstructured horticultural environments. Furthermore, conventional 2D segmentation and SLAM-based pipelines typically generate sparse, geometrically inconsistent semantic maps which are insufficient for actionable structural analysis in agricultural applications. To overcome these limitations, we propose a monocular 3D structural mapping framework tailored for horticultural plants via semantic scene completion. At inference, the proposed model takes a single RGB image as input and predicts voxel-wise geometry and semantics, from which task-oriented structural maps, including canopy height, canopy volume, and obstacle-aware traversability layers, are derived. Specifically, we first introduce a Depth-Aware Decoder Module that explicitly recovers depth in the spatial domain and fuses 2D-to-3D features, thereby mitigating depth ambiguity and reducing reliance on accurate pose. Second, an NCS-Guided Geometry Encoder is designed to inject normalized depth into voxel positional embeddings, enabling self-attention to perform global relational modeling within a depth-aware geometric coordinate system. In addition, a Global Encoder is utilized to refine local structural details, while an occupancy head produces the final 3D semantic completion outputs. We construct a horticultural 3D semantic scene dataset using an RGB-D sensor, which serves as a benchmark for evaluating our method, while the deployed model remains RGB-only. Extensive quantitative and qualitative experiments are conducted on both the Semantic-KITTI dataset and our dataset. On our dataset, the method achieves 82.31% occupancy IoU, 84.26% mIoU, and 86.25% precision. Beyond voxel-level evaluation, manual field measurements further show canopy height MAE values of 0.019-0.026 m and canopy volume proxy relative errors of 8.4%-11.4%. These results demonstrate the effectiveness of our approach in real-world agricultural scenarios, providing actionable structural insights for crop monitoring and autonomous robotic operations.
Why it matches plant phenotyping methods単眼RGB画像から植物の樹冠高・樹冠体積などの構造形質を推定する3Dフェノタイピング手法を開発し、データセット構築と実測検証も行っているため、方法が研究の中心である。
abstractwe propose a monocular 3D structural mapping framework tailored for horticultural plants via semantic scene completion.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 5 Sept 2026
Abstract Quantifying blueberry fruit yield and maturity is important for evaluating yield potential in breeding trials, but manual measurement remains slow, labor-intensive, and costly. Object detection and classification networks offer a high-throughput solution, yet few studies validate image-based counts against hand-harvested ground truth while explicitly accounting for canopy occlusion. Hence, this study developed a multi-class berry detection pipeline for immature and mature berries and validated image-based estimates against hand-harvest counts across 32 diverse southern highbush blueberry genotypes. Among the models evaluated, YOLOv8x achieved the highest detection performance, with an mAP50 of 0.82 and an mAP50–95 of 0.66. External validation produced F1 scores ranging from 0.74 to 0.91 for berry maturity classes. However, image-based detections systematically underestimated hand-harvested fruit counts, with R² values ranging from 0.40 to 0.61. Fruit occlusion varied widely among genotypes, from 42% to 90%, indicating that canopy structure strongly affects berry visibility. Incorporating image-derived canopy architecture, color, and texture features improved predictions of berry counts and maturity. Partial Least Squares regression provided the best performance, increasing R² values of hand-harvested fruit counts, ranging from 0.52 to 0.74. These results show that accounting for canopy occlusion improves image-based estimation of blueberry yield and supports more accurate high-throughput phenotyping.
Why it matches plant phenotyping methodsブルーベリーの収量・成熟度を画像から推定する検出パイプラインを開発し、手収穫値との外部検証および樹冠遮蔽・構造を考慮した改良を行っており、植物表現型取得法が中心である。
abstractthis study developed a multi-class berry detection pipeline for immature and mature berries and validated image-based estimates against hand-harvest counts
Agrophotovoltaic (APV) systems provide a unique opportunity for improving agricultural land-use efficiency by combining crop production with solar energy capture via photovoltaic panels. In-depth information on plant growth patterns within the spatially heterogenous microclimate created by APVs would enable better planning and management within such unconventional systems. Thus, the present study demonstrates the implementation of a customized robot-mounted 3D-multispectral imaging system for monitoring the growth and spectral reflectance patterns of a conventional soybean cultivar "Eiko" (EK) and a chlorophyll-deficient mutant variety MinnGold (MG) under an APV system. Weekly trends in canopy morphometric features revealed significant variations in canopy height, surface area, light penetration, and volume across the APV field depending on the proximity with the overhead solar panels for both EK and MG, with plants receiving adequate rainfall and intermittent shade performing the best. Furthermore, although spectral indices exhibited variations between EK and MG due to intrinsic differences in pigmentation, symptoms of stress could be detected for both genotypes within rain-shaded areas of the APV plot. Hence, the present investigation depicts the potential for complementary usage of robotics and machine vision for high-precision high-throughput crop monitoring under APVs, which would help improve crop management within such non-homogenous cultivation systems.
Why it matches plant phenotyping methodsカスタマイズしたロボット搭載3Dマルチスペクトル画像システムを実装し、植物形態・スペクトル・ストレス状態を高精度に取得することが研究の中心である。
abstractthe present study demonstrates the implementation of a customized robot-mounted 3D-multispectral imaging system for monitoring the growth and spectral reflectance patterns
Traditional manual measurement of garlic bulb phenotypic traits is inefficient, subjective, poorly reproducible, and may cause sample damage. To improve the adaptability of three-dimensional reconstruction to garlic bulb morphology and grading-related parameter extraction, this study developed a non-destructive phenotypic measurement workflow based on multi-view image-based three-dimensional reconstruction. Four garlic materials with distinct bulb morphologies and epidermal characteristics were used to demonstrate the feasibility of the reconstruction workflow, and 40 Lanling white-skinned garlic bulbs were used for quantitative accuracy validation. Multi-view images were acquired using a high-resolution camera, a motorized turntable, and a controlled illumination system. Three-dimensional models were reconstructed using ContextCapture, and the resulting point clouds were processed in CloudCompare through cropping, denoising, downsampling, and pose correction. Maximum longitudinal diameter, maximum transverse diameter, and volume were extracted from the processed point clouds according to GB/T 45244-2025 (Grades and Specifications of Garlic) and validated against manual reference measurements. The coefficients of determination for maximum longitudinal diameter, maximum transverse diameter, and volume were 0.9935, 0.9909, and 0.9924, respectively, with RMSE values of 0.0529 cm, 0.0520 cm, and 0.8874 cm3, and MAPE values of 0.6647%, 0.7765%, and 1.9149%. Additional MAE, bias, confidence interval, and Bland–Altman analyses further supported the agreement between model-derived and manual reference measurements. These results demonstrate the feasibility of multi-view image-based three-dimensional reconstruction for non-destructive garlic bulb phenotypic measurement and provide a methodological basis for future grading-related assessment and three-dimensional phenotyping of bulbous horticultural crops.
Why it matches plant phenotyping methodsニンニク球の形態形質を非破壊的に取得する3D画像計測ワークフローを開発し、手動測定と定量検証しており、フェノタイピング手法が中心である。
abstractthis study developed a non-destructive phenotypic measurement workflow based on multi-view image-based three-dimensional reconstruction.
Sustainable forest management requires inventory workflows that provide spatially explicit structural information while retaining field-based calibration and uncertainty control. This case study evaluated a field-calibrated UAV LiDAR workflow for individual-tree inventory in middle-aged and near-mature larch plantations in Chuanying District, Jilin City, China. UAV laser scanning point-clouds were integrated with six 30 m × 30 m field plots to assess three individual-tree extraction algorithms: point-cloud segmentation (PCS), marker-controlled region growing (MCRG), and region-based hierarchical cross-section analysis (RHCSA). Algorithm performance was evaluated using plot-level recall, precision, F-score, localization RMSE, tree-height and crown-width accuracy, bootstrap confidence intervals, exploratory Wilcoxon signed-rank comparisons, and leave-one-plot-out stability checks. MCRG provided the most balanced numerical performance under the tested configuration, with a mean F-score of 0.845, compared with 0.808 for PCS and 0.827 for RHCSA. However, the MCRG-RHCSA paired difference was not robust across the six plots, and the analysis should be interpreted as a dataset-specific workflow comparison rather than a universal algorithm ranking. Tree height was estimated with comparatively high accuracy, whereas crown-width estimation remained weak, indicating that vertical canopy structure was more reliable than lateral crown delineation. After calibration assessment, the workflow was applied to 157.47 ha of UAV LiDAR survey areas and generated 219,996 algorithm-based detections. These outputs are best interpreted as a spatial decision-support layer for compartment updating, density screening, and field-inspection prioritization, not as an independently verified wall-to-wall stem census.
Why it matches plant phenotyping methodsUAV LiDARによる個体樹の抽出・樹高・樹冠幅推定を中心に、複数アルゴリズムの精度比較、校正、安定性検証を行うワークフロー研究であり、植物形質取得手法が中核である。
abstractThis case study evaluated a field-calibrated UAV LiDAR workflow for individual-tree inventory
The genetic identity of coffee cultivars is fundamental to the specialty coffee sector, where premium prices are paid under the assumption that the purchased planting material corresponds to the declared variety. However, many producing countries lack the certification infrastructure necessary to guarantee this identity in their informal seed systems, exposing producers to undetected varietal non-conformity. In this study, we examine a case from a specialty coffee ( Coffea arabica L.) farm in southern Ecuador where seeds labeled as Sidra (USD 100/kg) and Gesha (USD 500/kg) were purchased without genetic or phytosanitary certification. Using a combination of SSR-based DNA fingerprinting and quantitative morphological characterization, including plant architecture, leaf functional traits, and fruit characteristics, we documented varietal identity and assessed the discriminant capacity of morphological traits across the four resulting morphotypes. Using eleven microsatellite markers for SSR fingerprinting, we found that two of the four morphotypes did not match their declared commercial identity. One plant sold as Sidra was identified as compatible with Batian, a composite variety of Kenyan origin that is genetically unrelated to Ethiopian landraces. The plants acquired as Gesha corresponded to a pure Ethiopian landrace that is genetically similar to, but not identical to, the Panamanian Geisha reference accession T.02722. Only two morphotypes were confirmed as Sidra. Furthermore, the placement of Sidra within the Core Ethiopia genetic group is consistent with prior population-level analyses and with its likely status as a selected Ethiopian landrace rather than a variety of hybrid origin. Morphological linear discriminant analysis achieved 82.4% overall classification accuracy under leave-one-out cross-validation (LOOCV), with internode length dominating the first discriminant function (LD1 = 66.6%). These results demonstrate that varietal nonconformity in the specialty coffee seed sector can extend to the inadvertent introduction of genetically unrelated material and underscore the urgent need for accessible seed certification.
Why it matches plant phenotyping methodsコーヒー品種識別のための形態形質測定と判別分析が研究の中心であり、形態形質の識別性能をLOOCVで検証しているため、植物フェノタイピング手法の適用・検証に該当する。
abstractquantitative morphological characterization, including plant architecture, leaf functional traits, and fruit characteristics
Reproduction assets foundThe paper's morphological/functional trait dataset (used for the phenotyping and LDA analysis) is explicitly stated to be publicly available on Figshare (10.6084/m9.figshare.32841344). No author analysis code repository is stated; other URLs in the text are generic libraries or cited prior work.Dataset · publicThe morphological and functional trait dataset generated and analyzed in this study is publicly available in the Figshare repository at 10.6084/m9.figshare.32841344 .Open asset ↗Figshare · 10.6084/m9.figshare.32841344lines:526-568Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Here, we present a single-operator push-cart platform equipped with a 16-beam LiDAR. A push-button interface controls data acquisition, and the data processing pipeline removes ground points, filters noise, performs 5-cm voxelization, and produces plot-level canopy metrics. We validated biomass estimation in hairy vetch (Vicia villosa) and corn (Zea mays) leaf- and whole-plant thinning experiments. In vetch, voxelized estimation of plant volume correlated strongly with destructively measured biomass (r2 = 0.88), showing that the multi-beam LiDAR can produce biomass estimates comparable to previously reported methods. In corn, comparisons of perpendicular (0°) and multi-angle LiDAR beams showed significantly greater voxel counts in the upper canopy when angled beams were used (beam angle × height interaction, p < 0.001), demonstrating that multi-beam scanning provides greater penetration into the upper canopy than a single perpendicular scan plane. We also extended the suite of LiDAR-derived traits to include apparent leaf area index (LAI), mean tilt angle (MTA), persistent homology-based stand density, and plot-bounded foliage area density (FAD). The persistent homology algorithm distinguished between leaf-removal and plant-removal treatments (removal type × removal amount, p = 0.0039). LiDAR-derived LAI has been used to estimate canopy leaf area, but gap-fraction approaches do not fully exploit the ability of LiDAR to resolve distance. Plot-bounded FAD used ray length and interception distance within defined plot volumes and was more sensitive to plot-level treatments than apparent LAI or MTA, detecting differences associated with both the removal amount and removal type. These results show that a robust, portable, multi-beam LiDAR cart can reproduce plot-level canopy measurements and improve trait especially in research-sized plots.
Why it matches plant phenotyping methods携帯型マルチビームLiDARプラットフォームと処理パイプラインを開発・検証し、バイオマス、LAI、葉面積密度などの作物形質を推定しているため、フェノタイピング手法が研究の中心である。
titleA Single-Operator Push-Cart Multi-Beam LiDAR Platform for Multi-Trait Field Phenotyping
0. Morphological traits such as floral area and body size are fundamental to ecological research, serving as inputs for studies of pollinator–plant interactions, habitat quality, and biodiversity monitoring. However, accurately measuring these traits from images remains challenging, particularly in complex field conditions where existing tools exhibit reduced accuracy and limited generalizability across taxa. We present EcoMorph, a modular morphological measurement system that leverages the Segment Anything Model 3 (SAM3) to quantify traits across diverse ecological contexts. Unlike task-specific segmentation models requiring domain-specific training data, SAM3’s prompt-based architecture enables segmentation of arbitrary biological structures from natural-language prompts, using the same underlying model across flowers, insects, and other targets without retraining. From the resulting segmentations, EcoMorph extracts three classes of measurement: area, linear dimensions, and object counts. We validated EcoMorph across two ecological scales. At the intermediate scale, EcoMorph-derived floral area agreed closely with manual ImageJ measurements (R 2 = 0.935, n = 74) under simple-background conditions and (R 2 = 0.928, n = 58) under complex-background conditions, with valid predictions for 95% of images. At the fine scale, EcoMorph-derived insect body area was strongly correlated with hand-measured intertegular distance (r = 0.810, n = 349), capturing body-size variation across species from the small Bombus impatiens to the large Xylocopa virginica . Object counts matched manual counts almost exactly for well-separated insects in an insect box (R 2 = 0.9997, n = 12). By combining prompt-based segmentation with modular measurement, EcoMorph enables high-throughput quantification of area, size, and abundance from heterogeneous image sources without taxon-specific training. This generality supports a broad range of ecological applications, including pollinator and plant trait research, biodiversity and abundance monitoring, and allometric biomass estimation.
Why it matches plant phenotyping methods画像から花の面積など植物形態形質を抽出する汎用システムを開発し、手動測定との一致で検証しており、植物フェノタイピング手法が中心である。
abstractWe present EcoMorph, a modular morphological measurement system that leverages the Segment Anything Model 3 (SAM3) to quantify traits across diverse ecological contexts.
Reproduction assets foundThe paper's Data and code availability statement provides a public Zenodo deposit containing the validation datasets and code used for the EcoMorph phenotyping measurements (floral area, insect morphometrics, counts), plus a public web deployment of the EcoMorph software itself.Code · publicValidation datasets and code are available here on Zenodo
https://zenodo.org/records/20980236.Open asset ↗Zenodo · 20980236pdf-page:2 lines:1-54Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Urban forest health may be monitored and supported with the implementation and maintenance of an accurate community tree inventory. The longitudinal recording of a tree’s attributes (i.e. diameter and height) may inform potential inputs and activities related to maintenance, health, and the establishment of a tree protection zone. Urban tree inventories feature barriers to implementation including resources (i.e. labour, time, and finances), competing priorities, and gaps in knowledge. The objectives of this case study were to investigate the feasibility of structure-from-motion (SfM) approaches to measure tree height and trunk diameter (1) with oblique RGB imagery, (2) with and without GCP, and (3) during leaf-off and leaf-on conditions. Structure-from-Motion datasets were obtained using Unoccupied Aerial Systems (UAS) – “drones” and related components – to collect oblique aerial imagery, which was processed with photogrammetry software Agisoft Metashape. Tree height measurements using leaf-on imagery (R2 = 0.58, RMSE = 1.34 m) were more accurate when compared to leaf-off imagery with Ground Control Points (GCP) (R2 = 0.47, RMSE = 3.41 m) and leaf-off imagery without GCPs (R2 = 0.43, RMSE = 3.49 m). Tree height measurements during the leaf-off period had no significant difference when comparing imagery with and without GCPs. Trunk diameter measurements using leaf-off imagery were not significantly different with the use of GCPs (R2 = 0.68, RMSE = 6.39 cm) compared to those without (R2 = 0.68, RMSE = 7.85 cm). This case study highlights the applicability and accuracy of Unoccupied Aerial Systems and Structure-from-Motion methods when collecting important urban tree inventory parameters, and presents an accessible, reliable, and replicable workflow for urban forestry practitioners with limited photogrammetry-related experience.
Why it matches plant phenotyping methodsUAS-SfMフォトグラメトリによる樹高・幹径という植物形態形質の推定法を開発・比較検証し、精度と再現可能なワークフローを評価しているため、方法が中心的です。
abstractThe objectives of this case study were to investigate the feasibility of structure-from-motion (SfM) approaches to measure tree height and trunk diameter
Tree growth determines how much CO2 is sequestered from the atmosphere and temporarily stored in woody biomass. At the same time tree growth is affected by increasing temperatures, more frequent drought periods, late frosts and other extreme events associated with climate change. While continuous measurements of radial (secondary) tree growth using dendrometers are well established, monitoring of shoot elongation (primary growth) has largely been neglected because suitable measurement techniques are lacking. As a result, the effects of climate change on primary tree growth remain insufficiently understood. This work aims at reconstructing native deciduous trees in 3D as a basis for measuring and monitoring shoot elongation over entire tree canopies. Here we explored the use of low-cost UAV photogrammetry and of a multi-camera CraneCam system under real-world conditions. Data were collected in two study areas over an entire growing season. We present sensor evaluations, photogrammetric data acquisition and processing strategies. A special focus is placed on the analysis of the resulting photogrammetric 3D point clouds in terms of accuracy, resolution and completeness. Results demonstrate 3D point accuracies of 5-6 mm for entire trees using consumer-grade UAVs weighing less than 250 g and a 3D reconstruction completeness between 92% and 98% depending on the UAV type. The paper introduces a novel 3Dprinted ground-truth branch to evaluate the capability to reconstructing fine-detail structures such as thin tree shoots. Finally, we discuss operational challenges and initial experiments towards a skeletonization of entire trees based on photogrammetric point clouds.
Why it matches plant phenotyping methods樹冠全体のシュート伸長を測定するための3D再構成手法を開発・評価し、センサー評価、取得・処理戦略、精度・完全性の検証を中心に扱っているため。
abstractThis work aims at reconstructing native deciduous trees in 3D as a basis for measuring and monitoring shoot elongation over entire tree canopies.
PlantCV is an open-source Python project aimed at developing tools to address a range of image-based, plant phenotyping questions. PlantCV has been used for more than 10 years to automate trait collection from image data, and the newest release, PlantCV version 4, continues to lower the barrier to entry for users without substantial coding experience through extensive example use-case tutorials and simplified installation. In addition to usability, we document added functionality since the release of PlantCV v2, including support for more image types such as fluorescence, thermal, and hyperspectral data. Finally, we describe the development of a new subpackage focused on morphological trait measurements like leaf angle, and demonstrate its utility as compared to more manual methods of data collection.
Why it matches plant phenotyping methods植物フェノタイピング用の画像解析ソフトウェア開発と、形態形質測定機能の実証が中心である。
abstractPlantCV is an open-source Python project aimed at developing tools to address a range of image-based, plant phenotyping questions.
Reproduction assets foundThe paper's data availability statement explicitly says that scripts used for the analyses in this paper are publicly available on GitHub (danforthcenter/plantcv-4-paper), and PlantCV source code is available via the PlantCV homepage. This is a paper-specific, public, actionable analysis-code asset.Code · publicerest.
DATA AVA I L A B I L I T Y S TAT E M E N T
Links to code, tutorials, documentation, and other resources
are available on the PlantCV homepage at https://plantcv.org. PlantCV source code is available on GitHub at https://
github.com/danforthcenter/plantcv. Scripts used for analyses
in this paper are available on GitHub at https://github.com/danforthcenter/plantcv-4-paper.O RC I D
HaleySchuhl https://orcid.org/0000-0002-8825-8297
KeelyE. Brown https://orcid.org/0000-0002-5371-5830
ParagK. Bhatt https://orcid.org/0000-0002-0396-6412
DominikSchneider https://orcid.org/0000-0002-5846-5033
Anna L. Casto https://orcid.org/0000-0002-9597-0514
Lucia Acosta-Gamboa https://orcid.org/0000-0001-77Open asset ↗danforthcenter/plantcv-4-paperpdf-raw-page:15 lines:1-92Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Introduction: Plant phenotyping requires accurate and repeatable three-dimensional structural information, but practical acquisition conditions in greenhouses, seedling rooms, and indoor pot experiments often include complex backgrounds, handheld motion blur, and thin leaf structures. These factors reduce the robustness of conventional three-dimensional reconstruction methods and limit their use in low-cost and automated phenotyping. Methods: To address this problem, this paper proposes F2DMAS, an automated three-dimensional plant phenotyping workflow using consumer-grade smartphone videos. The workflow first converts multiview RGB videos into image sequences and removes motion-blurred frames through frequency-domain quality filtering. A frequency-spatial plant segmentation module, termed FSAM3, is then introduced to separate plant structures from complex backgrounds without task-specific annotated training data. The segmented image sequences are further reconstructed using 2D Gaussian Splatting, followed by TSDF-based meshing, scale recovery, and virtual measurement for extracting plant height, canopy width, leaf length, and leaf width. Results: Experiments were conducted on 15 plant species under two acquisition scenarios. The proposed workflow achieved stable plant reconstruction under non-ideal background conditions, with PSNR, SSIM, and LPIPS values of 31.09, 0.9711, and 0.0365, respectively. Compared with the baseline reconstruction workflow, F2DMAS substantially reduced the processing time for mesh extraction while improving reconstruction quality. The extracted phenotypic traits showed strong agreement with manual measurements, with R² values ranging from 0.90 to 0.99, RMSE values ranging from 0.64 to 1.21 cm, and MAPE values ranging from 4.50% to 9.73%. Discussion: These results indicate that F2DMAS can provide an end-to-end workflow from smartphone video acquisition and plant segmentation to three-dimensional reconstruction and phenotypic trait extraction. The proposed method offers a practical and deployable solution for greenhouse seedling cultivation, potted plant experiments, and low-cost three-dimensional plant phenotyping.
Why it matches plant phenotyping methodsスマートフォン動画から植物の3D構造を再構成し、複数の形態形質を抽出・検証するワークフロー自体が中心的な方法論的貢献である。
abstractThe extracted phenotypic traits showed strong agreement with manual measurements, with R² values ranging from 0.90 to 0.99
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 5 Sept 2026
(L.) Merrill) is a highly important crop widely used for food, edible oil, animal feed, and microbial fermentation products. Traditional phenotypic measurement methods are often time-consuming, labor-intensive, destructive to plants, and prone to human error. High-Throughput Phenotyping (HTP) enables precise assessment of multiple soybean phenotypic features, including morphology, physiology, diseases, pests, and agronomic traits. Artificial Intelligence (AI) is a research field dedicated to developing algorithms for multiple tasks. This review highlights the application of HTP and AI in soybean breeding programs. We discuss the challenges of implementing HTP in soybean breeding and focus on the potential and limitations of Deep Learning (DL) to support soybean breeding goals. We demonstrate the application of HTP to key soybean traits, several HTP platforms, as well as DL applications across different datasets and strategies for developing large foundation models. While integrating AI into soybean breeding programs remains a challenge, leveraging HTP data and Large Language Models (LLMs) could reshape soybean breeding.
Why it matches plant phenotyping methods大豆育種におけるHTPとAIの応用、形質評価、プラットフォーム、データセットおよび深層学習を中心に扱うフェノタイピング手法レビューであり、方法論が中心的です。
abstractThis review highlights the application of HTP and AI in soybean breeding programs.
Abstract Background Accurate modeling of wildland fuelbeds requires knowledge of not only where fuels are in three-dimensional (3D) space but also what they are. In this study, we introduce an object-based scanning protocol designed to generate detailed three-dimensional mesh models of individual fuel particles (e.g., seedlings, shrubs, litter, and cones) using an industrial-grade laser scanner. While traditional terrestrial laser scanning (TLS) or photogrammetric approaches tend to require objects to be segmented from broader-scope environment-level point clouds, our approach begins with the object itself. Results By scanning discrete plant parts in controlled conditions and capturing their morphology, surface area, and volume at sub-millimeter precision, we create a methodological foundation for fuel characterization that is structurally explicit and ecologically specific. We also propose a flexible workflow to adapt the scanning process for the extensive natural range of variation in fuel object structures, classifying individual objects based on their structural complexity. Conclusions Digital twins of wildland fuel plants and particles serve as building blocks for future integration with machine learning techniques to improve wildland fuelbed classification and simulation. Our approach shifts the basis of 3D fuels modeling from environmental scanning toward object-driven understanding with implications for fire behavior, emissions, and ecological modeling.
Why it matches plant phenotyping methods個別の植物・植物部位をレーザースキャンし、形態・表面積・体積を抽出するオブジェクト中心の3D計測プロトコル自体が研究の中心であり、植物形態のフェノタイピング手法に該当する。
abstractwe introduce an object-based scanning protocol designed to generate detailed three-dimensional mesh models of individual fuel particles (e.g., seedlings, shrubs, litter, and cones) using an industrial-grade laser scanner.
An integrative principal component analysis-artificial neural network (PCA-ANN) framework was developed to characterize ecotypic variation among parsley landraces and predict quality-related traits in medicinal and aromatic plants. Fifteen Iranian parsley landraces collected from diverse agro-ecological regions, together with two commercial cultivars as reference genotypes, were analyzed to establish predictive links between easily measurable morphological traits and key biochemical, mineral, and essential-oil (EO) characteristics. Twenty-one independent morphological variables were recorded and used as model inputs. To minimize redundancy and multicollinearity, PCA was applied exclusively to the morphological dataset, reducing it to a smaller set of uncorrelated components that preserved most of the variance. These components served as input features for optimized ANN architectures developed to predict antioxidant properties, EO yield and composition, and mineral nutrient content. The resulting PCA-ANN framework achieved strong predictive performance, with R² up to 0.94. It accurately predicted antioxidant, mineral, and compositional profiles from morphological traits alone, demonstrating the potential of morphological phenotyping as a rapid, non-destructive proxy for complex chemical analyses. This integrative modeling approach reduces reliance on time-consuming and costly procedures such as GC-MS and offers a practical decision-support tool for genotype selection, breeding, and quality evaluation in medicinal and aromatic crops. The proposed framework provides a scalable, data-driven strategy for advancing precision agriculture and sustainable management of herbal plant resources.
Why it matches plant phenotyping methods形態形質から抗酸化性、精油、ミネラルなどを推定するPCA-ANNフレームワークの開発が研究の中心であり、形態フェノタイピングを用いた非破壊的な形質推定法に該当する。
abstractAn integrative principal component analysis-artificial neural network (PCA-ANN) framework was developed to characterize ecotypic variation among parsley landraces and predict quality-related traits in medicinal and aromatic plants.
Handheld Mobile Laser Scanning (HMLS) is increasingly used for high resolution 3D mapping in construction, mining and natural environments. This study evaluates the strengths and limitations of HMLS for vegetation assessment in diverse tropical eco-systems across north Queensland, Australia, including rangelands, grasslands, man-groves and estuarine wetland forests. We assessed the accuracy of HMLS-derived point clouds against ground-truth measurements and compared performance with UAV SfM–MVS surveying. HMLS achieved centimeter-level accuracy for vegetation metrics, with mean absolute errors of 8.5 cm for Diameter at Breast Height (DBH) in rangeland forests and 6.7 cm for tussock height. The system consistently produced high-density point clouds, enabling detailed characterization of vertical structure, particularly understory vegetation often obscured in aerial surveys. HMLS proved operationally flexible across closed-canopy wetlands, mangroves, rangeland forests and open grasslands. Key limi-tations included restricted horizontal point cloud penetration in dense vegetation, compounded by access constraints and environmental conditions, and point cloud drift in areas with few geometric features, such as grasslands, which introduced uncertainty in vegetation metrics. High computational demands further constrained workflow effi-ciency. Overall, HMLS demonstrates strong potential as an accurate and versatile tool for vegetation mapping and structural analysis in complex tropical ecosystems.
Why it matches plant phenotyping methodsHMLSを用いた植物の3D形態・構造計測手法を開発的に評価し、地上真値およびUAV手法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ある。
abstractThis study evaluates the strengths and limitations of HMLS for vegetation assessment
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 5 Sept 2026
Abstract In plant breeding and genetics, recent advances in high-throughput phenotyping are beginning to meet the growing demand for large-scale, high-quality phenotypic data that emerged after the development of next-generation sequencing technologies. Recent developments in phenomics have been incorporated into almond breeding programs, facilitating the large-scale acquisition of quantitative phenotypes and the dissection of the genetic architecture underlying morphological and quality-related traits. The implementation of a high-throughput phenotyping platform integrating RGB and hyperspectral imaging with genotyping using the 60K almond SNP array enabled the large-scale characterization of almond populations and the identification of 567 robust marker–trait associations across 66 traits. These analyses revealed two major genomic hotspots on chromosomes 2 and 5 associated with morphological and quality-related traits. These regions harbored biologically relevant candidate genes, including genes associated with OVATE family proteins, brassinosteroid signaling, protein ubiquitination, and acyl-CoA metabolism, as well as other regulators of organ growth, cell proliferation, hormone signaling, and seed development. Furthermore, a novel candidate gene encoding a COMT-like O-methyltransferase involved in lignin biosynthesis was identified and proposed to contribute to shell hardness, a major genetically controlled trait in almond. Together, these findings demonstrate the potential of integrating high-throughput phenomics and genomics to dissect complex traits, identify candidate genes, and accelerate genomics-informed breeding in almond.
Why it matches plant phenotyping methodsRGB・ハイパースペクトル画像を統合した高スループット表現型解析プラットフォームの実装と大規模形質取得が研究の主要部分であり、単なる形質のルーチン測定ではない。
abstractThe implementation of a high-throughput phenotyping platform integrating RGB and hyperspectral imaging with genotyping using the 60K almond SNP array enabled the large-scale characterization of almond populations
Rice lodicules are specialized floral organs located at the base of the ovary that undergo dynamic morphological changes during the flowering period. Water uptake-driven swelling and subsequent dehydration-induced shrinkage of the lodicules trigger floret opening and closure, respectively. Although lodicules play a central role in floret movement, standardized methods for quantitatively monitoring their temporal morphological changes remain limited. Here, we describe a detailed and reproducible workflow for lodicule sampling, dissection, imaging, and quantitative morphometric analysis. Florets are collected at predefined clock time points during the flowering period, and lodicules are carefully isolated under a stereomicroscope. High-resolution imaging is performed under consistent acquisition settings, followed by precise measurement of lodicule length, width, and thickness using image analysis software. This protocol emphasizes positional consistency in sampling, uniform imaging parameters, and standardized data analysis to enhance reproducibility. This method is suitable for evaluating the effects of genetic background or environmental conditions on lodicule morphology. By providing a standardized analytical framework, this protocol enables accurate and quantitative morphometric analysis of rice lodicules during floret opening. Key features • Standardized time-point sampling minimizes variability caused by diurnal fluctuations and handling during lodicule morphometric analysis. • Enables reproducible isolation and imaging of rice lodicules while preserving native morphology and preventing dehydration-induced artifacts. • Time-resolved workflow enables analysis of rapid morphological changes associated with floret opening and closure. • Applicable for comparing genetic and environmental effects on lodicule morphology under controlled experimental conditions.
Why it matches plant phenotyping methodsイネ小花器官の形態を標準化された採取・撮像・画像解析で定量する再現可能な表現型測定プロトコルが中心である。
abstractwe describe a detailed and reproducible workflow for lodicule sampling, dissection, imaging, and quantitative morphometric analysis.
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:1-48Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Non-structural carbohydrates (NSC) stored in the stem play a crucial role in supporting yield formation in rice. However, internode morphological factors associated with NSC accumulation remain unclear. This study aimed to clarify the relationship between internode morphology and NSC accumulation and to identify a robust morphological indicator for evaluating NSC accumulation capacity. Two years of field experiments were conducted using multiple cultivars. The NSC content was quantified for individual internodes and at the whole-plant culm level, and its relationships with internode morphological traits were analyzed. Since the upper internodes (UIN; first and second internodes) and lower internodes (LIN; third and subsequent internodes) exhibited contrasting roles in NSC accumulation, a novel index was introduced, the volume composition ratio (VCR) of UIN/LIN, which represents their relative volumetric contributions within a culm. The VCR of UIN/LIN showed the strongest correlation with culm NSC and high reproducibility across years, outperforming simple morphological traits. In addition, plant growth regulator treatments that altered VCR were accompanied by changes in culm NSC accumulation. Accordingly, the VCR of UIN/LIN serves as a robust morphological indicator of culm NSC accumulation capacity, providing a practical framework for improving stem carbohydrate storage capacity in rice.
Why it matches plant phenotyping methodsイネ茎の形態からNSC蓄積能力を評価する新規指標VCRを導入し、複数年で再現性と既存形態形質との性能を検証しており、形態表現型の抽出・評価法が中心である。
abstracta novel index was introduced, the volume composition ratio (VCR) of UIN/LIN, which represents their relative volumetric contributions within a culm.
NeRF / 3D Gaussian SplattingLiDAR / point cloudLeafWhole plant / canopy / plot / field2D/3D reconstructionGrowth / time-series analysisTrackingArchitecture / morphology / geometryGrowth / development / phenology
Quantifying plant growth dynamics from sparse longitudinal 3D observations is fundamental for agriculture and plant sciences. Yet, plants pose unique challenges: they undergo intricate non-rigid deformations, exhibit changing topology as new organs emerge, and often lack explicit temporal correspondences between consecutive data acquisitions due to newly formed tissue. Methods designed for general scenes struggle to model topology changes and asynchronous organ growth characteristic of plants. To address these challenges, we introduce GrowFields, a compositional dynamic neural field representation for organ-aware 4D plant growth modelling from point cloud time series. Our approach decomposes a plant into its constituent organs and aligns each organ into its own canonical coordinate frame, isolating intrinsic growth patterns from global plant motion. We then learn a shared continuous neural deformation field that models temporal dynamics across all organs, conditioned on learnable per-organ latent codes capturing organ identity and growth characteristics. The resulting modular yet unified representation naturally accommodates the asynchronous development of plant organs while remaining grounded in the practical setting of organ-level plant tracking. We evaluate GrowFields on growth sequences from four plant species, assessing geometric fitting and organ tracking accuracy using manually annotated leaf-tip trajectories. Results demonstrate consistent improvements in spatial precision, temporal coherence, and morphological fidelity over a range of existing representations.
Why it matches plant phenotyping methods植物の3D時系列観測から器官追跡と成長動態を抽出する、オルガン認識型4Dニューラルフィールド手法の開発・評価が中心である。
abstractwe introduce GrowFields, a compositional dynamic neural field representation for organ-aware 4D plant growth modelling from point cloud time series.
ABSTRACT Vase life is a key determinant of cut flower quality and market value. Conventional vase life assessment relies on visual inspection and physiological monitoring over several days to weeks, making it labor‐ and time‐intensive. This study introduces a rapid and noninvasive approach using plant acoustics to assess postharvest vase‐life‐related variation in cut chrysanthemum flowers. Six chrysanthemum cultivars were grown under two supplemental lighting treatments (Hybrid and LED) and two planting densities (54 and 74 plants m −2 ). Acoustic monitoring was compared with optical microscopy for the assessment of xylem vessel diameter, while conventional vase‐life testing was performed in parallel. Optical microscopy validated the acoustic measurements, with both methods consistently identifying vessel radii around 10 μm. The acoustic radius (), derived from pulse settling time measurements, showed cultivar‐ and planting‐density‐specific variation. Linear mixed‐effects modelling demonstrated that the relationship between acoustic radius and vase life differed significantly among cultivars, indicating that a universal relationship across cultivars is not supported. These findings show that acoustic monitoring provides a meaningful noninvasive proxy for vase‐life‐associated stem traits and may serve as a useful cultivar‐calibrated tool for evaluating postharvest longevity in cut chrysanthemums.
Why it matches plant phenotyping methods植物の茎の道管径に基づく音響的な非破壊測定法を開発し、光学顕微鏡および花持ち試験で検証しているため、表現型取得法が研究の中心です。
abstractThis study introduces a rapid and noninvasive approach using plant acoustics to assess postharvest vase‐life‐related variation in cut chrysanthemum flowers.
Leaf orientation (posture) influences photosynthesis and plant responses to environmental cues. However, the existing methods for quantifying posture typically compress its inherently three-dimensional structure into scalar values or single vectors, leaving the 3D aspects of leaf movement poorly understood. For a more complete geometric description, we propose representing leaf-blade posture by an orthonormal basis (ONB), defined as three perpendicular unit vectors aligned with the developmental axes of the leaf. This ONB serves as a local coordinate system and corresponds to rotation matrices used to represent orientation in three-dimensional space, embedding leaf posture within the mathematical structure of the special orthogonal group SO(3). Using three-dimensional point-cloud data, we reconstructed an ONB aligned with the three axes of the leaf blade and quantified elevation and azimuth angles. When applied to diurnal posture changes, the resulting angular patterns were consistent with previous observations. We then visualized posture changes after gravitational perturbation as continuous rotational trajectories. These trajectories could be compared with mathematically defined geodesic shortest paths and used to simulate alternative reorientation routes that satisfy constraints. The rotational trajectories could also be separated into swing and twist components, which reflect distinct deformation modes. The ONB can be obtained not only from three-dimensional point clouds but also from other measurement tools, making the approach broadly applicable. Conceptually, ONB representation places leaf posture within the geometric structure of SO(3), enabling the use of well-established mathematical tools such as rotational distance and geodesics for analyzing leaf reorientation.
Why it matches plant phenotyping methods葉の3次元姿勢を点群から定量化・解析する幾何学的方法を提案し、日周変化と重力摂動で実証しているため、植物表現型取得・解析が中心である。
abstractwe propose representing leaf-blade posture by an orthonormal basis (ONB)
The objective of this study was to develop a 3D plant modeling strategy that enables camera pose recovery from segmented plant images and the reconstruction of an initial point cloud. A lightweight, contour-aware framework leverages the view-consistent and surface-oriented representation of 2D Gaussian Splatting, making it suitable for plant surface reconstruction under the Plant-to-Camera mode. A contour-weighted Laplacian regularization suppresses depth discontinuities around plant boundaries, while simplified Gaussian primitives improve computational efficiency without compromising geometric fidelity. Organ-level semantics are integrated into the reconstructed geometry to distinguish plant organs such as leaves, stems, and ears. On maize and wheat datasets, our method outperformed existing approaches in terms of morphological fidelity, organ-level structural consistency, and processing speed, demonstrating its suitability for plant phenotyping
Why it matches plant phenotyping methods植物器官の3D再構成と形態情報抽出を目的とする計算手法を開発し、既存法と形態忠実度・器官構造整合性・処理速度で比較評価しており、フェノタイピング手法が中心である。
abstractThe objective of this study was to develop a 3D plant modeling strategy that enables camera pose recovery from segmented plant images and the reconstruction of an initial point cloud.
Accurate perception and 3D reconstruction of fruit tree branch structures are fundamental to smart orchard development, with broad applications in intelligent harvesting, crop phenotyping, and precision management. However, the slender and highly branched morphology, multi-scale distribution, weak surface texture, and severe occlusion inherent to fruit tree branches pose substantial challenges to high-fidelity modeling. This paper systematically reviews advances in branch feature extraction and 3D reconstruction for fruit tree canopies. A structured literature search was conducted using the Web of Science, Scopus, and Google Scholar databases, with search terms including “fruit tree branch”, “point cloud reconstruction”, “3D canopy modeling”, “branch feature extraction”, and “agricultural robotics”. Studies published between 2000 and 2025 were considered, with inclusion criteria requiring relevance to branch structure perception, reconstruction accuracy, or orchard application; non-peer-reviewed sources and studies lacking quantitative evaluation were excluded. We trace the evolution of feature extraction from classical 2D image processing and geometric fitting, through point cloud segmentation and skeleton extraction, to modern deep learning approaches and multimodal perception techniques. For 3D reconstruction, we compare active and passive sensing strategies alongside both explicit and implicit scene representation methods, discussing their respective strengths and applicable scenarios. A five-dimensional evaluation framework is also proposed, encompassing geometric accuracy, structural consistency, feature stability, computational efficiency, and generalization capability. Finally, we identify key bottlenecks in fine-grained structure recovery, occlusion handling, and cross-scene generalization, and highlight future directions in structural prior integration, multimodal collaborative modeling, and lightweight neural representations—offering a structured reference for advancing 3D perception research in smart orchards.
Why it matches plant phenotyping methods果樹の枝構造の特徴抽出と3D再構成を対象とする、植物形態計測・表現型取得手法のレビューであり、方法論が中心です。
abstractThis paper systematically reviews advances in branch feature extraction and 3D reconstruction for fruit tree canopies.
To overcome the inefficiency and subjectivity of manual seedling surveys, this study presents a unsupervised framework for evaluating maize sowing quality and emergence uniformity via UAV-based remote sensing. Centimeter-level multispectral imagery was captured to reconstruct 3D point clouds using SfM and MVS techniques. At the algorithmic level, an improved unsupervised pipeline was developed: the Otsu method was employed for plant segmentation, followed by a Fourier Transform to extract 2D spatial frequency features for precise crop row identification and automated spacing measurement. Subsequently, the Combined Entropy Uniformity (CEU) index was developed using Shannon entropy, and a proxy for canopy closure (CCP) was derived using a porosity model, thereby enabling the simultaneous relative quantification of seedling height consistency, spatial distribution uniformity, and canopy geometric structure variability. At the application level, the framework was validated through field trials involving 19 precision planters of diverse configurations. Performance was assessed using indices such as qualified spacing, miss-sowing, and the Coefficient of Variation of Plant Spacing (PSCV). Results indicate that: (1) Vacuum-type planters exhibited optimal stability at speeds of 7–9 km/h, achieving an average qualified spacing rate of 76.7% and a PSCV of approximately 24%, whereas finger-pickup planters were more sensitive to seed size variation and mechanical vibration. (2) The results from the Generalized Additive Model (GAM) suggest a possible nonlinear relationship between seeding rate and certain uniformity indices, indicating that appropriately adjusting operational parameters could help balance operational efficiency and seeding quality; however, this trend requires further validation with larger sample sizes and repeated observations. (3) Point cloud CEU metrics and canopy structure proxies based on the Gap Fraction model showed statistical correlations with certain manually collected indicators, indicating that this method has the potential for rapid screening of seeding quality and relative evaluation of seedling population structure at the field scale under the current experimental conditions.
Why it matches plant phenotyping methodsUAV画像・3D点群から作物の出芽、草丈均一性、空間分布、群落構造を抽出する解析ワークフローを開発し、19種のプランターで検証しており、植物表現型取得法が中心である。
abstractthis study presents a unsupervised framework for evaluating maize sowing quality and emergence uniformity via UAV-based remote sensing.
ABSTRACT Accurate measurement of individual‐tree structural parameters is critical for forest inventory, carbon stock assessment, and urban ecosystem monitoring; however, low‐cost image‐based approaches often fail to capture under‐canopy structures due to canopy occlusion and limited trunk visibility. This study proposes a fully photogrammetric and cost‐effective workflow based on a single‐sensor unmanned aerial vehicle (UAV) using a hybrid flight geometry to improve trunk observability and automate individual tree parameter extraction. A double‐grid acquisition plan combining nadir (−90°) and oblique (−45°) imagery was applied over a semi‐dense forest plot including 120 reference trees. SfM–MVS processing of 918 images produced a high‐density point cloud (162.5 million points) with 1.54 cm/pixel ground sampling distance. Individual trees were delineated using a hybrid strategy integrating DBSCAN‐based coarse clustering, RANSAC‐validated trunk geometry, and trunk‐seeded 3D splitting. Tree height, diameter at breast height (DBH), and crown area were derived from segmented 3D tree models and validated against field measurements and differential GNSS observations. The proposed method successfully detected 108 of 120 reference trees (Precision = 1.00, Recall = 0.90, F1‐score = 0.95) and demonstrated high agreement with in situ measurements, achieving RMSE values of 0.79 m for tree height ( R 2 = 0.975), 3.02 cm for DBH ( R 2 = 0.931), and 3.51 m 2 for crown area. In addition, crown boundary modeling showed that α‐shape reconstruction provided a more realistic representation (IoU = 0.78) than Convex Hull methods by reducing systematic overestimation.
Why it matches plant phenotyping methodsUAV画像とSfM–MVS、点群分割を用いて個体樹木の樹高・DBH・樹冠面積を抽出し、現地測定で検証する方法開発・応用が研究の中心であるため。
abstractTree height, diameter at breast height (DBH), and crown area were derived from segmented 3D tree models and validated against field measurements and differential GNSS observations.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Assessing tools for rapid evaluation of new cultivars across large fields is essential for improving crop yields and ensuring future food security. Current phenotyping approaches remain labor-intensive and imprecise at the field scale, particularly for traits determining light interception and the light extinction coefficient ( K ). While robotic phenotyping and three-dimensional (3D) models have gained interest in estimating light interception in plant canopies, primarily at the single-plant scale or using single-plant-derived virtual canopies, applications at the field-scale canopy level remain limited. In this study, a semi-automated robotic phenotyping platform, PhenoLinc, was deployed to obtain canopy-level multispectral 3D data across 200 diverse wheat genotypes grown under field conditions over two years. 3D canopy architecture revealed substantial genotype-specific variation in inclination angle and K , which influenced radiation use efficiency (RUE), contrasting with the constant K commonly assumed in conventional approaches. This architectural variation was classified as two distinct architectural phenotypes, erectophiles (median angle 65°) and planophiles (59°) at pre-anthesis stages, which converged toward being homogenous by the anthesis stage. Erectophile phenotypes exhibited higher RUE (33.1%) than planophile phenotypes, leading to a higher yield. In yield prediction analyses, 3D-derived architectural traits provided comparable predictive performance to conventional measurements, however, architectural phenotype information reduced prediction error. Together, these findings highlight the value of field-scale robotic canopy phenotyping for characterizing genotype-specific canopy architectural traits and their relationship with yield.
Why it matches plant phenotyping methodsフィールド規模のロボット型3D・マルチスペクトル計測プラットフォームを用いて、コムギ群落の建築形質を取得・評価することが研究の中心である。
abstracta semi-automated robotic phenotyping platform, PhenoLinc, was deployed to obtain canopy-level multispectral 3D data across 200 diverse wheat genotypes grown under field conditions over two years.
Field / plotMultimodalLiDAR / point cloudMultispectral / hyperspectralWhole plant / canopy / plot / fieldArchitecture / morphology / geometry
Crop phenotyping serves as a fundamental basis for crop breeding, precision cultivation, and smart agriculture. In recent years, it has evolved toward multi-modal integration and multi-scale coordination. This paper analysed indoor and outdoor phenotyping platforms across diverse application scenarios, and reviewed sensing technologies including RGB imaging, multi-spectral imaging, hyperspectral imaging, thermal imaging, fluorescence imaging, LiDAR, and nuclear magnetic resonance (NMR). The applications of these technologies were summarized in capturing crop morphological traits, physiological status and biochemical components. The phenotyping acquisition methods and intelligent analytical techniques were also analyzed at different scales such as plant cells, tissues and organs, individual plants, population plot and field. Additionally, the advancements were explored in high-throughput phenotyping technologies and their integration with crop gene function analysis, providing a reference for future phenotyping research.
Why it matches plant phenotyping methods作物表現型センシング技術・装置、取得法、解析技術、プラットフォームを主題とする包括的レビューであり、表現型手法が中心です。
abstractThis paper analysed indoor and outdoor phenotyping platforms across diverse application scenarios, and reviewed sensing technologies including RGB imaging, multi-spectral imaging, hyperspectral imaging, thermal imaging, fluorescence imaging, LiDAR, and nuclear magnetic resonance (NMR).
ABSTRACT Premise Seed size and morphology are critical traits in agriculture, ecology, and genetics, but high-throughput quantification of these traits is often limited by labor-intensive manual measurements or expensive, platform-specific imaging software. Methods and Results We developed SeedMeasure, a lightweight, open-source, and cross-platform command-line tool written in Python that automates the measurement of seed area, length, and width from images. Using a simple imaging setup, the program processes images by correcting for perspective skew, filtering debris, and exports quantitative data alongside quality-check images. We validated SeedMeasure across nine diverse species, ranging from small Arabidopsis thaliana seeds to large Zea mays kernels. The tool quickly handles images using multithreading and demonstrates high reproducibility, yielding low coefficients of variation across repeated runs. Conclusions Compared to existing software, SeedMeasure is free, offers faster processing through parallel computing, and provides standalone executables that require no programming dependencies. SeedMeasure offers an accessible, cost-effective, and high-throughput approach for rapid phenotypic profiling, making advanced seed morphological analysis available to researchers without specialized laboratory hardware.
Why it matches plant phenotyping methods種子画像から面積・長さ・幅を自動抽出するソフトウェアを開発し、複数種で検証しており、植物表現型取得法が研究の中心である。
abstractWe validated SeedMeasure across nine diverse species
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Field / plotLiDAR / point cloudWhole plant / canopy / plot / fieldMorphology / geometry measurement2D/3D reconstructionSegmentationStress / disease detectionArchitecture / morphology / geometryGrowth / development / phenology
Three-dimensional (3D) crop phenotyping is increasingly used to capture crop structure, but its value for crop protection is conditional rather than automatic. 3D approaches are operationally justified only when reconstructed geometry adds decision-relevant information beyond simpler 2D, spectral, scalar, or conventional baselines. This review examines 3D crop phenotyping through a reconstruction-trait-task-maturity framework for crop protection and synthesizes evidence across disease assessment, pest and stress interpretation, pesticide dose adjustment, spray deposition, weed-target perception, protection-oriented breeding, and digital-twin development. The literature is organized through four connected lenses: reconstruction routes that generate crop geometry, 3D traits that may alter protection reasoning, decision pathways that link traits to intervention variables, and maturity levels that distinguish static 3D models, validated phenotypic traits, process-coupled systems, protection outputs, and outcome-updated decision twins. The strongest decision-facing evidence currently comes from canopy-based dose adjustment, deposition prediction, drift reduction, and related spraying applications in which 3D traits are linked to intervention variables and field-facing comparators. Disease, stress, and architecture-aware modelling provide important but more heterogeneous evidence, while many point-cloud datasets, segmentation pipelines, neural reconstruction methods, and agricultural digital-twin frameworks remain upstream of practical crop-protection decisions because they do not yet connect 3D measurements to validated protection labels, comparator baselines, decision thresholds, intervention outputs, or outcome updating. A central conclusion is that high-fidelity 3D representation should not be conflated with decision-twin maturity. Protection-oriented digital twins require explicit coupling among synchronized crop geometry, functional or epidemiological models, decision rules, and recorded field outcomes. This review therefore identifies the evidence and reporting priorities needed to move 3D crop phenotyping toward validated, deployment-oriented, and feedback-aware crop-protection support.
Why it matches plant phenotyping methods3D作物フェノタイピングの再構成、形質抽出、検証、デジタルツイン成熟度を中心に扱うレビューであり、植物フェノタイピング手法が中核。
abstractThis review examines 3D crop phenotyping through a reconstruction-trait-task-maturity framework for crop protection and synthesizes evidence across disease assessment, pest and stress interpretation, pesticide dose adjustment, spray deposition, weed-target perception, protection-oriented breeding, and digital-twin development.
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:
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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:1-49Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Crop ideotype breeding aims to design plant architectures that enhance yield and resource use efficiency. Accelerating this process demands a framework for assembling accurate three-dimensional (3D) architecture. This Perspective synthesizes advances in technologies and methodologies in 3D architectural phenotyping. Rapid progress has enabled translating these advances into tangible gains in breeding efficiency. To this end, we propose integrating functional-structural plant models as an overarching framework that optimizes plant architecture combinations, shifting breeding from experience-driven to predictive ideotype design. Convergence of 3D phenotyping, plant modeling and artificial intelligence holds transformative potential to accelerate breeding cycles, enhancing productivity, sustainability, and food security.
Why it matches plant phenotyping methods3D植物形態フェノタイピング技術・方法論を中心に統合し、機能構造モデルやAIとの連携を論じる方法論的Perspectiveである。
abstractThis Perspective synthesizes advances in technologies and methodologies in 3D architectural phenotyping.
Abstract Forest biometrics has evolved from a measurement-driven discipline focused on field efficiency and statistical rigor to a data-rich, technology-enabled science integrating multisensor information and advanced modeling approaches. This special issue, inspired by the Second North American Forest Mensurationists Conference held in 2022, highlights this transformation through nine studies that collectively span scales from individual branches to regional forest dynamics. Together, they emphasize a shift from identifying single optimal models to developing integrated, uncertainty-aware model systems that support operational decision-making. At the finest scale, advances in terrestrial laser scanning enable improved characterization of branch geometry under challenging conditions, yielding robust taper and form factor estimates for volume. At the tree level, extensive benchmarking of height–diameter relationships demonstrates that model form and stand origin strongly influence predictive performance, with generalized additive models often outperforming traditional approaches. Complementary work shows that calibration strategies are not universally transferable across model forms, underscoring the need for careful alignment of function choice and calibration design. Addressing biases in young stands, Bayesian model averaging offers a practical interim solution where traditional volume models trained on mature cohorts fail. At broader scales, studies demonstrate the operational potential of integrating public and low-cost remote sensing data. Freely available USGS 3DEP LiDAR supports highly accurate dominant height and site index estimation, while bias-corrected digital aerial photogrammetry provides a viable alternative in areas lacking LiDAR coverage. Landscape-level analyses using Landsat time series and permanent plots enable mapping of basal area growth, revealing spatial variability and temporal trends driven largely by stand dynamics. Collectively, these studies define a cohesive framework for modern forest biometrics: combining multiple data sources, selecting model families deliberately, applying light but effective calibration, and explicitly quantifying uncertainty. This integrated approach supports scalable, reliable predictions tailored to the needs of forest managers and policymakers. The special issue thus outlines a forward-looking research agenda that prioritizes resilient modeling systems over isolated solutions, enabling forestry to meet contemporary challenges across scales from tree components to landscapes.
Why it matches plant phenotyping methods森林の枝形状、樹高、林分指標などの植物形質を、レーザースキャン、航空写真、LiDAR、時系列衛星データ、統計モデルで推定・検証する方法群を中心に扱う特集概説であり、測定・推定手法が中心である。
abstractAt the finest scale, advances in terrestrial laser scanning enable improved characterization of branch geometry under challenging conditions, yielding robust taper and form factor estimates for volume.
Accurate and non-destructive volume estimation of agricultural products is essential for precision agriculture, yet remains challenging when transitioning from controlled laboratory conditions to complex orchard environments. Although 2D image-based volume estimation methods provide a cost-effective and scalable solution, existing studies are fragmented and lack a unified perspective on their real-world applicability. This review presents a systematic synthesis of 2D image-based volume estimation methods, explicitly framed through the laboratory-to-orchard transition. We categorized existing volume estimation approaches according to the sensing modality into monocular RGB-based approaches and depth-assisted methods, and further reviewed them based on the image processing methods. A key finding is that high-precision geometric estimation can be achieved in laboratory environments, whereas deep learning and RGB-D fusion have driven a shift from conventional geometric modeling toward data-driven and hybrid learning frameworks in orchard settings. However, 2D image-based volume estimation remains fundamentally limited by scale ambiguity, severe occlusion, and sensitivity to illumination and background variability in real orchard environment. Overall, this review provides a unified perspective for understanding volume estimation methodology across environments and offers guidance for developing robust, scalable, and field-deployable volume estimation systems for real-world agricultural applications.
Why it matches plant phenotyping methods農産物の2D画像から体積を推定する手法を体系的にレビューしており、植物器官・果実の形態形質取得方法が中心である。
abstractThis review presents a systematic synthesis of 2D image-based volume estimation methods, explicitly framed through the laboratory-to-orchard transition.
Accurate stem-volume estimation is fundamental for urban tree inventory and management, but equations developed for forest-grown trees may not be directly suitable for open-grown urban trees with altered stem form and height–diameter relationships. This study developed species-specific, model-assisted stem-volume equations for four dominant urban broad-leaved species in Beijing, China: Quercus mongolica, Sophora japonica, Ginkgo biloba, and Populus davidiana. A total of 2679 standing trees from 535 plots were used for model development and evaluation. The diameter at breast height and ground diameter were field-measured, whereas tree height was obtained as a photogrammetry-derived non-destructive measurement using a handheld tree-measurement superstation. Bivariate DBH–height models, DBH-based linked models, and ground-diameter-based chained models were fitted using weighted nonlinear least squares. Model performance was assessed using validation statistics, 10-fold cross-validation, Monte Carlo uncertainty propagation, and an independent destructive reference dataset of 55 felled trees with section-measured stem volume. Across species, the bivariate models performed best, with mean percent standard errors of 8.68%–16.24%, compared with 9.76%–20.25% for DBH-based linked models and 15.13%–28.56% for ground-diameter-based models. Destructive reference validation showed acceptable agreement within the available validation dataset, with relative RMSE values of 2.30%–5.03% and relative bias values of 0.51%–2.51%. Monte Carlo simulation indicated species-specific propagation of photogrammetric height error, with the lowest average volume fluctuation in Ginkgo biloba. These results suggest that handheld photogrammetry combined with species-specific modelling provides a practical and uncertainty-aware basis for urban stem-volume estimation. This study directly estimates stem volume rather than biomass or carbon stock, and the equations may support future biomass- and carbon-related assessments when combined with appropriate conversion parameters.
Why it matches plant phenotyping methods携帯型フォトグラメトリによる樹高取得と、幹体積推定モデルの開発・交差検証・伐倒木による独立検証が研究の中心であり、樹木の形態形質を定量化する実質的なフェノタイピング手法である。
abstractThis study developed species-specific, model-assisted stem-volume equations for four dominant urban broad-leaved species in Beijing, China
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
L.) is increasingly being recognized for the production of functional food ingredients and nutraceutical products with broad applications in human nutrition. Its nutrient-rich seeds are of particular interest for their nutritional profile. Moreover, its inflorescences and trichomes provide sources of nutrient-rich proteins, bioactive compounds, and functional substances for food formulations. Agronomic practices, environmental factors, and genotype considerably influence the hemp nutritional profile; thus, continued interdisciplinary research is needed to standardize quality across supply chains. X-ray micro-computed tomography (micro-CT) combined with 3D image analysis is an emerging non-destructive technique in high-resolution plant phenotyping. The aim of this work was to show the contribution of X-ray micro-CT to the quantitative characterization of the internal hemp seed structure and of the trichomes. The 3D image analysis approach used allowed us to determine many morphometric traits of the different seed parts and of the trichomes. Among them, volume ratios of the different seed parts and the density and morphological characteristics of the trichomes of two cultivars were accurately quantified. Overall, this work showed the contribution of X-ray micro-CT in 3D morphometric characterization of the hemp achene structure and trichomes. The obtained seed morphometric traits could be correlated in future applications with nutritional and/or physiological properties of different hemp varieties in order to support different aspects of the whole hemp supply chain such as the dehulling process, oil and protein recovery, seed quality evaluation, and genotype screening, to which trichome characterization could also contribute.
Why it matches plant phenotyping methodsX線マイクロCTと3D画像解析による種子内部構造・トライコームの形態形質の定量化が研究の中心であり、植物フェノタイピング手法の実質的な適用に該当する。
abstractX-ray micro-computed tomography (micro-CT) combined with 3D image analysis is an emerging non-destructive technique in high-resolution plant phenotyping.
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:386-410Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 5 Sept 2026
Accurate characterization of plant 3D architecture and semantic parsing of key cotton organs represent an essential prerequisite for crop phenotyping, precision field management, and cultivar breeding and selection. However, the overall 3D structure of field-grown cotton plants is highly complex in field environments. The morphological traits and spatial distribution of various organs are modulated by multiple factors including genetics, environmental conditions, and cultivation management practices, resulting in pronounced phenotypic variation under field conditions. Thus, this study proposed an integrated technical framework for UAV-based 3D reconstruction and organ semantic segmentation tailored for field-grown cotton. High-fidelity 3D point clouds of the crop canopy are generated by coupling an field-adapted low-altitude unmanned aerial vehicle (UAV) acquisition strategy with neural radiance fields (NeRF). Despite these attractive characteristics, segmenting and analyzing such intricate point clouds can be quite challenging. To effectively parse these complex geometric structures, a novel deep learning architecture named FieldCotSeg-Net is introduced. This model integrates an Anisotropy-aware Local Attention (ALA) module and a Hierarchical Feature Refinement Gate (HFRG) module to capture fine-grained features for precise point cloud segmentation. Experimental results demonstrate that the proposed model achieves outstanding performance on both the Huaxing3Dcot and Crops3D cotton datasets. On the Huaxing3Dcot dataset, the model yields a mean intersection over union (mIoU) of 75.7%, representing a 4.5% improvement over the baseline model. On the Crops3D cotton dataset, after retraining the model on this dataset, it attains an mIoU of 74.5%, showing substantial adaptability and effectiveness in organ segmentation. This technical system provides a scalable and robust solution for field cotton phenotyping analysis in breeding research and commercial production.
Why it matches plant phenotyping methods綿花の圃場表現型解析を目的に、UAVによる3D再構成と器官セグメンテーション手法を開発・評価しており、植物形態の取得が研究の中心である。
abstractThis technical system provides a scalable and robust solution for field cotton phenotyping analysis in breeding research and commercial production.
LeafScans-Orchard is a curated, multi-year RGB image dataset of orchard plant leaves designed to support research in computer vision, machine learning, and plant phenotyping. The dataset comprises 9708 high-quality leaf scans acquired during collection campaigns conducted between 2015 and 2025, covering seven orchard crop species: apple, pear, sweet cherry, sour cherry, plum, peach, and apricot. In total, the dataset includes 67 cultivar labels. All samples were acquired using flatbed scanning under controlled conditions on a uniform background, ensuring high visual consistency and minimal background variability. The original scans were captured at 1200 dpi and subsequently converted into a public release format at 300 dpi, stored as lossless TIFF images to preserve morphological and textural details. Each image corresponds to a single leaf and is organized in a hierarchical directory structure by species, cultivar, and acquisition year, accompanied by image-level metadata and aggregated species–cultivar–year counts. LeafScans-Orchard is suitable for plant species classification, cultivar recognition, leaf morphology analysis, texture analysis, and general visual feature extraction. In addition to the main release, a representative subset of 300 original 1200 dpi scans is provided to support high-resolution analyses. The dataset is particularly suited for fine-grained classification, morphology-driven analysis, and methodological studies under controlled imaging conditions.
Why it matches plant phenotyping methods果樹葉のRGBスキャン画像を収録した公開データセットで、植物フェノタイピングおよび葉形態解析を目的とする。標準化された画像取得と再利用可能なデータ構成が中心であり、フェノタイピング用データセットとして適格。
abstractLeafScans-Orchard is a curated, multi-year RGB image dataset of orchard plant leaves designed to support research in computer vision, machine learning, and plant phenotyping.
Reproduction assets foundThe paper's core asset is the LeafScans-Orchard dataset itself (9708 RGB leaf scans, 300 dpi TIFF release plus 1200 dpi subset, image-level metadata and summary counts), openly deposited on Zenodo with an explicit DOI and CC BY 4.0 license. This is a paper-specific, public, actionable phenotyping image dataset. No codeDataset · publicthe published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The dataset described in this article is openly available in Zenodo
as LeafScans-Orchard Dataset (v1.0.0) at https://doi.org/10.5281/zenodo.20187966 (accessed on
10 May 2026). The repository includes the 300 dpi image release, the 1200 dpi high-resolution subset,
image-level metadata, aggregated species–cultivar–year counts, and supporting documentation. The
complete archive of original 1200 dpi scans is retained locally by the authors but is not included in
the current pubOpen asset ↗Zenodo · 10.5281/zenodo.20187966pdf-raw-page:12 lines:1-46Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 15 Sept 2026
The objective of this study is to propose a digital fruit tree reconstruction method based on neural radiation field theory that will enable the efficient, accurate, and non-destructive acquisition of phenotypic information from fruit trees, while simultaneously reducing the cost of collecting this data. Firstly, a low-cost information acquisition platform is constructed for the purpose of shooting a multi-view video around a fruit tree. The video is then extracted and framed using a motion recovery structural algorithm, thereby obtaining a multi-view image sequence of the fruit tree with positional data. Secondly, the the image sequence is employed to train the neural radiation field, thereby obtaining a converged three-dimensional scene of the fruit tree. Ultimately, the three-dimensional scene is derived in the form of a point cloud, thus yielding a high-phenotypic detail point cloud model of the fruit tree. A multi-period point cloud model of an apple tree in an orchard environment, encompassing the flowering, fruiting, and dormant periods, was reconstructed using the aforementioned method. The experimental results demonstrated that the error associated with the tree shape data recorded by the multi-period point cloud model established by the aforementioned method was, on average, below 5%. Furthermore, the average error across all periods was 2.69%, representing a 75.50% reduction compared to traditional reconstruction methods. The dimensional accuracy of the point cloud model at the organ scale can reach the millimetre level, with an average error of 3.10% for fruit diameter, which is 66.19% lower than that of the traditional reconstruction method. The reconstruction method is robust to all periods of fruit trees and can meet the majority of cases of digital fruit tree reconstruction.
Why it matches plant phenotyping methods果樹の多視点画像からNeRFと点群を用いて樹形・器官寸法などの表現型を非破壊取得する手法を開発・検証しており、方法が研究の中心である。
abstractpropose a digital fruit tree reconstruction method based on neural radiation field theory that will enable the efficient, accurate, and non-destructive acquisition of phenotypic information from fruit trees
Cultivars of strawberry (Fragaria × ananassa) differ in photoperiodic responses, which influence the balance between vegetative and reproductive growth, shaping canopy development, biomass production, and water use efficiency (WUE). Using 3D point-cloud phenotyping, this study compared the canopy structure and WUE of the short-day cultivar ‘Sonata’ and long-day cultivar ‘Favori’ grown under identical greenhouse conditions. Cultivar-specific growth and water use traits were quantified using daily non-destructive 3D point cloud phenotyping combined with continuous whole-plant gravimetry, supported by manual and destructive measurements. Non-destructive estimates of plant height and digital biomass corresponded moderately to measurements (height: R2 = 0.628; biomass: R2 = 0.579; mean absolute percentage error (MAPE) = 13.86%). Growth analysis indicated similar relative growth rates between the two cultivars, whereas the crop growth rate was higher in ‘Sonata’ than in ‘Favori’. Integration of growth estimates with gravimetric records revealed higher period average WUE in ‘Sonata’ (3.1 mg g−1) than in ‘Favori’ (2.5 mg g−1). These results highlight the distinctive growth strategies of a canopy-driven pattern in ‘Sonata’ and a reproduction-driven pattern in ‘Favori’. The combined 3D phenotyping–gravimetry framework provides a high-resolution, non-destructive approach to quantify cultivar-specific growth and water use traits.
Why it matches plant phenotyping methods3D点群による非破壊フェノタイピングと連続重量計測を組み合わせ、植物形態・バイオマス・水利用形質を定量化し、測定精度も検証しているため、手法が研究の中心である。
abstractUsing 3D point-cloud phenotyping, this study compared the canopy structure and WUE
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 13 Sept 2026
Modern agriculture operates at an unprecedented crossroads, it must simultaneously accelerate crop yields to feed an expanding global population and adapt to the severe, fluctuating pressures of climate change, structural soil degradation, abiotic water deficits, and evolving biological threats. Historically, selecting resilient crop varieties and implementing field-scale management strategies relied extensively on destructive, labor-intensive, and fundamentally subjective visual metrics. This manual processing approach has long been recognized as the primary operational bottleneck in agricultural advancement.To bridge the gap between rapidly expanding genomic data and actual field performance, the systematic, non-destructive quantification of structural and functional plant traits, plant phenotyping, has emerged as a transformative frontier. By integrating high-throughput engineering, multi-scale remote sensing, deep learning, and advanced molecular biology, modern phenotyping transitions crop science away from qualitative estimation toward highly reproducible, multidimensional data frameworks. This Research Topic presents new advances in advanced 3D reconstruction and deep semantic segmentation at the seedling stage; amodal fruit segmentation, morphological extraction, and early water-stress diagnostics; high-throughput in-field seedling counting and dynamic density modeling; multimodal foundation models, network pruning, and intelligent phytoprotection; aerial and spaceborne remote sensing for canopy analysis and weed monitoring; plant physiology, functional spectroscopy, and functional genomics under abiotic stress; and automated diagnostics for real-time orchard scouting and vineyard management.Automating the characterization of complex spatial layouts under controlled or greenhouse environments is essential for early variety selection and early-stage structural evaluation. Several contributions within this volume provide key breakthroughs in navigating overlapping tissues, severe occlusions, and low-contrast edge regions. showcases how substituting standard convolutions with deformable convolutions enables deep neural networks to accurately isolate the main stem of mature, high-density crops like soybeans. This architecture overcomes the traditional challenges of color mimicry and severe occlusion by pods and leaves, achieving an outstanding mIoU of 90.58% and providing reliable indices for lodging resistance and structural yield modeling (R 2 = 0.9746).Accurately extracting fruit morphology under commercial greenhouse conditions remains heavily constrained by overlapping crop structures, foliage cover, and variable shadows. Simple semantic masks typically fail when a target fruit is partially blocked, leading to a loss of key volumetric data.To resolve the challenge of hidden boundaries, Li, Yin, et al. (2025) developed CGA-ASNet, a specialized RGB-D amodal segmentation network driven by a Contextual and Global Attention (CGA) module designed to restore occluded tomato regions. Trained on a high-fidelity synthetic greenhouse dataset (Tomato-sim) generated via NVIDIA Isaac Sim's Replicator Composer and optimized with a mean coordinate fusion algorithm for real-world validation, this architecture expands the network's receptive field to predict the complete, hidden circular forms of occluded tomatoes, achieving an F@0.75 score of 94.2 and an amodal mIoU of 82.4%. This proves that simulation-to-real (Sim2Real) domain pathways can successfully decode full physical volumes under dense commercial canopies.Complementing this structural restoration, Yang, Li, et al. (2025) designed an integrated diagnostic framework to identify early water stress dynamics in greenhouse tomatoes. Built upon an optimized YOLOv11n core, their system integrates adaptive kernel convolutions (AKConv) into the network backbone's C3k2 modules and implements a recalibration feature pyramid detection head based on the specialized P2 small-target layer. This combination achieved a 5.4% increase in mAP50-95 for identifying fine phenotypic parts. By applying automated geometric analysis to the extracted bounding boxes, the system extracts plant heights and petiole count with low relative errors, feeding these phenotypic parameters into a Random Forest classification routine that flags water-stressed plants with 98% accuracy to guide targeted, automated drip irrigation.Accurate plant stands during early vegetative stages represent the foundational metric required to establish true field emergence rates, validate seed vigor across diverse breeding blocks, and perform early yield predictions.To solve the challenges of small targets, extreme spatial density, and adjacent leaf overlap, Zang et al. (2025) designed DM_IOC_fpn, a wheat seedling counting framework that balances local and global contextual features. By structuring a point-annotated dataset and embedding a densityenhanced encoder module, their network balances micro-scale spatial limits with macro-scale canopy structures. Optimized through a combined loss function tracking counting, classification, and regression parameters, this architecture achieved low error scores (RMSE = 2.91; MAE = 2.23), outperforming standard object-detection benchmarks in complex field environments.At the same time, scaling up to real-time aerial monitoring required major reductions in model complexity to support resource-constrained edge computers on autonomous aerial platforms. Feng, Nie, and Li (2025) engineered an ultra-lightweight YOLOv8n variant tailored for real-time maize seedling counting from high-speed UAV RGB overflights. By reparametrizing RepConv with HGNetV2, they constructed a lean Rep_HGNetV2 backbone, integrated a Bidirectional Feature Pyramid Network (BiFPN) for multi-scale feature alignment, and implemented a Task Dynamically Aligned Detection Head (TDADH). This architecture compressed total model parameters by 47% and reduced weight sizes to 3.5 MB while maintaining a 96.5% detection accuracy and an ultra-fast processing speed of 146.3 FPS, paving the way for low-cost, real-time field scouting.Automated phytoprotection requires machine-vision architectures capable of generalizing across highly diverse species, complex field conditions, and varying computational boundaries. A significant subset of the published papers addresses these challenges through foundation model adaptation, multi-modal alignment, and efficient network compression.A major paradigm shift presented in this collection involves moving away from task-specific training and toward foundation model adaptation. Chen, Ruan, et al. (2026) introduce a novel architecture integrating the DinoV3 foundation model with a Unet framework to achieve robust leaf lesion segmentation across diverse species (such as coffee and black gram). By incorporating a Spatial Prior Module (SPM), their approach surpassed standard benchmark networks by over 10.5% in IoU while reducing inference times by approximately 93.6%, demonstrating that highparameter foundation models can be highly optimized for resource-constrained edge devices in real-time scouting.To solve the perennial problem of limited training data for rare or emerging crop diseases, Cooper et al. ( 2026) developed an ingenious synthetic data generation pipeline. Combining 3D procedural leaf modeling in Blender with diffusion-based disease synthesis (Stable Diffusion fine-tuned with LoRA and ControlNet), they synthesized highly accurate plant disease images with perfect groundtruth annotation masks. When deployed in low-resource data settings, combining these synthetic pipelines with restricted real-world datasets consistently drives significant improvements in downstream segmentation tasks. To tackle specific, complex pathologies, Xu, Chang, et al. (2025) developed the TSSC deep learning model, which embeds three-neighbor channel attention paired with a complementary squeeze-and-excitation mechanism. This specific architecture minimizes structural degradation risks while pushing classification accuracy to 99.61% for highly complex pea leaf pathologies. Similarly, Feng, Liu, et al. (2025) tackled overlapping leaf occlusions and small lesion footprints in citrus groves with YOLO-Citrus, an optimized framework integrating C3K2-STA, ADown modules, and a Wise-Inner-MPDIoU loss function to strike a balance between edge computational constraints and field deployment.UAVs and high-resolution satellite imagery have expanded the operational scale of phenotyping from individual pots to vast breeding blocks and commercial fields, allowing researchers to capture macro-dynamic parameters over time.In complex canopy systems that defy standard top-down aerial sensing, such as single-staked white Guinea yams, Iseki et al. (2026) demonstrated the distinct advantage of utilizing multi-angle (combined nadir and oblique) UAV imaging configurations. When coupled with support vector regression, this method captures complementary canopy-structure information to model shoot biomass trajectories (R 2 = 0.79) across multiple years and management zones. These nondestructive, time-series datasets enabled the fitting of genotype-specific Richard's growth curves using Bayesian inference, isolating valuable genetic variations in early growth allocation.To capture full-season vertical physiological changes over large scales, Li, Yue, and Luo (2025) developed a hybrid CNN-LSTM-Attention (CLA) model designed to estimate the full-period Leaf Area Index (LAI) in rice using multi-temporal UAV multispectral imagery. By using the CNN layer to extract instantaneous spatial features, the LSTM block to process seasonal time-series intervals, and a self-attention mechanism to weight critical growth transitions, their platform achieved a high coefficient of determination (R 2 = 0.92) and kept relative root mean square errors (RRMSE) below 9%. This network minimized soil background noise during early vegetative stages (LAI values 1-
Why it matches plant phenotyping methods植物フェノタイピングの技術動向を扱うEditorialであり、画像解析、UAVセンシング、深層学習、形質抽出などの方法が中心的に整理されている。
Three dimensional (3D) instance segmentation is essential for precision characterization of tree architecture at the branch level, which supports both tree fruit crop breeding and the development of robotic systems for orchard management. Existing methods usually use sparse convolution-based operation, which requires a coordinate quantization preprocess to generate sparse tensors, risking the loss of geometric details for fine-grained downstream phenotyping tasks. To overcome this challenge, we developed the dynamic sparse point-voxel transformer (DSPVFormer) model for the efficient and accurate 3D instance segmentation of high-resolution point clouds for dormant apple trees. Our hybrid DSPVFormer architecture maximizes the use of raw point features by dynamically mapping and aggregating the raw point features into the sparse voxel embeddings, capturing strong geometric features that may be discarded during quantization. Evaluations demonstrate that DSPVFormer achieved statistically significant improvements over baseline models on most instance segmentation metrics, which are further translated into more accurate phenotyping evaluation including branch counting and pruning map generation. These advances directly benefit downstream applications in plant phenotyping and robotic pruning for tree crops such as apples. Meanwhile, experimental results on phenotyping tasks suggested that phenotyping-specific evaluation metrics should be prioritized over upstream computer vision performance metrics to realize the full potential of high-throughput phenotyping for real-world applications.
Why it matches plant phenotyping methodsリンゴ樹の3D点群から枝レベル形質を抽出するセグメンテーション手法を開発・評価し、枝数や剪定マップへの応用まで検証しており、植物フェノタイピング手法が中心である。
abstractwe developed the dynamic sparse point-voxel transformer (DSPVFormer) model for the efficient and accurate 3D instance segmentation of high-resolution point clouds for dormant apple trees.
Reproduction assets foundThe paper states that its data and code (including the DSPVFormer analysis pipeline built on Plant Segmentation Studio) are publicly available at the authors' PSS GitHub repository. The COS dataset of 98 dormant apple tree point clouds is also referenced as accessible via this repository/statement. Other URLs (spconv,mCode · publicThe data and code are available at the PSS GitHub repository: https://github.com/perrydoremi/PlantSegStudio .Open asset ↗https://github.com/perrydoremi/PlantSegStudiolines:383-408Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
The canopy closure stage is a critical phase of rice ( Oryza sativa L.) development that influences canopy structure and final grain yield. Accurate and continuous monitoring of canopy closure dynamics is therefore essential for variety screening and cultivation optimization. This study combines unmanned aerial vehicle (UAV) remote sensing technology with deep learning-based semantic segmentation to establish an efficient framework for quantifying rice canopy closure dynamics. UAV RGB images were acquired for 198 hybrid rice varieties during early growth stages and used to build a canopy segmentation dataset. Three semantic segmentation models, i.e., DeepLabv3+, U-Net, and PSPNet, were systematically evaluated. Results show that DeepLabv3+ performed the best and enabled precise extraction of rice canopy features, obtaining a mean intersection over union (mIoU) of 0.86. Based on the extracted canopy coverage, the Gompertz model was utilized to characterize temporal canopy closure trajectories for all varieties, achieving an average R 2 of 0.978. Subsequently, five key dynamic indicators were derived, including canopy closure limit value ( K ), initial growth coefficient ( a ), growth rate coefficient ( b ), maximum instantaneous growth rate ( MGR ), and days to maximum growth rate ( Tm ). K-means clustering analysis was performed on these indicators to categorize all rice varieties into three clusters, disclosing pronounced differences in early-stage canopy development characteristics. Correlation analysis further demonstrated that canopy closure dynamics were closely associated with grain yield. Overall, while acknowledging the limitations of a single-season and single-site dataset, this study provides a scalable and objective framework for quantifying rice canopy closure dynamics, offering valuable support for variety selection, cultivation optimization, and high-yield rice production.
Why it matches plant phenotyping methodsUAV画像と深層学習セマンティックセグメンテーションを用いてイネのキャノピー閉鎖動態を定量化する手法を構築・評価しており、植物形質抽出が研究の中心である。
abstractThis study combines unmanned aerial vehicle (UAV) remote sensing technology with deep learning-based semantic segmentation to establish an efficient framework for quantifying rice canopy closure dynamics.
Maize leaf diseases in field environments often exhibit large variations in lesion scale, irregular morphology, blurred boundaries, and complex backgrounds. These factors pose challenges for existing detection models, particularly in detecting small lesions and achieving precise bounding-box localization. To address these issues, this study proposes LSL-YOLO11n, a maize leaf disease detection model based on the YOLO11n framework. The proposed model improves feature representation, localization quality modeling, and bounding-box regression to enhance disease detection performance under complex field conditions. Experiments were conducted on a dataset containing 15,119 images and 29,366 annotated instances across eight categories, including seven maize disease categories and healthy leaves. To evaluate the effectiveness of the proposed model, ablation experiments, comparative experiments with mainstream object detection models, and visual detection analyses were carried out. The ablation results show that the improved components contribute positively to the overall detection performance. LSL-YOLO11n achieves a Precision of 84.4%, Recall of 73.9%, and mean Average Precision (mAP) of 83.3%, which is 3.1 percentage points higher than that of the baseline YOLO11n model. Compared with YOLOv8n, YOLOv9t, YOLOv10n, and YOLOv12n, the proposed model improves mAP by 4.7, 3.3, 5.3, and 10.9 percentage points, respectively. The visual detection results further indicate that LSL-YOLO11n performs more stably in complex backgrounds and small-lesion scenarios. These findings provide technical support for rapid maize disease recognition and intelligent field monitoring.
Why it matches plant phenotyping methodsトウモロコシ葉の病斑・病害状態を画像から検出するYOLOベース手法を開発し、アブレーション比較や既存モデルとの性能評価を行っており、植物病害表現型の取得法が中心である。
abstractTo evaluate the effectiveness of the proposed model, ablation experiments, comparative experiments with mainstream object detection models, and visual detection analyses were carried out.
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 cross-sectional area profile. New shape descriptors based on cross-sectional area 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小麦穂の形態を3D画像取得と計算解析で定量化するパイプラインを開発し、形状記述子の抽出と遺伝子型間での検証を行う、植物フェノタイピング手法の中心的研究である。
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 paper's Data Availability and Code Availability sections point to the authors' public GitHub repository containing sample 3D spike data and the analysis code for the wheat spike morphology pipeline.Code · publicCode Availability
The codes are available at the following link: https://github.com/LatifaGreche/3D-WheatSpikeMorphologyExtractionOpen asset ↗LatifaGreche/3D-WheatSpikeMorphologyExtractionlines:316-410Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Accurate 3D plant models are crucial for computational phenotyping and physics-based simulation; however, current approaches face significant limitations. Learning-based reconstruction methods require extensive species-specific training data and lack editability for hypothesis-driven research. Procedural modeling offers parametric control and large model variability but demands specialized expertise in geometric modeling and an in-depth understanding of complex procedural rules, making it inaccessible to domain scientists. We present FloraForge, an LLM-assisted framework that enables domain experts to generate biologically accurate, fully parametric 3D plant models through iterative natural language Plant Refinements (PR) during template creation, minimizing the need for programming expertise. Our co-design workflow leverages LLM-assisted code generation to progressively refine Python scripts that generate parameterized complex plant geometries as Non-Uniform Rational B-Spline (NURBS) surface representations, with botanical constraints. Plant organs are represented as spline surfaces that can be easily tessellated into polygonal meshes with arbitrary precision, ensuring compatibility with functional structural plant analysis workflows such as light simulation, computational fluid dynamics, and finite element analysis. We demonstrate the framework by generating procedural models of multiple maize genotypes, soybean (which shares the procedural generator with mung bean), and mung bean, with one plant tracked across different developmental stages. We fit procedural models to empirical LiDAR and NeRF-derived point cloud data through manual refinement of the Plant Descriptor (PD), a human-readable YAML file originally templated by the LLM, obtaining consistently low mean symmetric Chamfer distances that indicate close agreement between generated models and measured plant geometry. The pipeline generates dual outputs: triangular meshes (represented as STL or OBJ files) for visualization and triangular meshes with additional parametric metadata for quantitative analysis (stored as SMESH files). We further illustrate analysis-ready use by coupling the procedurally generated models to the HELIOS framework to simulate diurnal photosynthetically active radiation interception in virtual maize and mung bean fields across growth stages. Our framework uniquely combines pre-trained LLM-assisted template creation, mathematically continuous representations that support both phenotyping and rendering, and direct parametric control through the PD. The framework makes sophisticated geometric modeling accessible to plant science researchers while maintaining mathematical rigor through biologically interpretable parameterizations; additionally, the iterative PR dialogue produces an explicit record of model properties that is typically absent in conventional procedural modeling pipelines.
Why it matches plant phenotyping methods植物の3D形状を生成・編集し、LiDAR/NeRF点群との適合で検証する、計算機フェノタイピング向けの中心的手法開発である。
abstractWe present FloraForge, an LLM-assisted framework that enables domain experts to generate biologically accurate, fully parametric 3D plant models
Determining the drivers of ecological stability amid accelerating global environmental change is a critical goal of contemporary ecology. Various candidate drivers have been suggested, with recent attention turning to response diversity—the variation among organism-environment responses. However, despite conceptual interest in response diversity as a driver of stability, there remain few field tests of this relationship. Using multi-species competitive communities of floating aquatic macrophytes as an experimental model for measuring temporal stability and response diversity to nutrient loading, we show that response diversity does not promote temporal stability of total macrophyte cover, but that communities with an uneven distribution of species responses were more resistant to an exogenous shock. To quantify macrophyte composition and growth dynamics from photographic time series of our experimental communities, we developed an open-source, scalable, machine learning workflow ( LeafMosaic ) capable of classifying four species from noisy field data including variable lighting, resolution, and plant morphology. We measured response diversity as the balance of positive and negative biomass growth responses to dissolved nitrate concentration, weighted by species’ relative contributions to biomass, and tested its effect on temporal stability and resistance to an unexpected pulse disturbance (a large typhoon that disrupted our outdoor mesocosms). Response imbalance predicted typhoon resistance, but species asynchrony and mean population stability best predicted community stability, with no direct or indirect effect of species responses. Overall, our results provide new experimental evidence for how the structure of species responses promotes stability, and we aim our LeafMosaic workflow to empower future field experiments using floating macrophytes to study response diversity and ecological stability.
Why it matches plant phenotyping methods浮遊水生植物の写真時系列から種組成と成長動態を抽出する、オープンソースでスケーラブルな機械学習ワークフローを開発しており、植物表現型取得・解析法が中心的です。
abstractTo quantify macrophyte composition and growth dynamics from photographic time series of our experimental communities, we developed an open-source, scalable, machine learning workflow ( LeafMosaic ) capable of classifying four species from noisy field data including variable lighting, resolution, and plant morphology.
Main conclusion A novel and efficient method was developed to accurately measure thickness in 3D of many cells from a confocal stack, as well as to track changes in cell thickness overtime. Plant cells and organs are three-dimensional objects with a certain thickness. Among basic geometric parameters (length, width, depth/thickness), cell thickness is less accurately and comprehensively measured, probably because it cannot be directly seen. The current methods of cell thickness quantification have some limitations, such as measuring only from a cross-section, not accounting for the directionality of biological thickness, or not offering a way to track changes in thickness of individual cells over time. This research is an attempt to bridge the gap, by making the quantification of thickness and tracking its changes in many cells easier and more accurate. We devised a novel method to efficiently measure average cell thickness in 3D from cells imaged with confocal microscopy, the most popular technique to image live samples over many days. The method, in combination with the popular software MorphoGraphX, also allows accurate and efficient tracking of changes in thickness between different time points. We tested the method on various organs of the model plant Arabidopsis thaliana such as the shoot apical meristem, the hypocotyl, the cotyledon, and the sepal. We demonstrated that this new method can reliably measure the thickness of hundreds of cells at once in a short amount of time to reveal new biological insights. We believe this would be a useful tool for plant researchers to accurately characterize this hidden morphological dimension.
Why it matches plant phenotyping methods植物細胞の3D厚みと経時変化を効率・高精度に測定する手法を開発し、複数器官で検証しているため、植物形態フェノタイピング手法が中心である。
abstractA novel and efficient method was developed to accurately measure thickness in 3D of many cells from a confocal stack, as well as to track changes in cell thickness overtime.
Soil salinization has become a critical factor limiting global agricultural production. Characterizing the growth and developmental responses of okra to salt stress and developing efficient and accurate salt-stress phenotyping techniques can provide an important methodological reference for okra cultivation in saline lands and future multi-cultivar salt-stress phenotyping studies. Traditional manual measurement of plant phenotypic parameters suffers from low efficiency and insufficient detection accuracy, making it difficult to achieve rapid and non-destructive analysis of plant phenotypic traits under salt stress. Therefore, this study proposes a computational phenotyping parameter extraction method based on the CSP-MSG Net model. Using dual-view feature fusion, we constructed a dedicated dataset. On the basis of PointNet++-MSG, the original MLP layers were replaced with C2F modules, and the SGE attention mechanism was integrated to enhance morphological feature extraction, thereby constructing a lightweight CSP-MSG Net architecture adapted to okra seedling point clouds for semantic segmentation of okra point clouds combined with DBSCAN clustering to complete instance segmentation, phenotypic parameters including plant height, stem diameter and canopy width were further calculated. This scheme enables high-throughput data acquisition, improves measurement accuracy, effectively reduces model parameters and computational overhead, and realizes lightweight operational performance. The results show that okra seedlings can still grow with increasing salt stress concentration, while the growth rates of the three measured traits are all inhibited, indicating that high-concentration salt stress impairs the growth activity of okra seedlings. To verify the calculation accuracy of the model, the phenotypic parameters predicted by the model were compared with manually measured values. The coefficients of determination for stem diameter, canopy width and plant height of okra seedlings reached 0.96, 0.99 and 0.99, respectively. These results strongly demonstrate the excellent reliability and effectiveness of the proposed method, providing methodological support for non-destructive and accurate phenotypic detection of okra seedlings under salt stress.
Why it matches plant phenotyping methodsオクラ幼苗の点群から草丈・茎径・樹冠幅を抽出する計算フェノタイピング手法を開発し、手動測定との比較で精度検証しているため、方法が研究の中心である。
abstractthis study proposes a computational phenotyping parameter extraction method based on the CSP-MSG Net model.
Abstract Rice ( Oryza sativa L.) tiller angle is an important trait that influences plant architecture, canopy light interception, and yield potential. In this study, we proposed a deep learning‐based pipeline for automated measurement of rice tiller angle and plant base width using unmanned aerial vehicle (UAV) imagery. Our method leverages keypoint detection models to estimate tiller angles efficiently and accurately. We collected and annotated a dataset of UAV‐captured rice plant images for tiller angle estimation. We demonstrated that our approach provides scalable and precise measurement for keypoints under real‐world field conditions, achieving a mean average precision (mAP)@50 of 0.982 and a mAP@50:95 of 0.859 on the test set. Predicted tiller angle distribution aligns well with human annotations, with a mean absolute error of 5.3° across a range of 10.7°–27.8° and a Pearson's correlation coefficient of 0.64, offering acceptable accuracy for tiller angle measurement in real‐world agricultural settings. Additionally, the predicted plant base width ranges from 2.8 to 5.5 cm, with a mean absolute error of 1.02 cm compared to human annotations, highlighting the model's capability for precise spatial analysis. Significant differences in tiller angle and plant base width were detected among 27 rice genotypes. These results validate the proposed pipeline's potential for accurate and efficient differentiation of plant architecture traits. This research is the first to measure the rice tiller angle directly from UAV images. It lays a foundation for automated phenotyping of plant architecture traits and has the potential for integration into plant phenotyping frameworks to further promote artificial intelligence‐driven rice research and production.
Why it matches plant phenotyping methodsUAV画像と深層学習により、イネの分げつ角度・株元幅という植物形態形質を自動推定する手法を開発・検証しており、フェノタイピング手法が研究の中心である。
abstractwe proposed a deep learning‐based pipeline for automated measurement of rice tiller angle and plant base width using unmanned aerial vehicle (UAV) imagery.
In this study, the diameter and height of Pinus brutia Ten. trees were measured using orthomosaic data obtained from unmanned aerial vehicle (UAV) imagery, and the stem volumes were estimated using machine learning (ML) techniques. The research was conducted in southwestern Türkiye within the brutian pine stands managed by the Isparta Regional Directorate of Forestry. A total of 175 trees were measured for height and diameter at breast height (d1.3), and these measurements used to estimate volume. The accuracy of these estimations predictions was examined, with volume estimation values serving as dependent variables in various ML algorithms. The performance of nine ML algorithms - AdaBoost Regression, Artificial Neural Network, Deep Neural Network, Decision Tree Regression, Gradient Boosting Regression, Linear Regression, Random Forest Regression, Support Vector Regression, and eXtreme Gradient Boosting Regression - were compared. The results indicated that using only the diameter values (max. correlation 0.984) produced better results than using only the height values (max. correlation 0.932), while combining diameter and height variables (max. correlation 0.987) produced the most accurate results. Among the all algorithms, Random Forest Regression achieved the highest average correlation (0.968), whereas Decision Tree Regression had the lowest (0.906). All algorithms produced correlations exceeding 0.90. These findings demonstrate that ML models can effectively estimate stem volume from UAV-derived diameter and height data under field conditions similar to those in southwestern Türkiye. The integration of remote sensing and ML may therefore offer a viable approach for stem volume estimation in structurally comparable forest environments.
Why it matches plant phenotyping methodsUAV画像から樹木の直径・樹高を取得し、機械学習で幹材積を推定する手法を比較評価しており、植物形質の取得・推定が研究の中心である。
abstractthe diameter and height of Pinus brutia Ten. trees were measured using orthomosaic data obtained from unmanned aerial vehicle (UAV) imagery, and the stem volumes were estimated using machine learning (ML) techniques.
Abstract Purpose Robotic harvesting systems are commonly evaluated in canopies treated as fixed operating environments, although kiwifruit canopy architecture is actively shaped by seasonal pruning, fruit thinning, tipping, and cane training. This study developed HarvestField, a decision-support framework for quantifying how robot-oriented canopy management affects harvesting feasibility before field operation. Methods HarvestField defines a spatial harvestability field \(\:\mathscr{H}\left(x\right)\in\:\left[\text{0,1}\right]\) as the product of reachability, visibility, clearance, and detachability sub-fields. A mass-weighted field average at fruit positions yields the Harvested Yield Index (HYI). The framework couples an L-system-based functional–structural plant model of Hayward kiwifruit with NSGA-III multi-objective optimisation over six management variables, jointly evaluating HYI, yield, and labour cost. Results Calibration against a published 12,000-fruit robotic kiwifruit harvesting benchmark produced HYI = 0.562, with an absolute deviation of 0.004 from the observed success rate of 0.558. HarvestField-optimised management increased mean HYI relative to standard management (0.642 versus 0.587). Under equal fruit counts, \(\:\mathscr{H}\)-guided thinning improved HYI by up to 0.301 compared with uniform thinning. Sobol analysis identified clearance neighbourhood radius as the dominant parameter (\(\:{S}_{T}=0.576\)). Conclusion HarvestField provides a spatially explicit and robot-specific framework for linking kiwifruit canopy management with harvesting feasibility. It supports management trade-off analysis while indicating the need for independent orchard-scale validation before deployment.
Why it matches plant phenotyping methodsキウイフルーツ樹冠の可視性・到達性・クリアランス・離脱性を統合して、果実位置ごとの収穫可能性を定量化する計算フレームワークが研究の中心であり、単なる収穫対象の検出を超えた植物構造由来の状態推定である。
abstractThis study developed HarvestField, a decision-support framework for quantifying how robot-oriented canopy management affects harvesting feasibility before field operation.
This study evaluates the performance of LiDAR sensing and UAV photogrammetry for three-dimensional canopy reconstruction and structural parameter estimation in precision agriculture under Mediterranean conditions. Experiments were conducted in Sicily, Italy, on Moringa oleifera Lam. and Ficus macrophylla subsp. columnaris, representing contrasting canopy architectures. LiDAR and UAV photogrammetric data were used to generate canopy models and estimate canopy height, canopy volume, and vegetation density distribution. A voxel-based approach was applied to LiDAR-derived point clouds to quantify internal canopy structure and vegetation density within the canopy volume. Accuracy was assessed by comparing remote sensing-derived canopy metrics with ground-truth field measurements. LiDAR outperformed UAV photogrammetry in canopy height estimation, achieving lower RMSE values than UAV-derived models (0.19–0.21 m vs. 0.52–0.60 m), corresponding to an approximate error reduction of 60–65%. LiDAR also provided more accurate canopy volume estimation, with lower relative errors than UAV photogrammetry (3.5–4.2% vs. 13.7–16.1%). The voxel-based LiDAR approach enabled the quantification of vegetation density distribution within the canopy volume, showing higher sensitivity to internal canopy layers compared with UAV photogrammetry, particularly in the structurally complex Ficus macrophylla canopy. UAV photogrammetry provided reliable estimates of the external canopy surface but underestimated structural parameters in dense vegetation due to canopy occlusion and limited penetration into inner canopy layers. Differences between the two methods were more pronounced in Ficus macrophylla than in Moringa oleifera, confirming the strong influence of canopy complexity on sensing performance. These findings demonstrate that LiDAR-derived structural and voxel-based metrics can improve canopy characterization and support precision agriculture applications such as biomass estimation, irrigation planning, yield prediction, and canopy management in Mediterranean cropping systems.
Why it matches plant phenotyping methodsLiDARとUAVフォトグラメトリによる植物キャノピーの3次元再構築・構造形質推定を比較検証し、地上計測との精度評価まで行っており、フェノタイピング手法が研究の中心である。
abstractThis study evaluates the performance of LiDAR sensing and UAV photogrammetry for three-dimensional canopy reconstruction and structural parameter estimation
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:388-431Plant phenotyping relevance match · UnverifiedbioRxiv · checked 11 Sept 2026
Cassava is a major staple crop in tropical regions, particularly in Sub-Saharan Africa, yet its productivity remains constrained by genetic and agronomic limitations. A major bottleneck in cassava breeding is the difficulty of accurately phenotyping agronomic traits under field conditions using conventional, labor-intensive methods. Here, we evaluated the potential of uncrewed aerial vehicle (UAV)-based phenotyping to quantify canopy growth traits and assess their genetic relevance under realistic field conditions. For this, multi-temporal UAV imagery was collected over two growing seasons (2018-2019 and 2019-2020) in a panel of 46 cassava genotypes planted in fields of the International Institute for Tropical Agriculture (IITA), Nigeria. Canopy height, canopy volume, and their relative growth rates (RGRh and RGRv) were extracted at the plot-level, and their seasonal dynamics and canopy-yield relationships were further assessed across developmental stages and environmental conditions. Repeatability (R) and broad-sense heritability (H2) were estimated using a linear mixed model (LMM) that partitioned genetic, genotype-by-year, and residual variance components, enabling the evaluation of both measurement reliability and genetic signal. Overall, UAV-derived growth dynamics were found to exhibit comparable patterns across genotypes, reflecting shared seasonal growth trajectories, while canopy-yield relationships varied with developmental stage and environmental conditions. In terms of genetic metrics, R was high for all UAV-derived traits (R = 0.68-0.69), indicating reliable genotype-level assessment across replicates and seasons. In contrast, H2 differed substantially among traits. Canopy volume (H2 = 0.64) and canopy height (H2 = 0.58) exhibited moderate-to-high heritability, reflecting strong genotype effects and comparatively moderate genotype-by-year interactions. However, their relative growth rates showed near-zero H2 values, driven primarily by genotype-by-year interaction, indicating a dominant environmental influence. These results demonstrate that UAV-derived canopy height and volume provide a consistent basis for genetic differentiation of cassava genotypes across environments, supporting their use in selection, whereas growth-rate traits are better suited for characterizing growth plasticity and genotype-by-environment interactions.
Why it matches plant phenotyping methodsUAV画像からキャッサバの樹冠高さ・体積・相対成長率を抽出し、再現性と遺伝率を検証することが研究の中心であり、実質的な植物フェノタイピング手法の評価に該当する。
abstractwe evaluated the potential of uncrewed aerial vehicle (UAV)-based phenotyping to quantify canopy growth traits and assess their genetic relevance under realistic field conditions.
Estimating the age and growth of long-lived desert succulents is challenging due to the absence of annual growth rings. This study utilizes Single-Image Photogrammetry (SIP) and a historical photograph taken by G. Sykes in 1965 and two replicated photographs obtained in 2016 and 2025 to analyze architectural changes over six decades in an iconic “Boojum tree” ( Fouquieria columnaris ) and an adjacent columnar cactus ( Pachycereus pringlei ) from a relictual population in Sonora, Mexico. Results indicate that F. columnaris exhibited a slow mean vertical growth rate of 1.71 cm yr −1 during the 1965-2016 interval which increased to 3 cm yr −1 for the recent 2016-2025 period. This acceleration aligned with values recorded at more favorable sites (i.e. 3.3 to 3.6 cm yr −1 ). Conversely, P. pringlei exhibited a minimal growth rate (1.01 cm yr −1 ), which is much lower than rates reported from repeat photography (5-10 cm yr −1 ) or radiocarbon dating of spines (3-23 cm yr −1 ). Together, these species demonstrate high ecological resilience near their physiological limits in the Sonoran Desert through divergent morphofunctional pathways. F. columnaris favors architectural stability and sustained growth, whereas P. pringlei relies on mechanical robustness and structural redundancy. These findings highlight the efficacy of SIP and historical records for long-term demographic monitoring in extreme arid environments.
Why it matches plant phenotyping methods単一画像フォトグラメトリを用いて植物の建築構造変化と成長速度を定量化することが研究の中心であり、植物表現型の実質的な測定法適用に該当する。
abstractThis study utilizes Single-Image Photogrammetry (SIP) and a historical photograph taken by G. Sykes in 1965 and two replicated photographs obtained in 2016 and 2025 to analyze architectural changes over six decades
Cold hardiness is a critical trait for grapevine survival and productivity in cold climates. This study examined the relationships among cane morphological characteristics, shoot color parameters, and cold hardiness in two grapevine cultivars ('Prairie Star' and 'Frontenac') across four dormant-season sampling times (ST 1-ST 4) and three internode diameter classes (small, normal, and large). Morphological traits, including internode length, shoot diameter, and cross-sectional area, did not show a consistent temporal trend across sampling periods, suggesting that the observed variation was primarily associated with sampling time and cane class rather than progressive structural change during dormancy. In contrast, colorimetric traits showed a clear seasonal pattern, with shoots becoming darker and redder from ST 1 to ST 4, consistent with advancing lignification and cane maturation. Cold hardiness, assessed using low-temperature exotherms of bud, phloem, and xylem tissues, increased substantially from early to mid-dormancy, with xylem tissues reaching the greatest freezing tolerance by ST 3-ST 4. 'Prairie Star' showed slightly greater xylem cold hardiness than 'Frontenac', while bud survival remained consistently high across all treatments. Strong associations between shoot color and LTE values indicate that color traits, particularly at the fifth internode, may serve as reliable non-destructive indicators of cold hardiness status. Sampling time was the primary source of multivariate variation, with cultivar and internode class contributing secondary effects. These findings demonstrate that observable cane traits, especially shoot color, reflect the progression of seasonal cold acclimation and may support the evaluation and selection of cold-hardy grapevine germplasm.
Why it matches plant phenotyping methods枝の色・形態を用いてブドウの耐寒性を非破壊推定する指標として評価しており、単なる生物学的測定ではなく表現型取得法の妥当性評価が中心です。
abstractStrong associations between shoot color and LTE values indicate that color traits, particularly at the fifth internode, may serve as reliable non-destructive indicators of cold hardiness status.
BarleyCommon beanCowpeaGrowth chamberMesh / voxelLiDAR / point cloudMultispectral / hyperspectralLeafWhole plant / canopy / plot / fieldAnnotation / quality control
Abstract High-throughput 3D multispectral plant phenotyping platforms generate large volumes of point cloud files, but trait extraction is typically performed by sensor-bundled software whose internal algorithms are not publicly documented, which limits reproducibility and integration into custom research pipelines. Here we present PhytoScan3D, an open-source Python pipeline that extracts morphological and spectral phenotypic traits, spanning plant height, 3D leaf area, digital biomass, convex hull volume, leaf inclination, canopy geometry, NDVI, hue, and vegetation indices, from both PLY and PCD point cloud files generated by Phenospex PlantEye F500 and F600 sensors, and is portable to point clouds from any acquisition platform. PhytoScan3D was validated against HortControl (PhenoSpex) ground-truth measurements on 936 barley ( Hordeum vulgare ) pot-date observations from the growth chamber trial (20 Norwegian cultivars, 12 scan dates, Septemenr 2025 to January 2026), achieving Pearson r = 0.913 to 0.999 and ratio approximately 1.000 for Plant Height Max, 3D Leaf Area, and NDVI Average. A vectorised mesh face filtering implementation achieved a 120x speed improvement, increasing valid 3D Leaf Area coverage from 0.6% to 100% of files. Cross-format validation on 223 PlantEye F600 PCD files from the ICRISAT LeasyScan platform (four legume species: mungbean, cowpea, lima bean, and common bean; 1,523 plant observations) yielded r = 0.884 against independent cuboid annotation heights. The systematic positive bias (mean +27.2 mm, ratio = 1.44) is attributable to PhytoScan3D computing height from raw point cloud Z-range while cuboid annotations are fitted to segmented plant points only, with the offset consistent across all four species (per-species r = 0.880 to 0.888). Cross-dataset processing of 1,180 PLY files from the Crops3D benchmark (8 species, 3 acquisition methods) confirmed zero extraction errors. PhytoScan3D is available at “github.com/kovimallik/phytoscan3d” under the MIT licence and processes 1,651 files across three independent datasets in under 12 minutes on GPU hardware. Highlights PhytoScan3D is the first open-source Python pipeline for batch extraction of phenotypic traits, including plant height, 3D leaf area, digital biomass, convex hull volume, leaf inclination, NDVI, and excess green index, from both PLY and PCD point cloud files generated by Phenospex PlantEye sensors. Primary validation against HortControl ground-truth measurements on 936 barley pot-date observations achieved Pearson r = 0.913-0.999 for Plant Height Max, 3D Leaf Area, and NDVI Average. A 120x computational speedup in mesh face filtering (vectorised NumPy vs. set-based loop) increased the coverage of valid 3D Leaf Area extraction from 0.6% to 100% of files. Cross-format validation on 223 PlantEye F600 PCD files from ICRISAT LeasyScan (four legume species, 1,523 plants) achieved r = 0.884 against independent cuboid annotation heights. The systematic +27.2 mm bias reflects a methodological difference (raw Z-range vs. soil-segmented annotations), is consistent and predictable across all four species (per-species r = 0.880-0.888), and is correctable by a single linear factor. Cross-dataset processing of 1,180 PLY files from the Crops3D benchmark (8 species, 3 acquisition methods) confirmed zero extraction errors. Significant scan-unit variation was detected for Plant Height Max (F = 5.71, p < 0.001, η 2 = 0.138) and Canopy Width X (F = 6.32, p < 0.001, η 2 = 0.150), demonstrating the biological utility of extracted traits.
Why it matches plant phenotyping methods植物の3D点群・マルチスペクトルデータから形態・スペクトル形質を抽出するオープンソース手法を開発し、複数データセットで技術検証・ベンチマークしているため、植物フェノタイピング手法が中心である。
abstractHere we present PhytoScan3D, an open-source Python pipeline that extracts morphological and spectral phenotypic traits
Reproduction assets foundThe paper's own analysis code (PhytoScan3D pipeline) is publicly released on GitHub under the MIT licence, and the two external 3D point cloud datasets used for validation (Crops3D and ICRISAT LeasyScan) are publicly available on figshare. The primary barley PLY dataset is not yet public (to be deposited in NVA upon).Code · publicditing, Funding acquisition.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal
relationships that could have appeared to influence the work reported in this paper.
Data Availability
PhytoScan3D source code, documentation, and example datasets are available at
https://github.com/kovimallik/phytoscan3d under the MIT licence. The barley PLY dataset
will be deposited in the Norwegian Research Information Repository (NVA) upon acceptance.
The Crops3D benchmark dataset is publicly available at
https://doi.org/10.6084/m9.figshare.27313272 (Zhu et al. 2024). The ICRISAT LeasyScan
dataset is publicly available at https://doi.org/10Open asset ↗github.com/kovimallik/phytoscan3dpdf-raw-page:15 lines:1-36Dataset · publicData Availability
PhytoScan3D source code, documentation, and example datasets are available at
https://github.com/kovimallik/phytoscan3d under the MIT licence. The barley PLY dataset
will be deposited in the Norwegian Research Information Repository (NVA) upon acceptance.
The Crops3D benchmark dataset is publicly available at
https://doi.org/10.6084/m9.figshare.27313272 (Zhu et al. 2024). The ICRISAT LeasyScan
dataset is publicly available at https://doi.org/10.6084/m9.figshare.28270742 (Galba et al.
2025).
Acknowledgements
This work was supported by the PheNo, DLT-Farming and Soil2Milk from Research Council
of Norway and TWIN-NUE from Norwegian University of Life Sciences (NMBU). The
authoOpen asset ↗figshare · 10.6084/m9.figshare.27313272pdf-raw-page:15 lines:1-36Dataset · publicimallik/phytoscan3d under the MIT licence. The barley PLY dataset
will be deposited in the Norwegian Research Information Repository (NVA) upon acceptance.
The Crops3D benchmark dataset is publicly available at
https://doi.org/10.6084/m9.figshare.27313272 (Zhu et al. 2024). The ICRISAT LeasyScan
dataset is publicly available at https://doi.org/10.6084/m9.figshare.28270742 (Galba et al.
2025).
Acknowledgements
This work was supported by the PheNo, DLT-Farming and Soil2Milk from Research Council
of Norway and TWIN-NUE from Norwegian University of Life Sciences (NMBU). The
authors thank Sara Catarina Costa Laranjeira, Min Lin and other NMBU growth facility staff
for plant care and scanning operOpen asset ↗figshare · 10.6084/m9.figshare.28270742pdf-raw-page:15 lines:1-36Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 6 Sept 2026
Abstract Automated three-dimensional reconstruction of plant architecture under- pins high-throughput crop phenotyping, yet its deployment in practical field settings is constrained by two fundamental limitations of Neural Radiance Fields (NeRF): degraded geometry under sparse, unstructured image capture, and a complete absence of calibrated uncertainty estimates that would allow practitioners to distinguish reliable geometry from reconstruction artefacts. We present UA-PlantNeRF, a unified framework that resolves both limita- tions through three tightly coupled contributions. First, building on VISAR, our prior intelligent video frame-selection strategy (weights α1 = 0.4, α2 = 0.3, α3 = 0.3), the pipeline identifies maximally informative, non-redundant view- points from raw footage with as few as 15 frames. Second, a dual-head NeRF architecture augmented with Monte Carlo Dropout produces jointly decom- posed aleatoric and epistemic uncertainty alongside each reconstructed voxel, trained under a heteroscedastic negative log-likelihood objective. Third, split conformal prediction—with calibration performed on held-out plants to preserve exchangeability— yields provable, distribution-free per-ray cov- erage guarantees at any user-specified confidence level. Evaluated across three benchmarks (Pheno4D, ROSE-X, and our novel UA-Field greenhouse dataset) at sparsity levels N ∈ {15, 30, 50}, UA-PlantNeRF achieves PSNR 28.7±0.6 dB and SSIM 0.89±0.01 at N = 30, outperforming all sparse-view baselines (p
Why it matches plant phenotyping methods植物の3D構造・アーキテクチャを推定する画像ベースのNeRF手法を開発し、複数ベンチマークで評価しているため、植物表現型計測法が中心です。
abstractEvaluated across three benchmarks (Pheno4D, ROSE-X, and our novel UA-Field greenhouse dataset)
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
High-fidelity 3D reconstruction and precise phenotypic parameter extraction of banana plants are critical for crop growth monitoring and yield estimation in precision agriculture. However, traditional methods encounter significant bottlenecks: LiDAR systems are cost-prohibitive for widespread adoption, while traditional photogrammetry often fails to handle the complex canopy structures, severe occlusions, and weak texture features characteristic of banana leaves. To address these limitations, this article proposes a novel framework for 3D reconstruction and automatic phenotyping based on multi-view images captured by mobile phones. We introduce BN-NeRF, an enhanced Neural Radiance Field method built upon Instant-NGP. Specifically, we integrate three key technical improvements: (1) frame-level geometric calibration to correct camera pose drift caused by handheld motion; (2) sparse geometric anchoring to explicitly constrain depth and scale using sparse point clouds; and (3) thin-leaf prior regularization to suppress artifacts and improve the geometric accuracy of leaf surfaces. Building on this reconstruction, we establish a complete pipeline to recover explicit metric geometry from implicit radiance fields. By combining mesh topological analysis with geodesic algorithms, we achieve automated and precise extraction of key morphological parameters. Extensive experiments were conducted on a dataset of 90 banana plants in a real-world orchard. The results demonstrate that BN-NeRF achieves superior rendering quality (PSNR of 32.4 dB, SSIM of 0.951, and LPIPS of 0.152) while maintaining inference speeds comparable to Instant-NGP. Furthermore, the extracted phenotypic parameters showed strong agreement with manual ground truth across both leaf-level and structural traits. In addition to trait-specific regression performance, the evaluation also includes normalized completeness analysis, calibration-cube-based scale validation, and Bland-Altman agreement analysis, supporting the measurement reliability of BN-NeRF for field phenotyping. This study demonstrates that low-cost smartphone-based acquisition, combined with BN-NeRF, can support accurate field phenotyping of banana plants. In addition, an implemented mobile-cloud system was functionally validated through repeated end-to-end runs on an iPhone 13 client and a cloud workstation.
Why it matches plant phenotyping methodsスマートフォン画像からの3D再構成と植物形態形質抽出を中核とするBN-NeRF手法を開発し、圃場データで精度・再現性を検証しているため。
abstractthis article proposes a novel framework for 3D reconstruction and automatic phenotyping based on multi-view images captured by mobile phones
A deeper understanding of circadian rhythms in plants, especially trees, is crucial for uncovering how structural and physiological processes align with daily environmental cycles. However, most studies analyze biochemical changes and positional variations separately, with limited exploration of their coordination within the whole-plant system. Hyperspectral light detection and ranging (HSL) integrates three-dimensional (3D) structural mapping with hyperspectral reflectance, enabling non-destructive assessment of plant biochemistry. Previous work showed that HSL can detect nocturnal vertical canopy displacements with centimeter accuracy, but organ-level rhythmic patterns (e.g., branches vs. leaves) remain poorly studied. Here, we developed a hyperspectral point cloud classification method combining spectral and spatial data to separate branches from leaves and analyze their sleep movements independently. A novel sinusoidal-polynomial fitting model was then proposed to characterize circadian rhythms in both sleep movements and reflectance variations. We applied HSL data from a single birch tree (Betula pendula) collected at 30 distinct HSL measurement times over 24 h to develop a 3D canopy partitioning approach that divides the canopy into nine grids (3 × 3) and ten vertical layers per grid. Results revealed near-24-hour rhythmic patterns (max R² = 0.5841, P < 0.05) and stratified sleep movements: branches exhibited larger amplitudes than the corresponding canopy layers, with the overall maximum movement amplitude occurring shortly before sunrise (04:00-06:30) and recovering after sunrise. The model also effectively characterized diurnal reflectance variations (max R² = 0.5812, P < 0.05). In addition, chlorophyll-related spectral indices exhibited a sinusoidal variation, reaching a minimum around 03:00. These findings highlight the potential of HSL for the joint analysis of structural and biochemical circadian rhythms, providing a non-destructive approach to investigate plant rhythmicity in both structural and physiological domains.
Why it matches plant phenotyping methods hyperspectral LiDARによる植物の構造・生理形質の取得と、葉・枝の分類および概日リズム推定モデルの開発が研究の中心である。
abstractHere, we developed a hyperspectral point cloud classification method combining spectral and spatial data to separate branches from leaves and analyze their sleep movements independently.
Field / plotNeRF / 3D Gaussian SplattingPhotogrammetry / SfM / MVSLiDAR / point cloudStem / branchMorphology / geometry measurement2D/3D reconstructionArchitecture / morphology / geometry
Accurate characterization of tree stem geometry is essential for forest inventories, yet conventional field measurements of diameter at breast height (DBH) are limited to a single cross-section and do not capture vertical variability along the trunk. This study compares five approaches for stem characterization in a Mediterranean forest: mobile laser scanning (MLS), consumer-grade iPad-LiDAR, Structure from Motion (SfM) photogrammetry, Gaussian Splatting (GS), and manual field measurements. Data were acquired simultaneously within a 2.5 m radial plot. DBH was estimated through RANSAC-based circular fitting, and stem sections were extracted every 20 cm to assess diameter stability along the trunk. All techniques produced similar mean DBH values closely matching field measurements (23 cm), with MLS achieving the lowest RMSE (1.29 cm), followed by SfM (1.52 cm), GS (1.60 cm), and iPad-LiDAR (2.26 cm). However, marked differences were observed in vertical completeness. MLS captured the full vertical profile of the stems, reaching 14.11 m, whereas SfM and GS from iPhone, and iPad-LiDAR were limited to approximately 6 m or less. The results indicate that although low-cost image-based approaches can provide accurate DBH estimates under controlled conditions, MLS remains the most robust solution for comprehensive vertical stem characterization.
Why it matches plant phenotyping methods森林樹幹のDBHと垂直方向の形状を、複数の3Dセンシング手法で推定・比較し、RMSEや垂直完全性を評価している。植物形状計測法の技術比較・検証が中心である。
abstractThis study compares five approaches for stem characterization in a Mediterranean forest: mobile laser scanning (MLS), consumer-grade iPad-LiDAR, Structure from Motion (SfM) photogrammetry, Gaussian Splatting (GS), and manual field measurements.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Accurate and standardized phenotyping of complex, environmentally sensitive quantitative traits remains a major bottleneck for reliable locus discovery and breeding applications. Here, we established a skeleton-guided 3D digital phenotyping framework that generates standardized digital replicas and enables precise quantification of fruit and plant architecture traits in cucumber. The workflow was applied to a permanent recombinant inbred line (RIL) population (n = 211) evaluated across two seasons (2023-2024), from which nine traits were extracted from 3D models. All 211 RILs were whole-genome resequenced to generate genome-wide SNPs, enabling construction of a high-density linkage map and subsequent QTL mapping, complemented by GWAS for physical anchoring of association signals. Using this integrated design, we identified 29 QTLs across the nine traits and resolved cross-season major-effect loci with consistent genetic signals. Notably, two cross-season loci were detected as novel: FL4.1/FSL4.1 affecting fruit length and fruit stalk length, and NLB1.1/LLB1.1 affecting branching. GWAS further anchored lead variants to physical coordinates and supported cross-season associations. Together, these results demonstrate that standardized 3D phenotyping provides a reproducible and interoperable trait definition framework that supports cross-season locus discovery and downstream marker development for quantitative genetic dissection in cucumber.
Why it matches plant phenotyping methodsキュウリの果実・植物体形態を3Dモデルから定量化する標準化フェノタイピング手法の構築と応用が研究の中心であり、再現可能な形質抽出枠組みとして評価されている。
abstractwe established a skeleton-guided 3D digital phenotyping framework that generates standardized digital replicas and enables precise quantification of fruit and plant architecture traits in cucumber.
Accurate identification of tomato lateral shoots is essential for automated pruning and plant monitoring in greenhouse production. However, complex illumination, leaf occlusion, and morphological variability often reduce detection reliability in optical vision systems. This study proposes an optical vision-based framework that integrates deep learning perception with large language model assisted pruning decision support. A tomato lateral Shoot image dataset was constructed using RGB imaging in greenhouse environments. A lightweight YOLOv8n instance segmentation model with the Convolutional Block Attention Module (CBAM) was developed to enhance feature representation. Data augmentation strategies were applied to simulate illumination variations and improve model robustness. Model interpretability was analyzed using Principal Component Analysis (PCA) and Gradient weighted Class Activation Mapping (Grad CAM). Experimental results show that the proposed YOLOv8n-seg+CBAM model achieves a mAP 0.5 of 98.1% with only 3.28M parameters and an average inference time of 8.0 ms per image. Monte Carlo Dropout was further introduced to estimate the spatial uncertainty of cutting points. These structured perception features were provided to a large language model (LLM), enabling context aware pruning decision assistance. The proposed framework integrates vision-based shoot detection, uncertainty estimation, and LLM-assisted reasoning into a unified pipeline, enabling more reliable pruning decisions and improving safety and robustness compared with vision-only approaches in greenhouse environments.
Why it matches plant phenotyping methodsトマト側枝をRGB画像から検出・セグメンテーションし、不確実性推定まで行う画像ベースの植物形態計測手法を開発しており、方法論が中心である。
abstractThis study proposes an optical vision-based framework that integrates deep learning perception with large language model assisted pruning decision support.
Interest in light detection and ranging (LiDAR) for the precise monitoring of vegetative growth of grain crops has increased. The study was conducted to estimate wheat size and plant distance using LiDAR and the convex hull method (CHM) compared to the voxel grid method (VGM). A commercial LiDAR system was used for data collection in the middle and late growth stages using static and dynamic scanning. A small number (ten) of data frames, consisting of a region of interest (ROI) of 1 m × 0.9 m for each frame, were selected as data samples. The data processing workflow consisted of data conversion, targeted data frame selection, visualization, region of interest (ROI) segmentation, outlier and untargeted point removal, downsampling, denoising, voxelization, preparation of the convex hull, and 3D PCD density map. To estimate the plant size and distance of wheat, the results obtained using CHM and VGM were compared with measured data results, and both methods were applied for the middle and late growth stages of wheat. The relative accuracy of LiDAR-estimated plant height, canopy volume, plant spacing, and row distances with respect to the measured results were 94%, 87%, 94%, and 87%, respectively, using CHM, and 76%, 72%, 62%, and 71% by VGM for static data scanning; for dynamic scanning, the estimated relative accuracy percentages were 87%, 91%, 94%, and 93%, respectively, using CHM, and 77%, 74%, 75%, and 74%, respectively, using VGM. The same methods were applied to the late growth stage data sets. Between the two methods, CHM provided higher accuracy for static and dynamic data-scanning approaches in the middle and late growth stages because the complex geometry of plants, thin and sparse leaf area, and structure complicated voxelization. Despite several challenges in PCD collection and processing, this study supports size and distance estimation for wheat and similar grains as non-destructive methods.
Why it matches plant phenotyping methodsLiDARと3D点群処理によりコムギの草丈、キャノピー体積、株間・畝間距離を推定し、凸包法とボクセル法を実測値と比較検証している。表現型取得・抽出手法が研究の中心である。
abstractThe study was conducted to estimate wheat size and plant distance using LiDAR and the convex hull method (CHM) compared to the voxel grid method (VGM).
High-quality, real-time three-dimensional (3D) reconstruction and phenotyping of field crops are essential for advancing digital farm management. However, challenges such as diverse crop postures and fluctuating illumination in open-field environments significantly hinder reconstruction accuracy. To address these issues, this study proposes AgriGaussian, a novel 3D phenotyping method based on 3D Gaussian Splatting (3DGS), designed to enable low-cost, high-fidelity reconstruction and phenotypic analysis of agricultural fields, with chili pepper plants as an example. Specifically, a dataset encompassing three distinct growth stages of chili pepper plants was collected and preprocessed using a phenotyping robot (PR) equipped with oblique-view imaging. The AgriGaussian method integrates three core components: (1) a Depth-Supervised Strategy (DSS) for adaptively enhancing phenotypic features based on gradient variations; (2) a Crop Appearance Extraction (CAE) module that suppresses the effects of illumination and environmental interference while preserving fine phenotypic details; and (3) a Multi-scale Chunking Training (MCT) strategy that enables scalable reconstruction of large agricultural scenes. Experimental results demonstrate that AgriGaussian achieves high-fidelity reconstruction, with peak signal-to-noise ratio (PSNR) of 26.85 dB, structural similarity index (SSIM) of 0.86, and learned perceptual image patch similarity (LPIPS) of 0.24—surpassing existing baseline algorithms. Compared to manual measurements, the reconstructed canopy height and volume exhibit strong agreement, with coefficients of determination (R2) reaching 0.90 and 0.86, respectively, and average absolute percentage errors as low as 5.1%. Furthermore, the method accurately reconstructs fine-grained features such as foliar lesions and surface textures. In summary, AgriGaussian enables large-scale, high-fidelity reconstruction of field crop phenotypes and provides a promising foundation for advancing 3D phenotyping and digital twin technologies in precision agriculture.
Why it matches plant phenotyping methods植物の3D形態・構造を抽出する低コスト画像ベース表現型解析法を開発し、手動測定との一致性も検証しているため、表現型取得法が研究の中心である。
abstractthis study proposes AgriGaussian, a novel 3D phenotyping method based on 3D Gaussian Splatting (3DGS)
This study suggests a novel extraction pipeline based on terrestrial laser scanning across multiple growth stages to address the current deficiency of three-dimensional (3D) phenotypic traits for wheat populations derived from 3D point clouds. This study presents 3D Wheat Point-seg Net (3D WP-seg Net), a novel 3D point cloud segmentation network that incorporates an SA-CrossAttention module to address the difficulties presented by complex structures, background noise, non-uniform point distributions, and scale variations in plot-level wheat point cloud data. Plot height, canopy area, and volume are examples of common phenotypic parameters that are successfully extracted using this technique. Additionally, two new phenotypic parameters: plot extension distance and lodging angle are suggested by fusing the centroid and slice-skeletonization algorithms. A software platform called 3D Trait Analysis was created to facilitate multi-sensor 3D data processing and trait extraction. A genome-wide association study (GWAS) was then conducted using the extracted population-level traits to find potential genes linked to these new phenotypes. While the segmentation accuracies of 3D WP-seg Net achieved 93.1%, 88.3%, and 92.5% under various sensor systems, the results showed a strong correlation between the predicted and measured plot heights (R 2 = 0.954). Furthermore, four candidate genes linked to extension distance were found on chromosomes 1A, 2A, and 4A, and five putative genes controlling plot lodging angle were found on chromosomes 2D, 3A, and 7A. The multi-stage 3D phenotyping and analysis framework for wheat populations established by this study improves the accuracy of point cloud segmentation and trait quantification while offering a new and efficient method for the genetic analysis of important population-level traits.
Why it matches plant phenotyping methodsLiDAR点群の分割、3D形質抽出、検証、ソフトウェア基盤の開発が研究の中心であり、コムギの形態・倒伏関連形質を定量化しているため。
abstractThis study presents 3D Wheat Point-seg Net (3D WP-seg Net), a novel 3D point cloud segmentation network
Reproduction assets foundThe paper's data availability statement explicitly deposits the authors' source code (3D WP-seg Net segmentation pipeline and 3D Trait Analysis software), testing data, and supporting datasets in a public GitHub repository, directly supporting this paper's wheat 3D phenotyping and segmentation analysis.Code · publicThe source code, testing data, and other datasets supporting the results presented here are available at https://github.com/AI-PhenoLab/3D-WP-seg-Net .Open asset ↗AI-PhenoLab/3D-WP-seg-Netlines:511-575Plant phenotyping relevance match · UnverifiedOpenAlex · checked 13 Sept 2026
Because conventional vegetative propagation methods for Kiwifruit (Actinidia deliciosa (A.Chev.) C.F.Liang & A.R.Ferguson) often are constrained by their need for extensive plantation areas, high labour inputs, and intensive weed management. Therefore, in vitro micropropagation has emerged as an effective approach for the large-scale production of uniform, disease-free kiwifruit plant material. In the present study the shoot growth dynamics and spatial competition between explants for kiwifruit (cv. Hayward) in vitro-grown were investigated considering different explant densities (3, 5, and 7) and two subculture durations (30 and 45 days). Growth performance was assessed integrating traditional measurements (shoot viability, number and length, callus formation, fresh and dry biomass) with high-resolution three-dimensional photogrammetric reconstruction. Image acquisition was performed using a smartphone-based system (iPhone+viDoc RTK rover), and dense point clouds were generated through the Structure from Motion photogrammetric processing, providing the basis for quantitative geometric analysis. Consistent correlations were observed between manually measured growth traits and smartphone-derived morphometric parameters at both 30 and 45 days of subculture. Specifically, point cloud–based estimates of surface area, height, and volume were significantly associated with shoot number, shoot length, and biomass accumulation, supporting the reliability of 3D photogrammetry as a non-destructive tool for phenotyping in vitro kiwifruit growth. The proposed approach demonstrates the potential of 3D photogrammetry to enhance the objectivity, resolution, and repeatability of growth assessment in in vitro culture systems, offering new insights into shoot development and density-dependent interactions. Graphical Abstract
Why it matches plant phenotyping methodsスマートフォン撮影とSfMによる3Dフォトグラメトリで、キウイフルーツ苗条の表面積・高さ・体積を非破壊推定し、手動測定およびバイオマスとの相関で信頼性を検証しており、表現型取得手法が中心である。
abstractdense point clouds were generated through the Structure from Motion photogrammetric processing, providing the basis for quantitative geometric analysis.
Three-dimensional (3D) point-cloud phenotyping enables non-destructive and repeatable characterization of plant architecture, supporting the measurement of traits such as internode length, branching topology, and organ orientation. This article presents TomatoPGT (Tomato Plant Graph Twin) , a 3D tomato dataset designed for research on semantic/instance segmentation, graph-based structural representation, and graph-derived phenotypic trait extraction. The dataset contains 42 scans from three greenhouse-grown tomato plants acquired across early to mid-vegetative development using a rotational multi-view imaging system. Each scan consists of 60-70 overlapping RGB images captured under uniform illumination and reconstructed into a metrically scaled dense colored point cloud using Structure-from-Motion and multi-view stereo. TomatoPGT provides: (i) multi-view RGB images, (ii) dense colored point clouds, (iii) manually curated semantic and instance annotations at organ level, (iv) graph representations encoding plant topology and geometry, and (v) tabulated phenotypic traits computed deterministically from the graphs (internode length, insertion angles, and phyllotactic angles). TomatoPGT supports reproducible development and evaluation of 3D phenotyping pipelines, including learning-based segmentation and graph-based modeling of plant architecture.
Why it matches plant phenotyping methods植物の3D形態表現型抽出を目的としたデータセットで、画像・点群・器官アノテーション・グラフ・形質値を提供し、再現可能なフェノタイピング手法の開発と評価を直接支援している。
abstractThis article presents TomatoPGT (Tomato Plant Graph Twin) , a 3D tomato dataset designed for research on semantic/instance segmentation, graph-based structural representation, and graph-derived phenotypic trait extraction.
Reproduction assets foundThe paper's own TomatoPGT dataset (multi-view RGB images, dense point clouds, semantic/instance annotations, graph representations, and CSV phenotypic traits) is publicly deposited on Mendeley Data, and the authors' Cloud-Seg/Cloud-Graph software tools plus supplementary materials (camera calibrations, example datasetsDataset · publicRepository name 1: Mendeley[2].
Data identification number: DOI: 10.17632/72md54c7n7.1
Direct URL to data: https://data.mendeley.com/datasets/72md54c7n7/1Open asset ↗Mendeley · 10.17632/72md54c7n7.1html-lines:105-178Code · public6. Code and documentation: CloudSeg and CloudGraph software tools, environment specifications, and example usage instructions are hosted on Zenodo[3].Open asset ↗Zenodohtml-lines:264-308Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
P henotyping remains a critical bottleneck in sorghum ( Sorghum bicolor L. Moench) breeding programs, limiting rates of genetic gain due to labor-intensive yield estimation methods. To address this concern, this study investigates the potential of integrating remote sensing data with machine learning (ML) and deep learning (DL) models to improve sorghum grain yield predictions. Unmanned aircraft systems (UAS)-based imagery was collected across multiple field trials, extracting standard vegetation indices, canopy height features, and panicle traits using a YOLOv11-based object detection model, "YOLO-SORG." Six ML models-including ridge regression (RR), elastic net (EN), LASSO regression (LR), support vector regression (SVR), random forest (RF), and XGBoost (XGB)-were trained to predict plot-level yield using three distinct feature sets: panicle traits, canopy traits, and a combination of both. Results indicate that models relying solely or partially on canopy-derived features provided the most consistent and accurate yield estimates (R 2 ≈ 0.74-0.76), whereas models relying solely on panicle traits performed poorly (R 2 ≈ 0.28-0.42), indicating nadir-derived panicle metrics were potentially being indirectly captured with the canopy traits. Traditional regression models outperformed tree-based ensemble methods in variance partitioning and repeatability ( R ≈ 0.59-0.60), making them more suitable for many breeding applications. These findings highlight the promise of UAS-driven ML pipelines for non-destructive yield prediction but underscore potential limitations of nadir imagery for capturing panicle morphology and use in a robust yield prediction model. Future research should explore the inclusion of multi-temporal imaging, refined feature extraction approaches, and use of oblique, non-nadir imagery to enhance predictive accuracy in sorghum breeding programs.
Why it matches plant phenotyping methodsUAS画像からキャノピー高、穂形質、植生指数を抽出し、機械学習でソルガムのプロット収量を推定するパイプラインが研究の中心であり、形質取得・推定手法の評価も行っている。
abstractthis study investigates the potential of integrating remote sensing data with machine learning (ML) and deep learning (DL) models to improve sorghum grain yield predictions.
Reproduction assets foundThe authors explicitly state that the tabular data and code used in this sorghum yield prediction study are publicly available in their GitHub repository, which is a paper-specific asset containing the analysis code and phenotype data.Code · publicThe tabular data and code used in this study can be found in the following GitHub repository: https://github.com/AcePugh/Sorghum_Yield_Prediction_2025/Open asset ↗AcePugh/Sorghum_Yield_Prediction_2025lines:137-139Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
Predicting canopy traits non-destructively is important for understanding crop growth and improving phenotyping efficiency. Hyperspectral reflectance provides detailed spectral information, but the role of band selection in regression-based trait prediction at the canopy scale remains unclear. In this study, we evaluated the effects of different band-selection algorithms on the prediction accuracy of aboveground biomass (AGB), leaf area index (LAI), and canopy cover (CC) in soybeans across multiple sites, years, cultivars, and irrigation treatments. We compared a full-band partial least squares regression (PLS) model with three band-selection methods (PLS-Variable Importance in Projection (VIP), Bootstrapped least absolute shrinkage and selection operator (LASSO) (BoLASSO), and an ensemble approach), and model performance was assessed using independent validation datasets. The results showed that the effectiveness of band selection depended on the target trait. Full-band PLS provided the highest accuracy for AGB, whereas BoLASSO achieved comparable accuracy to PLS for LAI and CC using a reduced number of selected bands. The selected wavelengths were located mainly in the visible, red-edge, and near-infrared regions. These results indicate that band-selection strategies should be tailored to the target trait and provide a basis for efficient band design in crop phenotyping.
Why it matches plant phenotyping methodsハイパースペクトル反射を用いた作物形質推定について、バンド選択アルゴリズムと回帰モデルを比較・独立検証しており、フェノタイピング手法の技術評価が中心である。
abstractPredicting canopy traits non-destructively is important for understanding crop growth and improving phenotyping efficiency.
Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 15 Sept 2026
Accurate acquisition of plant phenotypes is crucial for elucidating plant growth and development, underlying genetic mechanisms, and responses to environmental stimuli. Traditional three-dimensional (3D) phenotyping mainly captures geometric traits such as height, leaf area, and canopy volume, while overlooking physiological and biochemical information. Here, we present a hyperspectral point clouds generation method based on PlantGaussian (a 3D Gaussian Splatting technique) that integrates structural and spectral information, extending 3D phenotyping beyond geometry to include physiology. High-quality plant point clouds were first reconstructed using PlantGaussian, and hyperspectral images(HSI) were mapped onto them to produce hyperspectral point clouds. In potted soybean experiments, we built predictive models linking hyperspectral reflectance to SPAD (chlorophyll content) and EWT (equivalent water thickness), and visualized their 3D distributions. The hyperspectral point clouds achieved strong predictive performance for SPAD ( R 2 = 0.78, RMSE = 2.05) and EWT ( R 2 = 0.80, RMSE = 1.07), thereby validating the approach. It further revealed clear vertical stratification within the canopy, highlighting significant spatial heterogeneity of SPAD and EWT in individual plants. Temporal monitoring from August 6 to 21, 2025, captured a sharp increase in EWT after heavy rainfall on August 11. Overall, our results demonstrate that hyperspectral point clouds enable accurate, non-destructive trait estimation and provide a powerful tool for exploring plant function, monitoring stress responses, and advancing precision agriculture.
Why it matches plant phenotyping methods植物の3D形態とハイパースペクトル情報を統合してSPAD・EWTを推定する手法を開発し、予測性能を検証しているため、植物フェノタイピング手法が中心である。
abstractHere, we present a hyperspectral point clouds generation method based on PlantGaussian (a 3D Gaussian Splatting technique) that integrates structural and spectral information, extending 3D phenotyping beyond geometry to include physiology.
Urban forest carbon sequestration is vital for environmental health, climate change mitigation, and enhancing the quality of life in urban areas. This paper presents a framework for high density point clouds production by integrating point data from aerial photogrammetry, UAV-based LiDAR and terrestrial LiDAR for individual tree measurements and carbon storage estimation. The aerial photos, UAV-based LiDAR and terrestrial LiDAR were observed based on common ground control points and combined using Iterative Closest Point (ICP) algorithm. The combined point cloud was iltered to separate ground points and normalized based on Digital Terrain Model (DTM). The normalized point cloud was used for individual tree segmentation from which individual tree measurements such as, tree height, Diameter at Breast Height (DBH) and crown diameter were estimated. The estimated tree parameters were used for individual carbon estimation. The results show that the individual tree segmentation method signi icantly underestimated the number of trees. The estimation of DBH, tree height, and crown diameter achieved the Root Mean Square Error (RMSE) value of 0.107m, 1.385m and 2.650m respectively. However, in general the estimates experience underestimation as shown by Mean Bias Error (MBE) with 0.003m, -0.636m and 0.001m for DBH, tree height and crown diameter respectively. The estimated values for each individual tree were used for individual tree biomass and carbon storage recording the Root Mean Square Error (RMSE) at 1970.236 kg and 886.606 kgC respectively while attaining the Mean Bias Error (MBE) measure of 140.019 kg and 63.009 kgC each. The proposed framework showed promising results for individual tree carbon estimation. Nonetheless, further attention should be given on individual tree delineation process.
Why it matches plant phenotyping methods航空写真、UAV・地上LiDARを統合し、個体樹木の分離と樹高・DBH・樹冠径を推定して精度評価する手法が中心であり、植物形質計測の技術的検証に該当する。
abstractThis paper presents a framework for high density point clouds production by integrating point data from aerial photogrammetry, UAV-based LiDAR and terrestrial LiDAR for individual tree measurements and carbon storage estimation.
Hyperspectral imaging is an imaging technique that allows for acquisition of high-resolution spectral information beyond that of the visible spectrum. When applied to plants, it effectively enables non-invasive characterization of physiological status and has been widely used in agricultural settings. Marchantia is a model bryophyte species whose flat morphology and visually distinct stress-response phenotypes makes it an ideal candidate for imaging studies. Here, we provide a comprehensive protocol for hyperspectral imaging for Marchantia plants, which encompasses hardware configuration, data acquisition, and computations processing. This protocol features a streamlined data processing pipeline hosted on a web-based development platform that automates 1) the segmentation of plant area into spatially distinct regions for localized analysis of intra-specimen physiological gradients, and 2) classification of plant pixels based on their spectral signatures. All results are exported as structured CSV files for ease of further analysis as desired by the user.
Why it matches plant phenotyping methodsマーチャンティアを対象としたハイパースペクトル撮像プロトコルと、植物領域のセグメンテーション・スペクトル分類を含む処理パイプラインを開発しており、植物の生理状態取得が中心的な方法論的貢献である。
abstractHere, we provide a comprehensive protocol for hyperspectral imaging for Marchantia plants, which encompasses hardware configuration, data acquisition, and computations processing.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicExample images used in this protocol have previously been published by Krishnamoorthi et al. (2024) 4 and can be downloaded from https://github.com/dr-daisuke-urano/PlantHyperspectralSVDOpen asset ↗PlantHyperspectralSVDlines:47-85Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Plant disease detection under complex field conditions remains a critical challenge for precision agriculture due to varying illumination, scale variations, subtle lesion patterns, and inter-class visual ambiguity. This study proposes MAFusionNet, a disease-aware hybrid vision framework integrating Mamba and Transformer architectures, with components explicitly designed for plant disease-specific challenges. The MAFusion Mixer operates parallel CS-Mamba and self-attention branches to simultaneously capture sequential lesion boundary evolution and global diseasecontext spatial relationships. The CS-Mamba branch employs the SS2D-LS Block with twodimensional selective scanning and Local-Selective enhancement for linear-complexity longrange modeling while preserving 2D lesion morphology. The PConv operator uses asymmetric directional kernels forming cross-shaped receptive fields to capture anisotropic disease patterns such as vein-aligned blights and directional rust streaks. We constructed PD40, a large-scale dataset with 80,369 expert-verified annotated images across 40 disease categories spanning eight major crops, with inter-annotator agreement Cohen’s κ = 0.874. Extensive experiments demonstrate that MAFusionNet achieves 94.7% mAP 50 and 81.8% mAP 50:95 on PD40, surpassing 25 state-of-the-art baselines including recent hybrid Mamba-Transformer detectors (CropMamba, HybridMamba, Mamba-DETR), with comprehensive ablation studies validating each component’s non-redundant contribution. Edge deployment analysis on NVIDIA Jetson hardware demonstrates practical feasibility: the compressed MAFusionNet-T-Lite variant (8.7M parameters) achieves 89.3% mAP 50 at 18.4 FPS on Jetson Nano with 8.3W power consumption. The dataset and code are available at PD40-Dataset GitHub Repository.
Why it matches plant phenotyping methods植物病害の症状を画像から検出・分類する視覚モデルを開発し、注釈付き大規模データセットで検証しているため、植物状態の画像ベース表現型計測が中心である。
abstractWe constructed PD40, a large-scale dataset with 80,369 expert-verified annotated images across 40 disease categories spanning eight major crops
Fractional Vegetation Cover of Crops (CropFVC) is a critical canopy parameter for monitoring crop growth, yet the behavior of widely used global FVC products (GLASS, GEOV1, GEOV2, and GEOV3) over croplands remains insufficiently understood due to fragmented validation references and limited crop-specific assessments. This study compiled a multi-source global CropFVC reference dataset (2000–2024) by integrating five international validation networks, the literature-derived samples, and newly acquired UAV and Jilin-1 satellite-derived CropFVC samples from China in 2024. The references were organized into three complementary validation contexts (V1~V3) to examine product behavior under different temporal coverage, crop purity, and reference conditions, together with spatio-temporal observations at the KONZ site. Results show that (1) across validation contexts, the evaluated products showed consistent behavior patterns, including shared overestimation under dense canopy conditions and reduced differences at low FVC levels; (2) spatio-temporal analysis at the KONZ site confirmed that peak-season deviations reflect shared response behavior rather than site-specific reference uncertainties; (3) historical mixed references (V1~V2) showed similar bias structures, whereas crop-specific validation (V3) preliminary revealed clearer crop-dependent responses, with predictive difficulty following winter wheat > maize > rice > soybean and improved stability after integrating 2024 observations. The integration of recent high-resolution crop observations expands existing global CropFVC references and enables behavior-oriented interpretation of global FVC products beyond simple accuracy ranking, providing an updated validation perspective for future development and application of global CropFVC products in agricultural monitoring.
Why it matches plant phenotyping methods作物キャノピーのFVCという植物形質を対象に、複数の全球FVC推定プロダクトを多様な参照データで体系的に検証し、UAV・衛星観測を含むCropFVC参照データセットを構築している。形質取得・検証が研究の中心である。
titleMulti-Context Validation of Global Fractional Vegetation Cover Products in Croplands Using Multi-Source Crop FVC References
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:240-256Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 13 Sept 2026
Forest digital twins play a crucial role in modern precision forestry by supporting biomass estimation and carbon cycle monitoring. However, existing 3D reconstruction methods struggle to simultaneously achieve metric-level structural accuracy and visual realism in complex understory environments. This study proposes a semantically constrained 3D Gaussian Splatting framework that fuses handheld LiDAR point clouds with unmanned aerial vehicle imagery. First, a multi-modal fusion mechanism is constructed to extract geometric anchors from registered LiDAR data for precise 3DGS spatial initialization, which mitigates rendering artifacts and geometric drift caused by poor initialization in purely visual methods. Second, a semantic regularization optimization strategy is proposed to realize differentiated modeling of tree trunks and canopies, effectively balancing the structural accuracy of rigid trunks and the photorealistic rendering of non-rigid canopies. Experiments conducted on three study plots demonstrate that the proposed approach achieves an average PSNR of 24.94 dB, SSIM of 0.773, and LPIPS of 0.231 across all plots, outperforming standard NeRF and baseline 3DGS, while enabling DBH estimation with R2 = 0.848 and RMSE = 2.705 cm. This method provides a solution for high-fidelity forest digital twin construction in open-canopy forest environments such as urban and campus forests.
Why it matches plant phenotyping methodsLiDAR・UAV画像を統合した3D再構成法を開発し、樹幹・樹冠の構造モデル化とDBH推定を評価しており、植物形質取得が中心的な技術貢献である。
abstractThis study proposes a semantically constrained 3D Gaussian Splatting framework that fuses handheld LiDAR point clouds with unmanned aerial vehicle imagery.
Understanding diurnal canopy orientation in crops is important for interpreting plant responses to light and environmental conditions, yet field-based quantification remains limited. In this study, we present Heliocot, a field RGB imaging approach that converts time-resolved images into reference-area standardized projected leaf area (PLA) time series to quantify within-day canopy orientation dynamics in early-season cotton. Leaf instance segmentation was performed using YOLOv8m-seg and refined through a 144-combination post-processing optimization. On the held-out early-stage validation/tuning set, the selected workflow showed strong agreement with manual ground truth (R2 = 0.948; NRMSE = 0.082) and destructive leaf area measurements (R2 = 0.836). Derived diurnal metrics, including Daily Orientation Amplitude (DOA) and Peak Orientation Index (POI), consistently revealed a midday maximum (13:15) in canopy projection. Exploratory genotype-level analysis suggested negative associations between orientation indices and selected plant traits, including specific leaf area (SLA) versus DOA (r = −0.71, p = 0.021, R2 = 0.508), destructive leaf area (LA) versus DOA (r = −0.69, p = 0.028, R2 = 0.471), and stem dry weight (SDW) versus POI (r = −0.74, p = 0.014, R2 = 0.554), while plant height was not significantly associated with POI and DOA (p > 0.05). Although currently limited to early-season conditions and two field-imaging dates, this approach provides a practical workflow for field-based monitoring of canopy projection dynamics in cotton, while broader temporal and environmental validation remains necessary.
Why it matches plant phenotyping methods圃場RGB画像から葉面積と日周キャノピー配向動態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 present Heliocot, a field RGB imaging approach that converts time-resolved images into reference-area standardized projected leaf area (PLA) time series to quantify within-day canopy orientation dynamics in early-season cotton.
Nodule color and morphology are key readouts of legume symbiotic performance. However, long-term preservation of post-excavation nodules with intact morphology, color, and microbial cleanliness remains a major challenge. This study developed a two-stage aqueous-phase preservation method (TAPP) that enables rapid structural fixation and long-term chemical stabilization. A comprehensive evaluation was subsequently established, incorporating composite morphological score (0-5), color difference (ΔE) and its piecewise slope over time ([Formula: see text]), and visible contamination grade (0-3). Peanut and soybean nodules from multiple regions and cultivars were tracked for 24 months under five preservation methods: TAPP, FormalinCu, TAPP-Resin, Resin, and AirDry. TAPP showed the best overall preservation, with composite morphological scores of 4.65 ± 0.14 for peanut and 4.63 ± 0.22 for soybean at 24 months, and no visible mold. Color change slowed over time: [Formula: see text] decreased from 1.83 to 1.10 ΔE·month - 1 during 0-1 month to 0.16 ΔE·month - 1 during 12-24 months, yielding final ΔE values of 10.53 ± 1.88 and 10.32 ± 1.93, respectively. Notably, TAPP pretreatment markedly improved resin-embedded samples, demonstrating scalability and flexible deployment. In addition, this study further proposes a stage-wise workflow that integrates on-site pre-fixation, long-distance transport, and long-term storage to enable cross-regional circulation and collaborative phenomics of oxidation-prone, dehydration-sensitive nodules. Together, this work establishes a standardized, traceable workflow to preserve and benchmark legume root nodule phenotypes, supporting cross-laboratory comparability and longitudinal cross-source analyses.
Why it matches plant phenotyping methodsマメ科根粒の形態・色・汚染状態という植物表現型を長期保存し、定量評価・比較する手法と標準化ワークフローが研究の中心であるため。
abstractThis study developed a two-stage aqueous-phase preservation method (TAPP) that enables rapid structural fixation and long-term chemical stabilization.
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:600-676Plant phenotyping relevance match · UnverifiedEurope PMC · OpenAlex · checked 13 Sept 2026
Volume electron microscopy based on serial sectioning allows for three-dimensional (3D) visualization and analysis of the internal structures of tissues, cells, and organelles. One such technique, focused ion beam (FIB) scanning electron microscopy (SEM), has the advantages of nanoscale sectioning and high z-resolution, but the disadvantage of limited volume processing. Because of this limitation, targeting localized objects by FIB-SEM is difficult. Here, we developed a FIB-SEM observation workflow that enables the analysis of the filiform apparatus of synergid cells enclosed in the Arabidopsis ovule. In this protocol, plant samples are stained, embedded, trimmed, and carbon-coated while maintaining their orientation within the tissue. Then, sequential observations are performed using Cut & See function of FIB-SEM, followed by image processing for 3D reconstruction. Utilization of multi-scanning and image cropping from high-resolution data helps to identify localized targets within plant tissue. The filiform apparatus, which is an invaginated cell wall structure of the synergid cells, shows distinct contrast in each image, allowing for segmentation using brightness-based binarization. Such segmentation avoids the need to manually trace complex structures and facilitates 3D reconstruction by volume electron microscopy. Key features • Sampling and trimming of the resin block enable directionally loading in FIB-SEM. • Multi-scanning by FIB-SEM and target extraction by image processing software enable 3D reconstruction of local areas within the sample block. • Binarization using distinctive brightness of cellular structures enables segmentation without manual tracing of complex structures such as the filiform apparatus cell wall.
Why it matches plant phenotyping methods植物組織内の構造をFIB-SEMと画像処理で3D再構成・セグメンテーションするワークフローを開発しており、フィリフォーム装置形態という植物器官形質の取得が中心である。
abstractHere, we developed a FIB-SEM observation workflow that enables the analysis of the filiform apparatus of synergid cells enclosed in the Arabidopsis ovule.
Abstract Magnetic resonance imaging (MRI) enables non-invasive and non-destructive, three-dimensional anatomical and functional imaging of plant tissues and the quantitative investigation of dynamic processes such as water transport. Despite these advantages, MRI remains underutilized in plant and biomimetic research. One major limitation is the difficulty of maintaining physiologically suitable and stable environmental conditions during prolonged measurements, particularly when using ultra-high-field preclinical MRI scanners that were originally developed for small-animal imaging. In this work, we present a low cost, climate-controlled and MR-compatible growth chamber that includes an in-bore extension for preclinical MRI scanners. The system integrates growth and imaging conditions into a single setup, allowing continuous control of temperature, humidity, and illumination by the same system and removing the need to maintain separate commercial growth chambers alongside custom in-bore extensions. The implementation was optimized for the horizontal bore of a small animal scanner (Bruker PharmaScan 70/16) with 16 cm bore diameter and 72 mm free access but is applicable to other ultra-high-field preclinical MRI systems with comparable dimensions. The performance of the climate chamber and the in-bore extension was characterized with respect to temperature, humidity, and illumination stability. In addition, the potential negative impact of the insert and its electronics on the MRI signal (B0 homogeneity, RF attenuation as well as potential RF artefacts) were verified. Functional validation in form of sap flow measurements as well as anatomical validation was demonstrated in a naturally transpiring stem of Passiflora quadrangularis. Under controlled in-bore environmental conditions, changes in sap flow velocity were reliably detected using a pulsed field gradient spin-echo sequence. Specifically, increasing the light intensity in the extension resulted in a shift of the maximum flow velocity in individual vascular bundles from 0.21 mm/s and 0.39 mm/s to 1.37 mm/s and 1.17 mm/s, respectively. In addition, high-resolution anatomical imaging (1 mm slices with an in-plane resolution of 25 µm) of branching regions in Dracaena braunii was successfully performed without observable motion artifacts. The presented system provides a low-cost, open-source solution for conducting anatomical and functional MRI studies of intact plants using ultra-high field preclinical MRI scanners.
Why it matches plant phenotyping methods植物のMRI計測を可能にする環境制御・MR互換チャンバーを開発し、性能および植物の解剖・通道機能計測で検証しており、フェノタイピング手法と基盤が研究の中心である。
abstractIn this work, we present a low cost, climate-controlled and MR-compatible growth chamber that includes an in-bore extension for preclinical MRI scanners.
Achieving high-throughput and precise phenotypic quantification and imaging modalities of stomatal and epidermal cells across diverse species remains a primary bottleneck in elucidating the mechanisms of stomatal dynamics, epidermal patterning, and environmental adaptation of plants. Here, we developed EpiReasoner, an artificial intelligence framework comprising a vision module, EpiVision, and a knowledge-based reasoning module, EpiBrain, for the quantitative phenotypic analysis and domain-specific knowledge reasoning of stomatal complexes and pavement cells in plants. Operating across bright-field, scanning electron microscopy, and differential interference contrast modalities, EpiVision achieves precise instance segmentation in various monocotyledonous, dicotyledonous, and fern species. Its performance significantly surpasses current state-of-the-art models. Moreover, we defined 23 quantitative indices describing stomatal cell morphology and spatial distribution. For domain-specific tasks such as phenotype prediction, genotype deduction, and molecular mechanism reasoning, EpiBrain demonstrates a human preference rate significantly higher than that of general-purpose large language models, including GPT-5 and Claude Sonnet 4. The application of EpiReasoner to phenotypic data of stomatal density derived from a tomato natural population of 170 accessions successfully identified a major quantitative trait locus on chromosome 8. The candidate gene, SKP1-interaction partner 19L ( SKIP19L ), encoding an F-box family protein, exhibited severe allele frequency drift during tomato domestication, which is highly consistent with the adaptive trend of reduced stomatal density under artificial selection. EpiReasoner provides a novel paradigm that unifies visual phenomics and knowledge-driven reasoning for the biology of stomata and pavement cells, thereby significantly accelerating scientific discovery in plant science.
Why it matches plant phenotyping methods植物の気孔・表皮細胞を対象に、画像解析と知識推論を統合したフェノタイピング手法を開発しており、形態・空間分布の定量化が中心的な貢献である。
abstractwe developed EpiReasoner, an artificial intelligence framework comprising a vision module, EpiVision, and a knowledge-based reasoning module, EpiBrain, for the quantitative phenotypic analysis and domain-specific knowledge reasoning of stomatal complexes and pavement cells in plants.
Conventional two-dimensional image-based methods are limited in measuring the three-dimensional morphology of tobacco stems, especially thickness and curved geometry. This study proposes a point-cloud-based method for three-dimensional tobacco stem measurement using a dual-laser scanning system. The method combines improved centerline skeleton extraction, upper–lower surface registration, and skeleton-guided cross-sectional analysis to estimate length, width, thickness, and fineness. Adaptive neighborhood re-weighting and curvature-constrained regularization are introduced to improve skeleton extraction, and reference-assisted registration is used to support thickness measurement. For a standard gauge block, the proposed method achieved mean absolute errors below 0.009 mm and root mean square errors below 0.011 mm for length, width, and thickness measurements. Validation on 30 tobacco stem samples showed good agreement with the YC image-based method for length and width, with correlation coefficients of 0.998 and 0.997, respectively. The results demonstrate the feasibility of thickness-aware three-dimensional morphological measurement of tobacco stems under the tested conditions.
Why it matches plant phenotyping methodsタバコ茎の長さ・幅・厚さ・細さという植物形態形質を、点群・レーザースキャン・骨格抽出で測定する手法の開発と検証が研究の中心である。
abstractThis study proposes a point-cloud-based method for three-dimensional tobacco stem measurement using a dual-laser scanning system.
Unmanned aerial vehicles (UAVs) are broadly used for high-throughput plant phenotyping, yet their long-term use in public-sector research is increasingly challenged by regulatory restrictions and reliance on proprietary platforms. This study presented a regulation-compliant, modular multi-sensor unmanned aerial system (UAS) designed to deliver flexible, high-quality phenotyping data without dependence on restricted ecosystems. A dual-mount, open-architecture payload integrated RGB, multispectral, and thermal sensors, enabling simultaneous acquisition of structural, spectral, and thermal information within a unified workflow. Field validation in a lantana (Lantana camara) breeding trial demonstrated high-precision multi-sensor data fusion and reliable trait extraction. Spatial co-registration achieved centimeter-level accuracy, with alignment errors of 0.88 cm (multispectral) and 3.23 cm (thermal) relative to the RGB reference. UAV-derived canopy height closely matched ground measurements (R2 up to 0.98; RMSE as low as 1.57 cm), while canopy coverage estimates showed consistency across sensing modalities (R2 = 0.99; RMSE = 0.02 m2). Calibrated thermal orthomosaics provided robust canopy temperature estimation (RMSE = 3.13 °C), supporting a quantitative assessment of plant physiological status. Together, these results demonstrate that a regulation-compliant, open-architecture UAV platform can achieve high accuracy in multi-modal phenotyping while maintaining flexibility and cost efficiency. This work demonstrates a scalable and sustainable framework for UAV-based phenotyping, enabling researchers to adapt to evolving regulations while advancing data-driven crop improvement.
Why it matches plant phenotyping methods植物フェノタイピング用のマルチセンサーUAVプラットフォームを設計・検証し、植物形質の抽出精度を評価しているため、方法が中心的である。
abstractThis study presented a regulation-compliant, modular multi-sensor unmanned aerial system (UAS) designed to deliver flexible, high-quality phenotyping data without dependence on restricted ecosystems.
Crop attribute detection, as a key component of intelligent agricultural harvesting machinery, plays a crucial role in harvesting efficiency, loss reduction, and autonomous operation control. Compared with existing reviews on artificial intelligence and sensing technologies in agriculture, this review focuses on crop attribute detection scenarios oriented toward the intelligent decision-making and control requirements of agricultural harvesting machinery. It mainly analyzes crop attributes that affect harvesting operations, as well as the sensors and algorithms involved in detecting these attributes, and further clarifies the relationship between detection methods and control decisions in agricultural harvesting machinery. For grain crops, the key attributes relevant to harvesting operations include plant height, plant density, spike number, crop lodging, canopy structure, and crop position. For fruit and vegetable crops, the key attributes relevant to harvesting operations include maturity, position, and quality. From the perspectives of multi-source data acquisition, data analysis, and attribute detection algorithms, the key technologies in the field of crop attribute detection are systematically summarized and analyzed, including sensors used in crop attribute detection, such as RGB, spectral, near-infrared, and LiDAR sensors, as well as data analysis and recognition approaches, such as image classification, object detection, and point cloud analysis. The complexity of field environments and the dynamics of machine operation are analyzed, highlighting the technical bottlenecks of current detection systems in environmental adaptability, real-time responsiveness, and resistance to interference. To address these challenges, feasible optimization directions were proposed, including multi-sensor fusion, weakly supervised learning, and few-shot learning. This review aims to provide systematic references and theoretical support for the coordinated development of crop detection and control decision-making in intelligent agricultural harvesting systems.
Why it matches plant phenotyping methods収穫機械向けではあるが、草丈・密度・穂数・倒伏・群落構造・成熟度など植物の形態・状態を検出するセンサーと解析手法を体系的にレビューしており、表現型取得法が中心である。
titleA Review of Crop Attribute Detection for Agricultural Harvesting Machinery
Abstract To address the problem of fine branch identification and pruning decision for dormant apple trees, this study proposes a 3D point cloud branch recognition method integrating Neural Radiance Fields (NeRF) and the PointNeXt network. This method employs the neural radiance field theory to construct a point cloud model of apple trees, achieving fine detail representation and providing a high-precision, high-standard dataset for subsequent branch pruning experiments. First, a panoramic video is captured by circling the fruit tree, and a multi-view image sequence is obtained through frame sampling. Subsequently, the Structure from Motion (SfM) algorithm is employed for sparse reconstruction to recover the pose information of the images. On this basis, a neural radiance field model is trained. Hierarchical sampling is performed using ray casting, and the sampled points, combined with positional encoding, are fed into a multi-layer perceptron (MLP). The radiance field is then generated via volume rendering, from which a high-fidelity 3D point cloud model of the fruit tree is derived. Finally, the point cloud is processed using the PointNeXt semantic segmentation network to achieve the identification and segmentation of branches to be pruned and branches to be retained. To verify the effectiveness of the method, this study reconstructed point cloud models of dormant apple trees and selected 10 of them for experimental analysis. The algorithm achieved an average overall recognition accuracy of 75.15% and an average false negative rate (FNR) of 24.85%. The experimental results demonstrate that the proposed method constructs a 3D point cloud model with multi-scale, multi-modal, and high-precision phenotypic information at a relatively low cost. It not only overcomes the limitations of traditional 3D reconstruction methods, such as insufficient point cloud accuracy and difficulty in accurately identifying thin branches, but also effectively mitigates the high misrecognition rate observed in conventional branch recognition approaches. This provides technical support for unmanned agricultural machinery pruning in orchards and holds significant implications for achieving precision agriculture and sustainable development.
Why it matches plant phenotyping methodsNeRFとPointNeXtを用いてリンゴ樹の3D点群を構築し、剪定対象枝を認識・分割する手法が研究の中心であり、植物の形態・構造状態を直接推定して性能評価している。
abstractthis study proposes a 3D point cloud branch recognition method integrating Neural Radiance Fields (NeRF) and the PointNeXt network.
Sudanian savannas remain underexplored in terms of utilizing close-range photogrammetry (CRP) for assessing tree characteristics, leaving a gap in ecological research. This study evaluates the performance of automatic stem modeling techniques using CRP-generated point clouds for 30 trees from five savanna species. Two labeling methods, a machine learning-based approach (StemML) and a flatness/vertical structure-based method (StemFlat), were used to extract stem points. We applied three diameter estimation techniques: convex-hull line fitting (CHM), least squares circle fitting (LSM), and Ransac circle fitting (RANSAC), comparing their results against field measurements using root mean square error (RMSE), bias and the coefficient of determination R2. The combination of StemML and CHM yielded the best performance, with an RMSE of 2.1 cm (5.8%), R2 of 0.983 and a bias of −0.30 cm, accurately identifying 93% of stem segments. Diameter estimation accuracy varied with height, with optimal alignment between CRP-derived profiles and manual measurements occurring between 0.5 and 2.5 m. These findings demonstrate CRP’s potential for modeling savanna tree stems and highlight the importance of method selection in ensuring reliable measurements.
Why it matches plant phenotyping methods近距離写真測量による樹幹点群の抽出・モデル化と直径推定手法を開発・比較し、野外測定で性能検証しているため、植物形質計測手法が研究の中心である。
abstractThis study evaluates the performance of automatic stem modeling techniques using CRP-generated point clouds for 30 trees from five savanna species.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 5 Sept 2026
Eggplant (Solanum melongena L.) is a widely cultivated vegetable crop worldwide, occupying an important position in the agricultural industries of Asia, the Middle East, and Southern Europe. Its significance extends beyond agricultural economics to diverse dimensions such as dietary nutrition, rendering it of considerable research and application value. Traditional crop phenotyping methods suffer from low efficiency, substantial manual errors, and a tendency to damage tender seedlings, while existing three-dimensional phenotyping techniques face challenges including strong background interference and large data volumes. These dual constraints limit the accuracy and application feasibility of seedling phenotyping. To address these issues, this study proposes a non-destructive phenotyping method for eggplant seedlings, with the improvement of the PointNet++ architecture as its core and point cloud background purification as a key preprocessing step, aiming to enhance eggplant breeding efficiency and seedling screening accuracy. The raw point clouds first undergo background purification to actively remove seedling tray points, thereby improving point cloud purity and reducing data size. Concurrently, based on the PointNet++ model, we develop an improved point cloud segmentation model, EggplantPointNet++, by introducing multi-scale residual blocks, integrating channel attention mechanisms, incorporating a global context module, and refining the feature propagation layer. In conjunction with the DBSCAN clustering algorithm, this approach achieves semantic and instance segmentation of eggplant seedling point clouds, with certain improvements in segmentation accuracy and model efficiency under small-scale and occluded scenarios. To validate the technical effectiveness, multiple comparative experiments and ablation studies were conducted. The results demonstrate that EggplantPointNet++ outperforms the original model, background purification preprocessing provides positive gains, and each improved module contributes positively. The final model achieves improvements in core metrics including Recall and F1-score. Based on the segmented point cloud data, this study calculates core phenotypic parameters including plant height, stem diameter, cotyledon angle, and cotyledon area. Using the technical system established in this study, we completed the time-series measurement of three-dimensional morphological changes in eggplant seedlings during the cotyledon stage, providing quantitative references for seedling growth assessment and superior plant selection.
Why it matches plant phenotyping methodsナス幼苗の3D点群から形質を抽出する非破壊フェノタイピング手法を開発し、比較実験・アブレーションで技術性能を検証しているため、方法が中心的である。
abstractthis study proposes a non-destructive phenotyping method for eggplant seedlings
Thinning is a cornerstone of sustainable forest management (SFM), yet decisions are often constrained by subjective experience and a lack of quantitative spatial data. To address this, we developed a precision thinning framework for Cunninghamia lanceolata plantations by fusing terrestrial and aerial LiDAR data. Instead of relying on manual selection, this study established a quantitative mechanism for identifying harvesting targets. We employed a layer-stacking seed-point algorithm to accurately segment individual trees and extracted key parameters (tree height, DBH, and crown width) with high precision (Overall R2 ≥ 0.85). Furthermore, an objective multi-criteria weighting method (AHP – CRITIC) was constructed to prioritize thinning targets based on stand stability and growth status. Results indicated that the proposed framework achieved an individual tree segmentation F1-score of 89.24%. Simulated thinning based on the calculated weights significantly optimized the stand spatial structure: canopy openness increased by 11.8%, and spatial indices converged toward optimal ranges. Compared with conventional practices, the proposed approach effectively reduced growth dispersion and enhanced population coordination. These findings demonstrate that integrating multi-platform LiDAR with objective weighting algorithms offers a scientifically rigorous pathway for precision forestry, promoting both productivity and the long-term sustainability of plantation ecosystems.
Why it matches plant phenotyping methodsLiDAR融合による個体木セグメンテーションと樹高・DBH・樹冠幅の抽出、および精度評価が枯間伐フレームワークの中心的な技術要素であるため、植物表現型計測手法として採用する。
abstractWe employed a layer-stacking seed-point algorithm to accurately segment individual trees and extracted key parameters (tree height, DBH, and crown width) with high precision (Overall R2 ≥ 0.85).
Traditional field surveys of urban park biodiversity lack efficiency and scale, whereas LiDAR offers precise 3D vegetation quantification. This study investigates how 3D vegetation structural complexity impacts urban park plant diversity. We integrated Unmanned Aerial Vehicle (UAV) and handheld LiDAR data with ground-based quadrat surveys to capture comprehensive vegetation structures. Using six key 3D structural metrics, we modeled their relationship with plant diversity via Random Forest. Results indicate canopy height standard deviation (Hstd) primarily influences cultivated plant diversity, while the vegetation density index (VDI) drives spontaneous diversity. The model predicted species richness better (30.63% variance explained) than the Shannon index (7.63%). These drivers exhibited significant non-linear effects and potential ecological thresholds. A strong synergy emerged: when the vertical structure is complex and the 3D spatial density is high, the predicted plant diversity initially exhibits a trend of saturation and stabilization. Ultimately, multi-dimensional 3D vegetation structure proves to be a robust indicator of plant diversity. Our proposed multi-platform LiDAR fusion framework enables rapid, precise ecological assessments, providing methodological references and support for the transition from 2D to 3D green quality evaluation in the fine-scale management of similar cities.
Why it matches plant phenotyping methodsUAV・ハンドヘルドLiDARを融合し、植物群落の3D構造指標を抽出して植物多様性を推定する測定・解析フレームワークが中心であり、植物状態の定量的評価に該当する。
abstractWe integrated Unmanned Aerial Vehicle (UAV) and handheld LiDAR data with ground-based quadrat surveys to capture comprehensive vegetation structures.
Statewide tracking of urban tree canopy change is essential for evaluating progress toward policy targets, but detecting real change requires both high-resolution mapping and rigorous uncertainty estimation. We produced a four-year canopy cover time series for all California census-designated places using 60-cm NAIP aerial imagery and a U-Net deep learning model trained with semi-automated LiDAR-derived labels and manually annotated tiles. Canopy cover and change were estimated using stratified, error-adjusted area estimation, enabling comparisons across years. Statewide canopy cover showed a modest negative trend from 2016 to 2022 (Sens slope: -0.60% per year), but confidence intervals included zero across all groups and climate zones, indicating that trends were not statistically distinguishable from no change. Urban canopy cover was consistently lower than non-urban canopy by approximately six percentage points, and canopy cover was highest in the Northern California Coast and lowest in the Southwest Desert. Residential parcels accounted for 55-56% of canopy within incorporated urban areas across all years, indicating that statewide canopy increase goals will require engagement with private landowners. Error adjustment substantially altered canopy estimates relative to raw pixel-count totals, with direct implications for AB 2251 canopy tracking where baselines and targets drawn from unadjusted maps may not reflect true canopy extent. This open-source workflow is transferable to future NAIP acquisition years and other U.S. states, providing a scalable framework for long-term urban forest monitoring.
Why it matches plant phenotyping methods航空画像とU-Netにより都市樹冠被覆率を推定し、誤差調整と不確実性評価を行う再利用可能なワークフローを中心に扱っているため、植物状態の測定手法として採用。
abstractWe produced a four-year canopy cover time series for all California census-designated places using 60-cm NAIP aerial imagery and a U-Net deep learning model trained with semi-automated LiDAR-derived labels and manually annotated tiles.
Introduction Addressing the core bottleneck in traditional crop models-the disconnect between morphology and physiological function at the organ scale and their limited dynamic response to environmental changes-this study aimed to construct a multi-source data fusion maize growth model for simultaneous organ-scale simulation. Methods We developed a closed-loop Environment-Driven-Functional Response-Morphological Feedback (EDFM) architecture. By integrating environmental time-series data, RGB images, and 3D point clouds, we created a multimodal fusion model based on a gated attention network. This approach adaptively weights multi-source features and pioneers a bidirectional morphology-physiology feedback loop based on physiological development time (PDT) and NURBS surfaces. The WOFOST moisture response function was also improved. Results The model significantly enhanced the simulation accuracy of organ-scale growth, reducing the root mean square error (RMSE) for plant height by 74.6% through a morphology-physiology dynamic weighting mechanism. More fundamentally, it resolved the disconnect between morphological and physiological processes. The improved plant height prediction validates the model's effectiveness at the organ scale. Discussion The pioneering "physiology-morphology" parallel simulation architecture provides an interpretable theoretical model and robust quantitative tools for designing high-photosynthetic-efficiency plant architecture and enabling precision water-fertilizer management.
Why it matches plant phenotyping methodsRGB画像・3D点群・環境データを統合し、器官スケールの形態と生長をシミュレーションする手法を開発しており、植物形質（草丈など）の推定が中心的な技術貢献である。
abstractWe developed a closed-loop Environment-Driven-Functional Response-Morphological Feedback (EDFM) architecture.
This study evaluates the performance of LiDAR sensing and UAV photogrammetry for three-dimensional canopy reconstruction and structural parameter estimation in precision agriculture. Experiments were conducted in Sicily (Italy) on Moringa oleifera Lam. and Ficus macrophylla subsp. columnaris, representing contrasting canopy architectures. LiDAR and UAV data were used to generate canopy models and estimate canopy height, volume, and vegetation density. A voxel-based approach was applied to LiDAR point clouds to analyze internal canopy structure. LiDAR significantly outperformed UAV photogrammetry, achieving lower errors in canopy height estimation (RMSE = 0.19–0.21 m vs. 0.52–0.60 m) and canopy volume (3.5–4.2% vs. 13.7–16.1%). UAV photogrammetry provided reliable estimates of canopy surface but underestimated structural parameters in dense vegetation due to occlusion effects. Differences were more pronounced in Ficus macrophylla than in Moringa oleifera, highlighting the influence of canopy complexity. These findings demonstrate that LiDAR-derived structural metrics can improve canopy characterization and support precision agriculture applications such as biomass estimation, irrigation planning, and canopy management in Mediterranean cropping systems.
Why it matches plant phenotyping methodsLiDARとUAVフォトグラメトリによる樹冠の3次元再構成と、樹冠高・体積・密度推定を比較検証しており、植物形質取得手法が研究の中心です。
abstractThis study evaluates the performance of LiDAR sensing and UAV photogrammetry for three-dimensional canopy reconstruction and structural parameter estimation in precision agriculture.
Abstract This study assesses high‐throughput red, green, and blue (RGB) imaging as an approach for detecting subtle phenotypic differences under well‐watered and reduced watering conditions in genetically uniform wheat ( Triticum aestivum L.) populations. It aims to support the design of breeding populations by identifying parents with complementary coping mechanisms that can be combined in crosses to produce superior progeny. We used RGB imaging to monitor side‐projected area (SPA) in BC 2 F 6 wheat progenies under well‐watered, pre‐anthesis, and post‐anthesis reduced watering conditions. SPA was modeled with logistic growth curves per genotype to extract dynamic canopy traits, which, together with the area under the SPA‐based growth curve, were then correlated with yield, straw biomass, harvest index, and spike traits measured at maturity. Despite genetic similarity, RGB‐based imaging revealed distinct phenotypes under normal conditions and stress response strategies among wheat lines, highlighting the value of dynamic, non‐destructive phenotyping for identifying complementary response patterns. Under well‐watered conditions ( n = 36), area under the curve was strongly associated with grain weight ( R 2 = 0.76, 95% confidence interval [CI]: 0.59–0.87), but relationships weakened under reduced watering, especially post‐anthesis, indicating a reduced association of canopy size with reproductive output. The data revealed contrasting response patterns among breeding lines based on characteristics of the logistic growth curve under normal conditions, their recovery slope after pre‐flowering reduced watering, or conversion of their straw biomass into harvestable grains. RGB imaging enables real‐time, non‐destructive detection of reduced watering responses in genetically similar wheat lines and provides complementary in‐season data to design next‐generation breeding populations for climate‐resilient cultivars.
Why it matches plant phenotyping methodsRGB画像で動的なキャノピー形質を抽出し、育種利用に向けた非破壊・リアルタイム表現型解析を実質的に評価しているため。
abstractThis study assesses high‐throughput red, green, and blue (RGB) imaging as an approach for detecting subtle phenotypic differences
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Introduction: Tiller production is a critical determinant of turfgrass canopy density and plant performance, yet manual tiller counting is too labor-intensive for large breeding programs. Methods: To address this limitation, we evaluated 770 plants from an interspecific bentgrass hybrid population and developed three automated approaches for tiller quantification: a classical edge-based segmentation pipeline and two deep-learning models, Faster R-CNN and YOLOv8. Using a large annotated image dataset, we assessed each method's accuracy, robustness under occlusion, and computational efficiency. Results: Although two-stage detectors are often expected to provide superior precision for complex plant structures, the one-stage YOLOv8 model achieved the highest accuracy (R² = 0.97) and processed images substantially faster than Faster R-CNN, while both the edge-based method and Faster R-CNN showed reduced performance in dense canopies. Discussion: These findings demonstrate that recall-oriented one-stage detection can outperform more complex two-stage models for phenotyping tasks involving fine, highly occluded structures. The resulting workflow provides a reliable, high-throughput solution for generating biologically meaningful tiller counts and offers a transferable framework for integrating image-derived phenotypes into genetic analyses and breeding pipelines across grass species.
Why it matches plant phenotyping methodsイネ科植物の分げつ数を画像から自動抽出する複数手法を開発・比較検証しており、植物表現型取得法が研究の中心である。
abstractdeveloped three automated approaches for tiller quantification: a classical edge-based segmentation pipeline and two deep-learning models, Faster R-CNN and YOLOv8.
Abstract Branch shape is a relevant tree morphological trait, representative of tree ontogenetic stage, successional status and resource-use strategy. However, branch-level studies have been limited due to tedious, time-consuming or costly measurement procedures. Here we applied a cost-efficient, quantitative framework for tree branch shape data collection and statistical evaluation, applied to young open-grown Carya laciniosa , an ecologically valuable but rare large-seeded deciduous tree species. After accounting for branch size and orientation, we fitted different polynomial models to photogrammetric points of 41 major branch axes belonging to four arboretum-grown leaf-off C. laciniosa trees, ranging from 6.1 to 8.7 m in height. Parametric branch shape was identified with great precision by the fourth-order polynomials (R 2 = 0.96 ± 0.07 SD), but also acceptably by the third-order polynomials (R 2 = 0.93 ± 0.13 SD). The shape parameters were weakly related to branch position within the crown (R 2 < 0.40), in contrast to branch size (R 2 = 0.73). The identified S-shaped branch type may be termed plagio-orthotropic, with the proximal part arching plagiotropically and the distal part ascending orthotropically. This type of shape was stable across canopy height strata, but the shape variation and the magnitude of branch curvature clearly decreased towards the upper canopy layers, revealing combined effects of branch age, gravitropism and bending strains induced by the seasonal loads. Our results corroborate the architectural similarity among the mid-successional Carya spp. This study highlights the relevance of branch shape, which can be feasibly recorded in terms of transferable parameters, possibly as a generic functional trait with a potential for quantification of ecosystem services, such as rainfall interception and retention, shading potential, thermal regulation and biodiversity support.
Why it matches plant phenotyping methods枝形状という植物形態形質をフォトグラメトリで定量化する枠組みの開発・適用が研究の中心であり、植物フェノタイピング手法に該当する。
abstractwe applied a cost-efficient, quantitative framework for tree branch shape data collection and statistical evaluation
To address the issue of 3D reconstruction information loss caused by occlusion during single-view camera acquisition of crop phenotypic parameters, this study proposes a detection method for tomato plant phenotypic parameters based on multi-view 3D point cloud reconstruction. The Kinect 2.0 sensor was employed to acquire point cloud data of tomato plants from three different viewpoints. Background noise was effectively removed using a combination of Conditional Filtering and Statistical Outlier Removal methods. By extracting surface normal features and calculating Fast Point Feature Histograms (FPFH), the Sample Consensus Initial Alignment (SAC-IA) and Iterative Closest Point (ICP) algorithms were utilized to accomplish coarse and accurate registration of the point clouds, respectively, ultimately achieving 3D reconstruction. Experimental results demonstrated that the reconstructed 3D model of the tomato plant was clear in outline and complete in structure. For the phenotypic parameters of plant height, canopy width, and leaf angle, the coefficients of determination (R²) between the calculated and manually measured values were 0.98, 0.94, and 0.89, respectively, with Root Mean Square Errors (RMSE) of 0.75 cm, 1.10 cm, and 4.43 °. Compared to single-view measurements, the accuracy of plant height and maximum canopy width derived from multi-view reconstruction increased by 15.31% and 13.12%, respectively. This method provides technical support for the rapid and accurate extraction of phenotypic parameters in tomato plants.
Why it matches plant phenotyping methodsトマトの草丈、キャノピー幅、葉角を抽出するマルチビュー3D点群再構成法を開発し、手動測定との精度検証も行っており、植物表現型取得が研究の中心である。
abstractthis study proposes a detection method for tomato plant phenotypic parameters based on multi-view 3D point cloud reconstruction.
Tree barriers are the key factors of current transmission line failures, early discovery of tree barriers hidden dangers, the implementation of forest clearing tasks on transmission lines is the current power inspectors need to pay attention to the key issues. The article is based on the inclined photogrammetry technology to obtain the three-dimensional data of transmission lines, and construct a real-time database to realize the standardized management of three-dimensional data. Then the Mean Shift algorithm is used to preprocess the remote sensing data, and the forest diameter measurement system and transmission line forest clearing program are designed. QDN Power Supply Bureau was selected as a research sample to verify the effectiveness of the application of the above methods. The study showed that the RMSE of the breast diameter monitoring results ranged from 4.30% to 5.05%, and the reduction of forced outage rate of transmission lines of 110kV and above in the power grid could be up to 72.28%, and the overall work efficiency was improved by about 5.14 times. Therefore, actively realizing the optimization of transmission line forest clearing tasks can ensure the stable operation of transmission lines and provide basic support for ensuring the power supply of the grid.
Why it matches plant phenotyping methods傾斜写真測量とMean Shift処理により樹木の胸高直径を推定する測定システムを構築し、RMSEで検証しており、植物形質の取得・評価が実質的な方法貢献として含まれる。
abstractThe article is based on the inclined photogrammetry technology to obtain the three-dimensional data of transmission lines, and construct a real-time database to realize the standardized management of three-dimensional data.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 7 Sept 2026
Recent advancements in 3D reconstruction technologies have significantly transformed plant phenotyping, enabling precise, scalable, and automated trait extraction. Traditional manual phenotyping methods are increasingly being replaced by image-based approaches, such as photogrammetry, LiDAR, RGB-D sensing, and deep learning (DL)-based techniques. These tools allow for non-destructive, high-throughput measurements of plant morphology, structure, and physiological traits. This review synthesizes the state of the art in 3D reconstruction methods, including conventional geometric algorithms and emerging DL methods, and evaluates their application across diverse plant species. In addition, we discuss the sensing modalities, evaluation metrics, and crop-specific deployments. Although promising, current technologies still face challenges in terms of computational efficiency, scalability to outdoor environments, and generalizability across crop types. This review concludes by identifying research gaps and future directions for making real-time, field-deployable 3D phenotyping systems.
Why it matches plant phenotyping methods植物フェノタイピングにおける3D再構成技術と形質抽出を主題とする方法論レビューであり、評価指標やセンサー、応用を体系的に扱っている。
abstractThis review synthesizes the state of the art in 3D reconstruction methods
Monitoring the growth dynamics in field-grown cabbage is critically important for ensuring stable vegetable production and advancing precision agricultural management. However, conventional two-dimensional (2D) image-based monitoring approaches are limited to planar projection information and lack representations of spatial structural characteristics, rendering them inadequate for supporting high-precision, full-cycle phenotypic monitoring of cabbage under open-field conditions. In this study, a high-precision three-dimensional (3D) point cloud dataset covering the period from the seedling stage to maturity was constructed using depth cameras in conjunction with multi-view spatial registration techniques. Building on this dataset, an adaptive point cloud segmentation network designed for the whole-cycle growth monitoring was proposed, incorporating a Head Refinement Module (HRM), a Leaf Instance Segmentation Module (LISM), and Cross Module Interaction (CMI) to address leaf adhesion and head boundary delineation. Experimental results demonstrated that the proposed method consistently outperformed state-of-the-art models in both semantic and instance segmentation tasks. For semantic segmentation, the mean Intersection over Union (mIoU) reached 0.767, with a point classification accuracy of 94.8%. The model comprises 54.25 million parameters and achieves an average response time of 0.76 s. For instance segmentation, the Average Precision (AP) improved by 2.3% for cabbage heads and 3.8% for leaves, while the Average Recall (AR) increased by 6.9%. Growth parameters, including plant height and canopy spread, extracted from the segmentation results showed strong agreement with ground-truth measurements, with correlation of coefficients (R 2 ) exceeding 0.9 for plant height, canopy length, and canopy width. Leveraging these multidimensional phenotypic descriptors, the temporal dynamics of cabbage growth throughout the entire growth cycle were systematically characterized. Overall, this study enables dynamic monitoring of cabbage phenotypes across the full growth cycle, providing a novel technical pathway for extending 3D phenotyping from controlled environments to open-field applications and offering important support for precise crop monitoring and the development of digital twin agriculture.
Why it matches plant phenotyping methods3D点群データセット、セグメンテーションネットワーク、形質抽出を開発・検証し、圃場キャベツの草高や冠幅を定量化する植物フェノタイピング手法が研究の中心である。
abstracta high-precision three-dimensional (3D) point cloud dataset covering the period from the seedling stage to maturity was constructed using depth cameras in conjunction with multi-view spatial registration techniques.
Reproduction assets foundThe paper's authors publicly release their improved OneFormer3D point cloud segmentation code on GitHub; the cabbage 3D point cloud dataset is only available upon request.Code · publicThe code is available at https://github.com/PandaDalin/improve_oneformer3d. The data of this study are available from the corresponding author upon request.Open asset ↗PandaDalin/improve_oneformer3dhtml-lines:449-475Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
Abstract. Vegetation in dryland ecosystems often exhibits spatial patterning that leads to the formation of fertile islands. Discrete patches accumulate nutrients, organic litter, seeds and water, slow down geomorphodynamic dispersion processes and contrast with rather barren interpatch areas. While this fragmentation shapes surface dynamics in dryland environments across the globe, their spatial arrangement remains difficult to quantify at fine scales. In this study, we utilised data collected by an uncrewed aircraft system (UAS) together with field data to study surface patterns in degraded Argania spinosa forests in South Morocco that show fertile island dynamics. Point clouds generated from UAS imagery using a Structure-from-Motion photogrammetric workflow were classified into vegetation and ground points, allowing the derivation of digital terrain and digital surface models as well as conventional orthophotos and artificial ground-only orthophotos. This high-resolution geospatial data was used to map tree-influenced soil surface areas and crown areas. Their size and spatial relationships were compared and complemented by a detailed assessment of tree morphologies and terrain characteristics based on both field observations and UAS-based geodata. Spatial and statistical analyses were conducted to study the effects of tree morphology, hillslope wash, shading and wind on emerging surface patterns beneath Argania spinosa trees. Across the 496 evaluated tree-influenced areas, the surface influence extended on average to 1.69 times the size of the crown covered area. The extent of this influence beyond the canopy cover is strongly controlled by tree size and morphology, indicating that browsing-induced degradation influences not only tree conditions but also the spatial extent of positive surface effects in the interpatch area. The tree-influenced areas exhibit a consistent north-east displacement, a pattern that surprisingly appears largely decoupled from hillslope wash and is most reasonably explained by a combined influence of shading and wind effects. The results of this study demonstrate the potential of UAS imagery to complement fertile island research by providing spatial insight beyond conventional field-based assessments. At the same time, the impact of browsing on surface dynamics in a UNESCO biosphere reserve is highlighted as a factor contributing to degradation in this silvopastoral land-use system.
Why it matches plant phenotyping methodsUAS-SfM画像から樹冠面積、樹体形態、樹木影響域を抽出するワークフローが研究の中心で、個体レベルの植物形態・状態を定量化しているため。
abstractPoint clouds generated from UAS imagery using a Structure-from-Motion photogrammetric workflow were classified into vegetation and ground points, allowing the derivation of digital terrain and digital surface models as well as conventional orthophotos and artificial ground-only orthophotos.
Objectives: Multispectral imaging and spectrophotometry are widely used to estimate plant physiological characteristics, yet the literature remains fragmented across sensors, indices, and analytical approaches. Methods: This systematic review followed PRISMA 2020 and was preregistered in OSF (Open Science Framework). Web of Science, Scopus, Google Scholar, and Consensus were searched up to January 2025 for peer-reviewed studies and selected gray literature studies focused on plant physiological trait estimation using multispectral or spectrophotometric methods. From 256 identified records, 96 studies met the eligibility criteria. Methodological quality was assessed across five domains, and results were synthesized narratively owing to high heterogeneity. Results: A total of 96 studies met the eligibility criteria. Among these, multispectral sensors were the most commonly used (40.7%), followed by UAV-mounted platforms (25.9%), while hyperspectral sensors accounted for 18.5% of the studies. The most frequently used vegetation index was NDVI, reported in 87% of the studies, mainly for estimating vigor, biomass, and canopy structure. Discussion: Although multispectral indices reliably capture key agronomic traits, cross-study comparability is currently hampered by significant methodological variability and a lack of consistent validation protocols. Conclusions: Multispectral imaging and spectrophotometry are effective tools for estimating plant physiological traits, but greater standardization is needed across studies. Owing to the limited number of studies on Stenocereus spp., the review was expanded to plants in general; the shortage of reports addressing Stenocereus spp. highlights the need for future research in these species.
Why it matches plant phenotyping methods植物生理形質推定のためのマルチスペクトル画像・分光法を対象とした系統的レビューであり、方法の比較、品質評価、検証標準化を中心に扱っている。
abstractThis systematic review followed PRISMA 2020 and was preregistered in OSF (Open Science Framework).
Abstract 3D models are used in plant phenotyping for non-destructive quantification and analysis of morphological characteristics. Analyzing plant structure allows breeders to select for desirable traits, associated with e.g. drought tolerance or increased productivity. In sugar beet, morphological parameters depict an essential element of the variety approval for distinguishing between genotypes. However, only a limited number of measured or scored parameters are considered at a single time point. In contrast, 4D data adds a temporal component and can depict the dynamic development of 3D parameters. To explore the potential of spatio-temporal 4D phenotyping for automated crop genotype differentiation, a greenhouse experiment was conducted by us covering twelve sugar beet genotypes. High-resolution 3D models were generated twice a week over the course of two months and both common and novel 3D morphological parameters were extracted. The importance of these parameters was assessed by us, and the dataset was analyzed using unsupervised pointwise clustering and time series clustering. Varying importance of parameters depending on the time point and significantly higher importance of plant parameters compared to leaf parameters are demonstrated by our results. Moreover, increased and more stable genotype differentiation is archived using time series clustering compared to pointwise clustering. Furthermore, taproot formation of sugar beet was found to have a crucial impact on morphological development. Substantial variations in the dynamic development of 3D morphological parameters underline the importance of 4D data for plant genotype differentiation. Thus, a novel foundation for genotype differentiation in plant phenotyping is provided by our findings.
Why it matches plant phenotyping methods3Dモデルから植物形態形質を抽出し、時系列クラスタリングで遺伝型識別を評価する4Dフェノタイピング手法が研究の中心である。
titleSpatio-temporal 4D phenotyping for automated morphological genotype differentiation of sugar beet
Reproduction assets foundThe paper publicly deposits its generated sugar beet point cloud dataset under CC BY 4.0 at a Dataverse DOI, directly reproducing the paper's phenotyping measurements. Supplementary Python codes and extracted parameter values are stated to be included with the article, but no authors' public URL for the code is presentDataset · publicThe generated point cloud dataset is available at https://doi.org/10.60507/FK2/IS8YBZ under CC BY 4.0 license.Open asset ↗10.60507/FK2/IS8YBZlines:277-363Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Accurately simulating shoot-scale light scattering in physically based radiative transfer models remains a key challenge for conifer ecosystems. This study evaluates the high-resolution three-dimensional (3D) radiative transfer capability of the Discrete Anisotropic Radiative Transfer (DART) model using laboratory reflectance measurements and detailed photogrammetric reconstructions of Norway spruce ( Picea abies (L.) H. Karst) shoots. Samples representing multiple age classes and crown positions were collected from temperate (Czech Republic) and hemiboreal (Estonia) Norway spruce stands. Their geometry was reconstructed with sub-millimetre accuracy using structured blue-light 3D scanning, while the optical properties of needles and twigs were measured using an integrating sphere. We measured shoot reflectance under controlled laboratory illumination and compared it to DART simulations based on the identical 3D structures and optical inputs. DART simulations accurately reproduced the measured spectral signatures (R 2 = 0.95; median spectral angle mapper = 4.8°), demonstrating the model's capacity to simulate shoot-scale reflectance across diverse viewing geometries. These results suggest that detailed 3D shoot representations can improve radiative transfer modelling accuracy, and that DART efficiently simulates shoot reflectance across diverse viewing geometries as an alternative to labour-intensive goniometer measurements. This work provides the first empirical evaluation of DART at the shoot-scale and establishes a transferable framework for integrating detailed 3D photogrammetry into radiative transfer modelling. This approach enables more accurate upscaling from the conifer needle to the canopy-level and can enhance future model intercomparison exercises, such as the Radiation Transfer Model Intercomparison benchmark. • First empirical validation of DART simulation of conifer shoots. • High-resolution blue-light photogrammetry captures realistic shoot architecture. • DART-simulated reflectance closely matches laboratory measurements. • Framework enables realistic needle-to-canopy upscaling in radiative transfer models.
Why it matches plant phenotyping methods針葉樹シュートの3D構造を高精度に取得し、反射率モデルを実測値で検証する技術研究であり、植物形質（シュート構造・反射特性）の取得とモデル評価が中心である。
abstractTheir geometry was reconstructed with sub-millimetre accuracy using structured blue-light 3D scanning
This study compared 3D point cloud data derived from Structure-from-Motion (SfM) in 2021 and lidar in 2022 acquired using remotely piloted aircraft systems (RPAS). The overall objective was to develop and compare optical and active point cloud methods for deriving vegetation structures commonly measured in the field to quantify wildfire fuel distribution. The outcomes of the modelling framework were then applied to examine the impacts of mountain pine beetle (MPB) on canopy fuel load volumes in Jasper National Park prior to a high intensity wildfire in 2024. Tree species were classified using geographic object-based image analysis (GEOBIA) with an overall accuracy of ∼ 90%, with higher performance in relatively open canopies with minimal shadow. Photogrammetric and lidar point clouds resolved accurate individual tree height (R 2 = 0.96; 0.99, respectively) when compared to field measurements. Crown base height derived using a windowed point density approach improved agreement with field data (R 2 = 0.76; 0.91, respectively) and improved relative to previously reported methods. Across sites with varying MPB-induced tree mortality, plots dominated by dead conifers showed a redistribution of canopy fuels towards the ground compared to plots of mostly live conifers. This structural shift suggests increased ladder fuel development, reduced canopy continuity, and a heightened likelihood of surface to crown fire transition. The results demonstrate that RPAS point clouds can effectively characterize tree structure and improve crown base height estimation, supporting more accurate assessment of canopy bulk density. These measurements provide a viable alternative to labour-intensive field surveys and can then be used as calibration and validation data for broad-area forest assessment fuel modelling using airborne and satellite remotely sensed data.
Why it matches plant phenotyping methodsRPASのSfMおよびLiDAR点群を用いて樹高、樹冠基部高、林冠燃料構造を抽出し、現地測定と比較・検証している。個体・林分の植物構造測定法が研究の中心である。
abstractThe overall objective was to develop and compare optical and active point cloud methods for deriving vegetation structures commonly measured in the field to quantify wildfire fuel distribution.
Optimizing olive orchard management requires timely, per-tree data to enhance productivity and sustainability. Unoccupied aerial vehicle (UAV)-based red, green, and blue (RGB) imagery offers a low-cost solution for acquiring high-resolution spatiotemporal insights for orchard management, which are not yet common in Tunisia. This study monitored tree structural parameters, leaf area index (LAI), and leaf nitrogen content (%N DW) in two Tunisian olive orchards during 2022 and 2023. UAV-derived imagery was photogrammetrically processed into 3D point clouds and analyzed using an automated approach. Target variables of the automated approach included tree-wise estimates of height, projected crown area, and crown volume, as well as raster cell counts of the canopy cloud and spectral indices such as the normalized green-red difference index (NGRDI) and green leaf index (GLI). In addition, the estimated parameters per tree were used to model LAI and leaf nitrogen content. Analyses were conducted separately for trees represented by a high and a low number of points in the dense point cloud. Outcomes were compared to reference data collected in the field on dates close to the UAV flights. The findings showed strong relationships for the projected crown area (R2 = 0.82 and 0.91) and tree height (R2 = 0.89 and 0.88) when compared to reference values. Linear regression models for LAI (R2 = 0.73 and 0.68) and crown volume (R2 = 0.85 and 0.91) estimation also show strong relationships. However, leaf nitrogen estimation was not feasible from RGB spectral index values, as it showed a weak relationship (R2 = 0.34). A dataset with multispectral imagery could overcome this limitation but would increase costs, making it less suitable for the low-budget approach required in price-sensitive farming contexts, particularly in low-income regions.
Why it matches plant phenotyping methodsUAV画像から樹体形状・LAIなどの植物形質を自動推定し、圃場基準値との比較検証を行う手法が研究の中心である。
abstractUAV-derived imagery was photogrammetrically processed into 3D point clouds and analyzed using an automated approach.
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:415-421Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Salinity stress in coastal areas threatens the stability of rice production in Indonesia, necessitating innovative breeding strategies to adapt to this stress. In breeding, screening methods are crucial to improve selection effectiveness. One approach is pot selection on saline soil. However, this concept requires a precise approach, so integrating image-based phenotyping (IBP) screening and validation by physiological traits provides a rapid and effective approach to assessing salinity tolerance in rice genotypes. This study aimed to identify robust IBP traits for pot salinity screening and validate them through physiological response patterns among rice genotypes under salinity conditions. Six rice genotypes were evaluated under normal and saline environments using artificial pot trials. IBP traits related to plant geometry were quantified and complemented with physiological indicators, including Na⁺/K⁺ balance, chlorophyll pigments, and proline accumulation. Based on the result, perimeter and ferret were identified as effective IBP selection criteria. Both criteria captured differences in osmotic regulation and photosynthetic performance under salinity stress. Principal component analysis clearly separated tolerant, moderately tolerant, and sensitive genotypes, with geometric traits contributing most strongly to genotype discrimination. It supported a bit of physiological responses, which revealed distinct tolerance patterns. Tolerant genotypes (Pokkali, HS4.15.1.70, and HS4.15.2.4) maintained better Na⁺/K⁺ balance, lower chlorophyll loss, and adaptive proline responses, while sensitive genotypes (IR 29 and Ciherang) showed pronounced ionic imbalance and chlorophyll reduction; HS4.45.1.66 exhibited intermediate responses. The integration of IBP and physiological traits offers a practical framework for high-throughput salinity screening.
Why it matches plant phenotyping methods画像ベース表現型形質を定量化し、塩分耐性スクリーニングの選抜基準として検証することが中心である。生理形質による妥当性検証も含む。
abstractThis study aimed to identify robust IBP traits for pot salinity screening and validate them through physiological response patterns among rice genotypes under salinity conditions.
Conventional approaches to measuring stomatal conductance (gs) and transpiration often rely on instruments that interfere with plant physiology. Porometers, for example, restrict natural leaf movement, apply pressure, and introduce dry airflow that can alter stomatal behaviour, thereby reducing the relevance of such measurements. Prior studies report discrepancies among devices attributable to such interferences (Toro et al. 2019). To minimise artefacts, transpiration should be estimated remotely without physical contact, which theoretically can be achieved via a thermal leaf energy-balance approach that infers gs from leaf temperature, radiative load, and boundary-layer terms. In this study, we combine 3D plant models, light interception models, and thermal imaging to solve the energy-balance equation of individual leaves, estimating transpiration entirely remotely. Approaches to estimate stomatal conductance based on the energy-balance equation were developed recently to aid phenotyping of plantss. Most methods either imposed rapid changes in air humidity to perturb transpiration and, consequently, leaf temperature (Driever et al. 2023), or relied on ‘dry’ and ‘wet’ reference surfaces (as in Leinonen et al. 2006) to compute stress indices (Vialet-Chabrand and Lawson 2020). These methods require reference materials to assess surface temperatures under maximum and zero transpiration, showing the effect of longwave radiation. However, reference-material methods were constrained by heterogeneity in light interception caused by variation in leaf angle and orientation, because reference surfaces could not reorient like real leaves (Zhang et al. 2025). In this study, we addressed this challenge by using thermal imaging and 3D photogrammetry to capture leaf temperature and geometry noninvasively, allowing parameter estimation for each leaf individually. Here, ρ is the density of air (kg m−3), cp is the specific heat capacity of air (J kg−1 K−1) and rHR is the parallel resistance to heat and radiative transfer on the leaf surface (s m−1), s is the slope of the curve relating saturating water vapour pressure to temperature (Pa °C−1). TL and TA are leaf and air temperatures (°C), respectively, δe is air vapour pressure deficit (Pa), γ is the psychrometric constant (Pa K−1) and rva is the boundary layer resistance to water vapour (s m−1) (Supporting Information S2: Equation S1). The net radiative energy Rn in the energy-balance term was obtained from the same 3D light interception model, which integrates measured direct and lateral scattered irradiance (W m−2) (Supplement Material and Methods, File S2). Stomatal conductance gs (m s−1) is the inverse of stomatal resistance rs (s m−1). To experimentally obtain a wide range of gs values, we grew eggplant (Solanum melongena L.) plants in hydroponic units in growth chambers under four sets of environmental conditions (Table 1). Thirty-day-old plants (4–5-leaf stage) were placed on balances (Supplementary Materials and Methods, File S2). Units were sealed with plastic film to minimise evaporation. Mass loss attributable to transpiration was logged automatically every 30 s. To induce short-term changes in stomatal conductance, we imposed an acute osmotic stress by delivering a saline NaCl solution with high electrical conductivity (60 mS cm−1) to the root zone, producing a steep drop in root osmotic potential. This created rapid physiological and morphological responses that altered incident irradiance at the leaves, leaf temperature, and consequently energy balance, stomatal conductance and transpiration. We chose this stressor for operational simplicity. Any perturbation that modifies transpiration dynamics and thus gas exchange could have served our purpose. The total transpiration of a leaf, Et (kg s−1), is the product of the total conductance to water vapour from the mesophyll to the atmosphere, gv (m s−1), calculated from the estimated stomatal resistance rs (s m−1) and the boundary layer conductance gva (m s−1), the difference between water vapour concentration inside the leaf Cvs (dimensionless), and in surrounding air Cva (dimensionless), the leaf area A (m2), and the density of water ρw (kg/m3) (Jones 1992). Estimated stomatal conductance was obtained from leaf energy balance calculation (Equation 1). Boundary-layer conductance was computed from measured wind speed and leaf dimensions (leaf area, length, width) extracted from structure-from-motion 3D reconstructions (Supporting Information S1: Equation S6; Grace et al. 1980). Transpiration was then calculated for each leaf at each thermal 3D imaging time point, and whole-plant transpiration for comparison with gravimetric logs was the sum of all per-leaf estimates. As a non-invasive approach, we evaluated the plausibility or our model derived stomatal conductance (Equation 2) indirectly by comparing calculated and measured whole plant transpiration. We emphasise that this is not a direct validation of gs. Rather, the close agreement between modelled and measured transpiration across the wide range of environmental treatments, both stressed and non-stressed, provides confidence that the inferred gs is realistic. RGB and thermal images acquired before, during, and after stress application enabled dynamic tracking of leaf position and temperature (Supplementary Material and Methods, File S2). As expected, osmotic stress application had immediate effects on morphology and physiology. While control leaves maintained an angle of around 110° throughout, osmotic shock induced immediate turgor loss and drooping in all environments except one (Figure 1A,B). Leaf angles recovered to pre-stress positions within 1 h, indicating adaptation to the osmotic shock and restoration of turgor. Only environment 4 (high light, low air temperature and low humidity) maintained turgor during stress. Angle shifts were most pronounced in older leaves, which drooped and reduced light interception; younger leaves better preserved structure and turgor (Supporting Information S1: Figure S2). These angle changes also altered incident irradiance at the leaf surface (Supporting Information S1: Figure S3). These morphological responses coincided with increases in leaf temperature, consistent with altered water fluxes and stomatal regulation after stress. Across environments, plants showed a uniform rise in leaf temperature following osmotic stress, regardless of initial temperature (Supporting Information S1: Figure S4). This response held across leaf ages, encompassing older (Figure 1C) and younger (Figure 1D) leaves. Stomatal conductance estimated with our method followed the same pattern, dropping rapidly after osmotic shock in both older (Figure 1E) and younger (Figure 1F) leaves (Supporting Information S1: Figure S5). We estimated no stomatal conductance recovery to pre-stress conditions over the time course of stress exposure. Model-estimated and gravimetrically measured transpiration showed identical time courses across all four environmental conditions (Figure 1G–J). Transpiration rates did not recover to the same extent as leaf turgor, indicating long-term effects of the osmotic shock. Across environments and time points, correlation between model estimated and measured whole-plant transpiration was high (Figure 1K). In this study, stomatal conductance (gs) is a model-derived quantity inferred from the same physically constrained framework and model (leaf temperature, boundary-layer conductance and vapour pressure deficit). Since we did not measure gs directly, we cannot validate gs directly. Instead, we used a non-invasive check via transpiration. Model predictions closely tracked measured transpiration across the four controlled environments. This agreement increases confidence that the inferred gs is realistic, while we acknowledge that transpiration agreement alone is not a rigorous validation and cannot fully rule out compensating errors. Our study demonstrated the potential of our approach to estimate transpiration accurately by combining 3D imaging and thermography with physiological modelling without the use of reference materials that imitate real leaves. This remote approach enables simultaneous assessment of morphological and physiological responses to stress, yielding a more integrated view on plant transpiration and gas exchange. In contrast to chamber and porometer measurements or IR methods requiring wet and dry references or calibration plates, our workflow is reference-free. Absorbed shortwave radiation is derived from measured irradiance and a 3D reconstruction of leaf geometry, with no external reference materials. Moreover, remote measurements avoid continuous pressure from clamp-on porometers, permitting long-term observation and capture of rapid stress responses without sustained damage or microclimate artifacts. Further, the approach is not limited by any clamp on sensors and as such enables multi-leaf tracking. Applied to crop canopies, this approach could improve understanding of canopy processes that influence productivity and enable remote estimation of canopy transpiration. Future research could further improve by replacing our strong saline solution stress by gradual soil drying to depict a more realistic and natural stress while testing the approach under long-term conditions. Recent studies indicate that, with rising atmospheric CO2 concentrations, breeding for reduced stomatal conductance could increases WUE without affecting photosynthetic capacity (Srivastava et al. 2024). As such, remote systems for high-throughput plant phenotyping (HTP) are required to scan vast quantities of plants. We see a potential use of our system for such purposes to quickly estimated whole plant and individual leaf transpiration, as initial image capturing is very fast. A large bottleneck in our work was 3D model generation speed and manual extraction of leaf parameters from these 3D models. Both could be streamlined with more automated software, possibly including neural network solutions. The authors have nothing to report. The authors declare no conflict of interest. The data that support the findings of this study are available from the corresponding author upon reasonable request. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
Why it matches plant phenotyping methods3D画像、熱画像、光遮断モデル、エネルギーバランスモデルを統合し、葉ごとの蒸散と気孔コンダクタンスを非侵襲的に推定する手法を開発・評価しており、植物表現型取得が研究の中心である。
abstractIn this study, we combine 3D plant models, light interception models, and thermal imaging to solve the energy-balance equation of individual leaves, estimating transpiration entirely remotely.
This paper integrated multimodal remote sensing (RS) data with deep learning to develop a maize growth analysis and income prediction model based on CNN (Convolutional Neural Network)-Attention and Bi-LSTM (Bidirectional Long Short-Term Memory). Utilsing Landsat 8, Sentinel-2, and Sentinel-1 satellite data, the CNN-Attention network extracts vegetation features such as NDVI (Normalised Difference Vegetation Index), EVI (Enhanced Vegetation Index), and canopy density for corn growth stage identification and prediction. The study combined these features with meteorological, soil, and market data and fed them into a Bi-LSTM model for time series analysis to forecast corn income. The method achieved 98.3% accuracy in growth stage classification, with an RMSE (Root Mean Squared Error) of 0.04 and R² of 0.94 for canopy coverage prediction under 10-fold cross-validation, and an RMSE of 0.13 and R² of 0.94 for income prediction, showing high stability over time. Overall, this research supports data-driven precision agriculture through real-time monitoring and reliable economic forecasting.
Why it matches plant phenotyping methods衛星リモートセンシングと深層学習による作物生育段階・キャノピー密度の推定手法が中心的に開発・検証されており、植物状態の定量化を含むため。収益予測も扱うが、植物フェノタイプ推定部分が明示されている。
abstractintegrated multimodal remote sensing (RS) data with deep learning to develop a maize growth analysis and income prediction model
Wood apple (Feronia limonia L.) is an underutilized perennial fruit tree with substantial ecological, nutritional, and economic potential, yet its phenotypic diversity and trait organization remain poorly characterized. Here, we applied a mixed-scale multivariate framework to resolve phenotypic structure in 62 wood apple genotypes using 31 ordinal and categorical vegetative, leaf, floral, fruit, and seed descriptors. Trait interrelationships were examined through the complementary use of Spearman’s rank correlation and Cramér’s V association analyses, capturing both directional rank-based dependencies and scale-independent categorical linkages. Hierarchical clustering based on Gower distance separated the genotypes into three distinct phenotypic clusters, with inter-cluster dissimilarities (0.92–1.18) consistently exceeding intra-cluster variation (0.42–0.55), indicating well-supported phenotypic stratification based on cluster validation. Multiple Correspondence Analysis (MCA) explained 23.30% of total inertia across the first two dimensions, with tree growth habit, branch angle, tree shape, and fruit color emerging as the principal drivers of phenotypic differentiation. Vegetative and leaf traits formed a tightly integrated module, whereas fruit-related traits displayed weaker monotonic but persistent categorical associations, reflecting partial phenotypic independence. The strong concordance among association analyses, clustering, and MCA indicates structured patterns of coordinated and partially independent trait associations in wood apple. Overall, this study demonstrates the effectiveness of mixed-scale multivariate approaches for resolving complex trait architecture in underutilized perennial fruit crops and provides a quantitative phenotypic framework to support germplasm conservation, parent selection, and ideotype-oriented improvement of wood apple.
Why it matches plant phenotyping methods混合尺度の多変量解析を用いて植物遺伝資源の表現型構造を定量化する手法が研究の中心であり、単なる生物学的実験の routine 測定ではない。
abstractHere, we applied a mixed-scale multivariate framework to resolve phenotypic structure in 62 wood apple genotypes using 31 ordinal and categorical vegetative, leaf, floral, fruit, and seed descriptors.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicAll data generated or analyzed during this study are available in the article and the accompanying Supplementary Table S1.Open asset ↗lines:137-161Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 5 Sept 2026
Published20 Apr 2026Chemical and Biological Technologies in AgricultureCited by 1 · OpenAlex ↗
Biological control represents a valuable tool for the sustainable management of soil-borne diseases in tomato cultivation and relies on the availability of effective microbial solutions. Digital technologies can support to the ecodesign stage by accelerating the screening and selection of high-performing microbial biocontrol agents. In this study, a collection of eleven endophytic bacteria strains recruited from the tomato root endosphere and proved to be compatible with Trichoderma spp. (non-target effect), was characterized for antagonistic and biofertilization/biostimulant traits, and evaluated in planta against two major tomato pathogens: Fusarium oxysporum f. sp. lycopersici and Sclerotium rolfsii. Plant phenomics realized using the PlantEye 500 multispectral dual scanner, was used to screen the effectiveness of microbial agents determining plant performances under both infection and healthy conditions. Multivariate analysis of 20 digitally computed phenotypic traits helped the detection of Peribacillus sp. C5NA and Neobacillus sp. TR12 as highly effective against wilt, and capable of counteracting the reduction in leaf angle surface and chlorophyll: typical tracheofusariosis symptoms. On the other hand, Peribacillus strains TR2 and C6 treatments caused partial phenotypic recovery in plants affected by Sclerotium rot. Interestingly, Microbacterium sp. TR9, appeared to be multifaceted. It showed mild multisuppressivity against both pathogens coherently with the exhibited N-acetyl-b-glucosaminidase, polysaccharide breaking and in vitro antifungal activities. In addition, it also acted as a putative biostimulant in the absence of pathogens, increasing digital biomass, plant height, and NDVI, in line with its proven strong ability to produce ammonia, fix nitrogen, solubilize phosphates, and release indoleacetic acid. Overall, the integration of phenomics supported the high-resolution detection of plant responses and supported the identification of multifunctional microbial strains with biocontrol and biofertilization potential for sustainable tomato production.
Why it matches plant phenotyping methodsPlantEye 500によるマルチスペクトル表現型取得と20形質のデジタル解析が、微生物資材のスクリーニングおよび植物応答評価の中心的手法として用いられているため。
abstractPlant phenomics realized using the PlantEye 500 multispectral dual scanner, was used to screen the effectiveness of microbial agents determining plant performances under both infection and healthy conditions.
BACKGROUND: Leaf inclination angle (LIA) is a key trait affecting crop canopy structure and photosynthetic efficiency, but its accurate measurement is challenging due to complex leaf geometry, especially in narrow, curved rice leaves. As the flag leaf serves as the primary photosynthetic organ in rice, the precise spatial parsing of its architecture is crucial for optimizing canopy light interception and yield potential. With the rapid development of high-throughput phenotyping technologies, an increasing number of studies have focused on the fine-grained characterization of 3D crop architecture. However, accurate methodologies for extracting the flag leaf inclination angle (FLIA) in rice, as well as systematic investigations into its spatiotemporal variation patterns, remain largely unexplored. RESULTS: In this study, we systematically evaluated multiple plane-fitting strategies based on SfM-MVS point clouds, finding that voxel-based piecewise analysis outperformed traditional global approaches. To further improve accuracy, skeleton extraction methods were innovatively extended to LIA estimation. A proposed multi-method ensemble, based on the median of eight skeleton extraction combinations, yielded high robustness (R2 = 0.923, RMSE = 2.072°) against photographic ground truth. By applying the proposed framework to both field- and pot-grown rice, we observed no significant FLIA differences between varieties or nitrogen treatments under field-grown conditions, likely due to phenotypic plasticity regulated by population effects. However, pot-grown plants, experiencing reduced interplant competition, exhibited significant varietal differences in FLIA. Across growth environments, varieties, and nitrogen treatments, FLIA at maturity was significantly lower than at anthesis and grain filling stages due to leaf senescence. CONCLUSIONS: This study establishes a robust and accurate measurement framework for LIA based on 3D point clouds, improving estimation performance through piecewise analysis, voxelization, and ensemble strategies. The proposed approach is demonstrated to be an effective tool for the precise quantification of rice leaf phenotypes.
Why it matches plant phenotyping methodsSfM-MVS点群からイネ葉の傾斜角を抽出する手法を開発・検証し、圃場および鉢植えで適用しているため、植物フェノタイピング手法が研究の中心である。
abstractA proposed multi-method ensemble, based on the median of eight skeleton extraction combinations, yielded high robustness (R2 = 0.923, RMSE = 2.072°) against photographic ground truth.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe python program, complete dataset, including the original two-dimensional images and corresponding piecewise measurement trajectories, is publicly available at https://github.com/Interstingsun/LIA (accessed on 6 February, 2026).Open asset ↗Interstingsun/LIAlines:77-83Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
• Depth informed trunk detection performed reliably under field conditions. • Trunk diameter estimates aligned closely with ground truth. • Transmission-based digital odometer provided reliable along-row positioning. • Full-block mapping demonstrated large-scale applicability. Trunk diameter is an important structural trait used to assess tree size, vigor, and long-term growth in orchard systems, but it remains difficult to measure efficiently at orchard scale. This study developed and validated a low-cost mobile imaging system for automated trunk diameter estimation and spatial mapping in tart cherry (Prunus cerasus) orchards. The system integrated RGB-D cameras, deep learning-based trunk detection and segmentation, three-dimensional depth reconstruction, and driveline-based digital odometry for spatial positioning under canopy conditions where GNSS performance was unreliable. Trunks were detected using a YOLO-based model, segmented using a Segment Anything Model (SAM)-based approach, and reconstructed in 3D from depth data to estimate real-world trunk diameter at 40 cm above the trunk base. The system was evaluated in a 15-year-old, 1 ha experimental orchard in Utah, USA, and then applied in a 14-year-old, 9.5 ha commercial orchard. Under unobstructed viewing conditions, trunk diameter estimates showed strong agreement with manual measurements, with a mean absolute error (MAE) of 0.95 cm, a root mean square error (RMSE) of 1.16 cm, and R 2 of 0.79. Across all 462 trees, automated per-tree averaging produced an MAE of 1.40 cm. In the commercial orchard, mapped trunk diameter patterns aligned with UAV-derived canopy height, reflecting underlying zones of tree vigor. These results show that mobile RGB-D imaging combined with driveline-based odometry can provide practical, cost-effective orchard-scale trunk diameter mapping under commercial field conditions.
Why it matches plant phenotyping methodsRGB-D画像、深度再構成、物体検出・セグメンテーション、オドメトリを統合し、樹幹径という植物構造形質を自動推定・空間マッピングする手法を開発・検証しているため。
abstractThis study developed and validated a low-cost mobile imaging system for automated trunk diameter estimation and spatial mapping in tart cherry (Prunus cerasus) orchards.
Diameter at Breast Height (DBH) is a key parameter in forest measurement. However, existing research has mostly focused on improving the accuracy of individual technologies, lacking a systematic synthesis of the evolutionary logic of measurement techniques and a standardized selection framework for forestry applications. To this end, this paper constructs a multi-level classification framework based on measurement platforms and technical principles, establishes for the first time a five-dimensional comprehensive evaluation system (covering accuracy, efficiency, cost, environmental adaptability, and automation) along with a hierarchical technology decision tree, and systematically analyzes the application logic of multi-source fusion technologies across three levels: ground-based, near-ground mobile, and aerial. The review indicates that traditional contact-based measurement has limited efficiency; modern remote sensing technologies (photogrammetry and LiDAR) offer significant advantages in automation and accuracy, but still face challenges such as high equipment costs, complex data processing, and poor environmental adaptability. Multi-source fusion and machine learning are key methods to overcome the limitations of single sensors and improve the robustness of DBH estimation. Finally, it is anticipated that with decreasing sensor costs and the advancement of intelligent algorithms, DBH measurement will continue to evolve toward automation, intelligence, and engineering practicality, providing technical support for large-scale, long-term, and repeatable forest monitoring.
Why it matches plant phenotyping methods森林樹木のDBHという明示的な植物形態形質の測定技術を対象に、測定原理の分類、評価体系、意思決定木、リモートセンシングと機械学習の比較を体系化した方法論レビューであり、フェノタイピング手法が中心である。
abstractDiameter at Breast Height (DBH) is a key parameter in forest measurement.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Introduction Accurate recognition of plant pests and diseases under field conditions remains challenging due to complex symptom morphology, environmental variability, and limited annotated data. While deep learning has been widely adopted for image-based diagnosis, existing studies are often model-centric and evaluated under heterogeneous experimental settings, making it difficult to derive paradigm-level insights into data efficiency, robustness, and practical deployment suitability. Methods In this research, we systematically compare three main types of vision models: Convolutional Neural Networks (CNNs), Vision Transformers, and mixed State Space Model-based structures (MambaVision). These models are applied to classify images of pests and diseases across multiple crops. Using a unified and reproducible experimental framework, we benchmark representative models across multiple training regimes, diverse crop species, and symptom types reflecting realistic agricultural scenarios. Results Results reveal clear paradigm-level differences. CNN-based models perform competitively on diseases dominated by localized lesion textures but show limited robustness for symptoms requiring global spatial interpretation. Transformer-based models benefit from global dependency modeling yet exhibit increased instability under small-sample conditions. In contrast, hybrid MambaVision-based models consistently demonstrate superior data efficiency and robustness, retaining approximately 60–80% accuracy under extreme data scarcity (1% training samples) and achieving stable, high F1-scores across symptom types that require joint modeling of fine-grained textures and long-range spatial distribution. Furthermore, performance–efficiency analysis shows that hybrid MambaVision-based models achieve a more favorable accuracy–computational cost trade-off than CNN-based and Transformer-based models, supporting deployment under practical resource constraints. Discussion Overall, this study provides pathology-oriented and deployment-aware insights into how architectural inductive bias interacts with symptom morphology and data availability, highlighting hybrid MambaVision-based models as a robust and effective solution for real-world plant pest and disease recognition.
Why it matches plant phenotyping methods植物病害の症状画像を対象に、複数の深層学習アーキテクチャを統一条件で比較・ベンチマークしており、植物の病害状態を推定する方法の評価が研究の中心である。
abstractUsing a unified and reproducible experimental framework, we benchmark representative models across multiple training regimes, diverse crop species, and symptom types reflecting realistic agricultural scenarios.
Understanding how oat (Avena sativa L.) cultivars differ in canopy development and competitive ability is essential for improving yield stability under increasing weed pressure. This study used unmanned aerial vehicle (UAV)-based multispectral imaging to characterize the temporal spectral and structural traits of sixteen oat cultivars grown under weed-free and weedy conditions across two locations for two years. Weedy conditions involved natural weed populations and pseudo-weeds where canola (Brassica napus) seeded as a weed. Weekly drone imaging was carried out using a multispectral sensor, which provided vegetation indices (NDVI, NDRE, ExG) and canopy metrics (ground cover, height, volume). Logistic and Gompertz models were fitted to cultivar traits to describe growth trajectories and obtain dynamic growth parameters. Cultivars showed clear differences in early canopy expansion, maximum NDVI, and canopy volume, with forage types expressing aggressive growth and several grain types combining high early growth rate with high yield potential. Machine-learning models integrating static and dynamic UAV-derived plant traits identified early ground cover and NDRE at three weeks after planting as the strongest predictors of grain yield. Models accurately predicted both weed-free (MAE = 262, R2 = 0.90) and weedy yield (MAE = 258, R2 = 0.90), demonstrating that early-season UAV traits capture the physiological and structural characteristics associated with competitive ability and grain yield. These findings show that high-throughput UAV phenotyping can reliably identify traits linked to yield formation and weed tolerance, providing a scalable approach for selecting competitive oat cultivars without relying solely on labor-intensive weedy field trials.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像からキャノピー形質を高スループットに抽出し、機械学習による予測性能も評価することが研究の中心であるため、植物フェノタイピング手法の実質的応用・検証に該当する。
abstractThis study used unmanned aerial vehicle (UAV)-based multispectral imaging to characterize the temporal spectral and structural traits of sixteen oat cultivars
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:485-547Plant phenotyping relevance match · UnverifiedEurope PMC · bioRxiv · Crossref · checked 5 Sept 2026
Summary (1) Rationale Quantifying and predicting plant morphology is central to understanding development and evolution, yet many plant forms lack homologous features required for traditional morphometrics. We apply the Euler Characteristic Transform (ECT), an injective descriptor from topological data analysis, to encode 2D plant shapes. The ECT converts contours into image-like representations that preserve shape information while enabling deep learning. (2) Methods We computed ECTs for large datasets of leaf and pavement cell shapes and used convolutional neural networks (CNNs) for classification. We also trained CNNs to approximate the inverse mapping, predicting leaf shape masks from radial ECTs. (3) Key results ECT-based models achieved high classification accuracy, surpassing previous approaches on millions of herbarium-derived leaves. Notably, grapevine leaf venation was predicted from blade geometry alone, demonstrating that vascular structure is encoded in the outline. (4) Main conclusion The ECT provides a compact, information-preserving representation of biological shape that integrates naturally with deep learning. It enables both accurate classification and predictive reconstruction, revealing latent morphological information and offering new opportunities to study plant form across scales.
Why it matches plant phenotyping methods植物形状を定量化・分類し、葉形状や葉脈を推定するECTベースの計算手法を中心に開発・評価しているため、植物フェノタイピング手法研究に該当する。
abstractQuantifying and predicting plant morphology is central to understanding development and evolution
Sowing space and depth critically influence wheat canopy architecture, yet their layer-specific effects remain poorly understood. This two-year field study evaluated the effects of three sowing spaces (1.5, 3.0, 4.5 cm) and three sowing depths (2, 3, 6 cm) on canopy projection area, leaf inclination angle, leaf area distribution, and leaf area index (LAI) of dryland wheat (Triticum aestivum ‘Ningmai 13’) in Luhe, Nanjing, China, using image-based phenotyping with manual validation. Narrow spacing (1.5 cm) with intermediate depth (3 cm) produced the largest canopy projection area (0.239–0.245 m2) and an increase in leaf erectness in the middle canopy layer (+23% above average). The highest LAI values (4.23–4.28 m2 m−2) were achieved with narrow spacing (A1B1, A1B2), demonstrating that dense canopies can be established under dryland conditions. Grain yield (g/plant) was measured as a supporting agronomic indicator; the highest yield per plant (14.36 g/plant) was observed in A3B1. Image-based measurements showed excellent agreement with manual methods (R2 > 0.97 for all traits), validating the phenotyping pipeline. These findings contribute to a deeper understanding of how sowing parameters shape wheat canopies in dryland systems.
Why it matches plant phenotyping methods画像ベースの表現型測定パイプラインを用いてキャノピー形態・葉形質を抽出し、手動測定との一致性も検証しており、フェノタイピング手法が中心的です。
abstractusing image-based phenotyping with manual validation
Introduction To accurately segment point clouds and quickly calculate leaf length and stem diameter, thereby enabling phenotypic analysis and variety selection of greenhouse tomato plants, this paper proposes a voxel grid downsampling (VGDS)-PointNet++-based model for point cloud segmentation and trait calculation. Methods The point clouds of the tomato canopy were acquired using a depth camera. After labeling, point cloud augmentation was performed, and the tomato point cloud dataset (TPCD) containing 1,552 sets of data was rebuilt. Voxel grid downsampling was applied to replace the original sampling strategy of PointNet++. Models of PointNet, PointNet++, VGDS-PointNet++, and Point Transformer were trained with the TPCD and compared on segmentation quality with accuracy and mean Intersection over Union (mIoU). After segmentation, skeletal morphology was fitted for non-occluded leaves by applying a series of surface fitting techniques. The leaf lengths and stem diameters were automatically calculated and compared with the manually measured values. Results The validation results showed that the average runtime of voxel grid downsampling was 0.132 s, which was lower than under the same number of sampled points. Compared to the other three models, the proposed model had higher accuracy and mIoU, reaching up to 96.80% and 88.95%, respectively. The proposal's accuracy and mIoU increased by 3.9% and 4.45% over PointNet++, respectively. The determination coefficient R 2 between the automatic calculation and manual measurement values of leaf length and stem diameter was 0.93 and 0.87, respectively. Discussion This can help extract phenotypic traits of tomatoes using depth cameras.
Why it matches plant phenotyping methods深度カメラ点群のセグメンテーションと葉長・茎径の自動推定手法を開発し、手測定および複数モデルと比較検証しており、表現型取得が研究の中心である。
abstractthis paper proposes a voxel grid downsampling (VGDS)-PointNet++-based model for point cloud segmentation and trait calculation
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Introduction High-throughput field phenotyping (HTFP) holds great potential for elucidating the relationship between genomes and phenotypes. However, obtaining high-quality three-dimensional point cloud data of field populations and achieving single-plant phenotypic analysis remain challenging. Methods This study develops an integrated framework for field crop reconstruction based on 3D Gaussian splatting, incorporating a geometry-aware dynamic constraint algorithm to achieve instance segmentation and extract key phenotypic traits of individual plants. Using 3D Gaussian splatting technology, field-scale cotton population modeling is accomplished, generating dense 3D point clouds for regions of interest. Furthermore, the concept of a crop localization domain is proposed, establishing a longitudinal mapping that associates plant positional coordinates with long-term phenotypic attributes. Finally, through a dynamic spatial constraint mechanism, the accuracy and computational efficiency of instance segmentation for crop population point clouds are significantly improved, enabling rapid extraction of individual plant traits such as cotyledon node height, plant height, and leaf area. Results The results demonstrate that PhenotypeAI successfully reconstructed nine cotton populations with PSNR exceeding 30.0 dB. It successfully extracted regions of interest from 403 cotton plants, achieving an average F-score of 91.32% for instance segmentation and an average accuracy of 91.35%. The extracted traits—cotyledon node height, plant height, and leaf area—exhibited strong correlations with manual measurements, with coefficients of determination ( R 2 ) of 0.90, 0.91, and 0.91, respectively. Discussion The proposed method provides a low-cost solution for high-throughput field phenotypic analysis of field cotton and improves the efficiency of cotton breeding.
Why it matches plant phenotyping methods3D再構成と動的空間制約による個体セグメンテーションおよび形質抽出が研究の中心で、綿花の草丈・葉面積などを検証しているため。
abstractThis study develops an integrated framework for field crop reconstruction based on 3D Gaussian splatting, incorporating a geometry-aware dynamic constraint algorithm to achieve instance segmentation and extract key phenotypic traits of individual plants.
Abstract Maize（Zea mays L.） is an important crop, and improving its productivity is required even under challenging conditions such as labor shortages and uncertain climate fluctuations. One approach to enhancing yield is utilizing crop data for cultivation management and yield prediction. However, efficient acquisition of such data remains constrained by various limitations. In this study, we developed a non-contact and labor-efficient method for crop data acquisition by generating 3D models of maize at the ripening stage using Neural Radiance Fields (NeRF). Segmentation was performed on the obtained point clouds to estimate plant height, leaf area, and leaf angle. The coefficients of determination (R2) were 0.903, 0.954, and -0.521, respectively, demonstrating high accuracy for plant height and leaf area even at the ripening stage, while reducing the time required for data acquisition by 93% compared to manual measurements. Nevertheless, some manual operations−such as removing kernels and separating overlapping leaves−were still necessary, and full automation was not achieved. The main sources of error were identified as reconstruction errors in the base during scale adjustment, excessive removal of leaf sheaths, and the curvature of individual plants. Furthermore, we examined how measurement accuracy was influenced by factors such as the time of day and cultivar. The proposed method is expected to contribute to the practical implementation of a labor -saving 3D measurement technique that supports yield prediction and growth diagnosis in maize.
Why it matches plant phenotyping methodsNeRFによる3D再構成と点群セグメンテーションを開発し、トウモロコシの草丈・葉面積・葉角度を推定して精度と誤差要因を検証しており、表現型取得法が研究の中心である。
abstractwe developed a non-contact and labor-efficient method for crop data acquisition by generating 3D models of maize at the ripening stage using Neural Radiance Fields (NeRF).
Abstract Accurate estimation of individual tree above‐ground biomass (AGB) and its component‐wise allocation is crucial for advancing ecological research and forest management. However, current biomass estimation methods, such as destructive sampling and allometric equation–based approaches, face limitations in both operational efficiency and cost‐effectiveness, and only destructive sampling can provide component‐wise biomass measurements, which is impractical for large‐scale studies or repeated measurements. In this study, we present a terrestrial laser scanning (TLS)‐based workflow integrating wood–leaf separation, voxel‐based foliage estimation and detailed 3D reconstruction of tree architecture to achieve accurate estimation of individual tree AGB and its component‐wise allocation. A total of 68 trees were scanned to obtain high‐resolution TLS data and subsequently destructively harvested to acquire field reference measurements for validation. The results demonstrate that the workflow achieved high accuracy in predicting AGB at the individual tree level (coefficient of determination/ R 2 = 0.88, root mean squared error/RMSE = 16.83 kg, mean absolute error/MAE = 12.18 kg), significantly outperforming estimates derived from locally calibrated allometric equations ( R 2 = 0.61, RMSE = 29.86 kg, MAE = 24.52 kg). Furthermore, this study provides evidence of the strong capability of TLS in estimating branch‐level biomass, with high accuracy achieved across branch orders ( R 2 ranging from 0.66 to 0.91, RMSE from 3.55 to 380 g and MAE from 2.97 to 290 g). By providing precise, non‐destructive estimates of biomass distribution across branches and leaves, this workflow demonstrates strong potential for improving the accuracy of tree biomass quantification, supporting investigations of resource allocation strategies, and enhancing forest carbon monitoring.
Why it matches plant phenotyping methodsTLSによる樹木の地上部・器官別バイオマスを推定するワークフローを開発し、破壊的実測で精度検証しており、植物形質取得法が研究の中心である。
abstractwe present a terrestrial laser scanning (TLS)‐based workflow integrating wood–leaf separation, voxel‐based foliage estimation and detailed 3D reconstruction of tree architecture to achieve accurate estimation of individual tree AGB and its component‐wise allocation.
Abstract Non-structural carbohydrates (NSC) stored in the stem play a crucial role in supporting yield formation in rice. However, internode morphological determinants of NSC accumulation are unclear. This study aimed to clarify the relationship between internode morphology and NSC accumulation and to identify a robust morphological indicator for evaluating NSC accumulation capacity. Two years of field experiments were conducted using multiple cultivars. The NSC content was quantified for individual internodes and at the whole-plant culm level, and its relationships with internode morphological traits were analyzed. Since the upper internodes (UIN; first and second internodes) and lower internodes (LIN; third and subsequent internodes) exhibited contrasting roles in NSC accumulation, a novel index was introduced, the volume composition ratio (VCR) of UIN/LIN, which represents their relative volumetric contributions within a culm. The VCR of UIN/LIN showed the strongest correlation with culm NSC and high reproducibility across years, outperforming simple morphological traits. Manipulation of internode development using plant growth regulators demonstrated that altering VCR effectively modified culm NSC accumulation. Accordingly, the VCR of UIN/LIN serves as a robust morphological indicator of culm NSC accumulation capacity, providing a practical framework for improving NSC accumulation to achieve high and stable yield performance in rice.
Why it matches plant phenotyping methodsイネ茎の形態からNSC蓄積能を評価する新規指標VCRを導入し、再現性と既存形態形質との比較まで行っており、表現型評価法が研究の中心である。
abstracta novel index was introduced, the volume composition ratio (VCR) of UIN/LIN, which represents their relative volumetric contributions within a culm.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract High-throughput phenotyping of biological samples is essential for large-scale studies but is frequently bottlenecked by the need for accurate instance segmentation in crowded images. While deep learning offers powerful solutions, the high cost of manual annotation and the requirement for coding expertise often limit adoption in routine laboratory workflows. Here we present SegBio, a lightweight, open-source pipeline that enables end-to-end instance segmentation for non-expert users. The protocol features an interactive annotation GUI that extrapolates full masks from minimal centerline markings, significantly reducing manual labeling effort. It further integrates a configurable U-Net training module and a standalone inference application with a ‘human-in-the-loop’ editing workflow for rapid and intuitive error correction. We employ the pipeline to annotate and train the model on a novel dataset of crowded C. elegans images. Validated on independent datasets, SegBio achieves high segmentation performance (Panoptic quality ∼0.85) and accurately quantifies per-animal morphology and fluorescence. By eliminating external dependencies and streamlining the correction process, SegBio provides a scalable solution for routine phenotyping that is easily generalized to other crowded biological samples, such as cellular organelles, cells, and organisms.
Why it matches plant phenotyping methodsC. elegansの画像から個体インスタンスを分離し、形態と蛍光を定量するエンドツーエンドの表現型解析ツールを開発・検証しており、方法が研究の中心である。
abstractHere we present SegBio, a lightweight, open-source pipeline that enables end-to-end instance segmentation for non-expert users.
Accurate segmentation of fine-scale organs from 3D point clouds poses a substantial challenge in high-throughput plant phenotyping (HTP), where existing methods are hindered by the loss of topological features and the resulting low accuracy. To address this, we introduce CotSkNet, a semantic segmentation network based on structured representation learning. This approach is the first to define plant architecture as a knowledge-based intermediate representation that integrates geometric, topological, and hierarchical relationships, thereby incorporating topological information into the segmentation process. CotSkNet features an innovative Topological Geometric Feature Fusion Extractor and a Two-Way Reinforcement Module to efficiently extract and enhance salient features. Furthermore, the proposed Topological Attention Aggregator enables dynamic focus on key branch points. On a dataset of 403 field-grown cotton plants, our method achieved mean intersection over union values of 91.55%, 92.37%, and 98.21% for the main stem, fruiting branches, and leaves, respectively, far surpassing those of mainstream methods. Moreover, automatically extracted phenotypic parameters, such as plant height and fruiting branch length showed excellent consistency with manual measurements (R 2 > 0.91, root mean square error (RMSE) < 0.14). This study confirms that structured representation learning excels at capturing fine-organ phenotypes, providing an innovative analytical pathway for HTP in complex field crops.
Why it matches plant phenotyping methods3D点群の器官セグメンテーションと形質抽出手法を開発し、圃場データで精度検証しているため、植物フェノタイピング手法が研究の中心である。
abstractwe introduce CotSkNet, a semantic segmentation network based on structured representation learning.
Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 15 Sept 2026
Computer vision and Internet of Things (IoT) technologies offer robust solutions for plant phenotyping, but traditional mainstream segmentation methods often fail in high-density plantings with overlapping foliage. This study introduces an integrated phenotyping system combining automated data capture and high-temporal RGB-D imaging using off-the-shelf hardware (Intel RealSense D435 and Raspberry Pi) to generate 3D point clouds of lettuce under controlled greenhouse conditions. While recent agricultural applications have shown limited success and required domain-specific adaptations, Segment Anything Model (SAM) and FastSAM were demonstrated to achieve exceptional zero-shot segmentation performance for individual lettuce plants in high-density arrangements without additional training. This capability effectively addresses the traditional challenges of species-specific parameter tuning and extensive training data requirements and fine-tuning. By mapping 2D segmentation masks to corresponding 3D point clouds, the system accurately extracted key phenotypic traits, namely plant height, length, and width, from which area and volume were subsequently estimated, showing strong correlations with manual measurements for Rex and Rouxai lettuce cultivars. This high-temporal, non-destructive monitoring provided unique insights into plant growth dynamics. The study highlights distinct growth patterns among these cultivars, underscoring the importance of tailored phenotyping approaches to optimise crop management strategies. By addressing the limitations of existing phenotyping methods, this work advances precision agriculture technologies, offering a cost-effective and efficient solution for monitoring dynamic crop growth with potential applications across various crops and growing conditions.
Why it matches plant phenotyping methodsRGB-D撮像、3D点群、セグメンテーションを統合した植物表現型取得システムを開発し、草丈・長さ・幅などを抽出して手測定と検証しているため、方法が中心的である。
abstractThis study introduces an integrated phenotyping system combining automated data capture and high-temporal RGB-D imaging using off-the-shelf hardware (Intel RealSense D435 and Raspberry Pi) to generate 3D point clouds of lettuce under controlled greenhouse conditions.
Computer vision and Internet of Things (IoT) technologies offer robust solutions for plant phenotyping, but traditional mainstream segmentation methods often fail in high-density plantings with overlapping foliage. This study introduces an integrated phenotyping system combining automated data capture and high-temporal RGB-D imaging using off-the-shelf hardware (Intel RealSense D435 and Raspberry Pi) to generate 3D point clouds of lettuce under controlled greenhouse conditions. While recent agricultural applications have shown limited success and required domain-specific adaptations, Segment Anything Model (SAM) and FastSAM were demonstrated to achieve exceptional zero-shot segmentation performance for individual lettuce plants in high-density arrangements without additional training. This capability effectively addresses the traditional challenges of species-specific parameter tuning and extensive training data requirements and fine-tuning. By mapping 2D segmentation masks to corresponding 3D point clouds, the system accurately extracted key phenotypic traits, namely plant height, length, and width, from which area and volume were subsequently estimated, showing strong correlations with manual measurements for Rex and Rouxai lettuce cultivars. This high-temporal, non-destructive monitoring provided unique insights into plant growth dynamics. The study highlights distinct growth patterns among these cultivars, underscoring the importance of tailored phenotyping approaches to optimise crop management strategies. By addressing the limitations of existing phenotyping methods, this work advances precision agriculture technologies, offering a cost-effective and efficient solution for monitoring dynamic crop growth with potential applications across various crops and growing conditions.
Why it matches plant phenotyping methodsRGB-D画像、3D点群、セグメンテーションを統合して植物形質を抽出するフェノタイピングシステムの開発・評価が中心であり、手動測定との相関検証も行っているため。
abstractThis study introduces an integrated phenotyping system combining automated data capture and high-temporal RGB-D imaging using off-the-shelf hardware (Intel RealSense D435 and Raspberry Pi) to generate 3D point clouds of lettuce under controlled greenhouse conditions.
施設園芸における環境制御では，温度や湿度の空間変動など環境の不均一性を考慮せずに，平均化された指標に基づいた制御が行われており，作物の生育にばらつきが生じる問題があった．これらを解決するためには，主に日照や温度・湿度などの不均一の原因となっている作物群落のキャノピー構造を可視化することが重要である．本研究では，低コストの2D LiDAR（Light Detection and Ranging）計測により，作物のキャノピー構造の可視化を試み，薄い葉や細い茎によるレーザー反射の有効性と適切なスキャン条件を検証した．機器を設置した台車を移動プラットフォームとして，圃場の畝に沿って移動させることで，畝に沿った作物のキャノピー構造を把握する．2つのLiDARの走査面を変えて用いることとし，水平スキャンにより，台車進行方向の作物及び障害物の2Dマッピング，垂直スキャンにより作物の高さ方向のスキャンを時系列的に重ねることで，3Dマッピングを行う．結果として，水平スキャンのデータは，台車の走行制御のための状況把握としては十分な精度で利用可能である．垂直スキャンのデータは，作物の高さ方向の構造を把握できることが確認された．2つのLiDARを搭載したシステムを用いて，圃場で定期的に移動計測を行うことで，作物のキャノピー構造を把握することができ，環境の不均一の要因として利用可能となることが期待される．
Why it matches plant phenotyping methods低コスト2D LiDARによる作物キャノピー構造の可視化手法を開発し、反射の有効性と走査条件を検証しているため、植物表現型取得が研究の中心である。
abstract本研究では，低コストの2D LiDAR（Light Detection and Ranging）計測により，作物のキャノピー構造の可視化を試み，薄い葉や細い茎によるレーザー反射の有効性と適切なスキャン条件を検証した．
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 5 Sept 2026
Abstract Grapevine cluster architecture is a key selection target in breeding programs because it influences disease susceptibility, yield stability and juice quality. High-throughput phenotyping offers a rapid and non-destructive approach to capture biochemical and structural variation in these traits, yet the influence of plant organ reflectance and data partitioning strategies on trait prediction remains poorly understood. In this study, we evaluated how hyperspectral reflectance from different grapevine organs contributes to the prediction of cluster architecture and juice quality traits in two clonal populations of Riesling and Pinot. Using partial least squares regression (PLSR), we assessed the prediction accuracy of eight cluster architecture and six juice quality traits under two data partitioning strategies. Models based on cluster reflectance outperformed those using dry leaf reflectance for most traits, except for pH. Partitioning the dataset by cluster type increased trait variance and improved predictions for number of berries (R² = 0.53), berry diameter (R² = 0.79), and total acidity (R² = 0.48). Visible, red-edge and NIR spectra were most informative regions to predict the traits studied. Together, our results highlight the importance of organ-specific data and appropriate calibration strategies to improve phenomic models for the development of scalable proxies for grapevine improvement. Highlight Spectral phenomics reveals that prediction accuracy in grapevine depends on organ spectral signatures and traits, with cluster reflectance outperforming leaves, informing new phenotyping strategies for breeding improvement.
Why it matches plant phenotyping methodsブドウの器官反射スペクトルとPLSRを用いて、房構造および果汁品質形質の予測性能を評価することが中心であり、スペクトル表現型解析手法の検証・応用に該当する。
abstractHigh-throughput phenotyping offers a rapid and non-destructive approach to capture biochemical and structural variation in these traits
Accurate fresh weight (FW) estimation is essential for growth monitoring and yield prediction in greenhouse fruit vegetables, but remains challenging due to the dynamic allocation between vegetative and reproductive organs. This study aimed to systematically evaluate modeling strategies for FW estimation in sweet pepper and identify which approach is most suitable under conditions of dynamic biomass partitioning. Non-destructive morphological measurements were collected under greenhouse cultivation, and allometric models based on geometric equations were established as baselines. Their performance was compared with machine learning (ML) models and ensemble learning frameworks. To address limited data availability, numerical data augmentation with Gaussian noise and a variational autoencoder was applied. Among the allometric models, the stick model combined with a sigmoid function showed the highest performance, with an R2 of 0.80 for shoot FW and 0.54 for fruit FW. All ML models outperformed the allometric models, and the ensemble model achieved the highest predictive accuracy, with an R2 of 0.96 for shoot FW and 0.89 for fruit FW. Data augmentation further improved predictive performance across all ML models, particularly for fruit FW prediction. Feature contribution analysis revealed that temporal progression was the dominant predictor of fruit FW, while structural traits played the primary role in shoot FW estimation. Ensemble-based ML, combined with data augmentation, provides a methodological framework for non-destructive FW estimation of sweet pepper in controlled environments such as greenhouses and smart farming systems.
Why it matches plant phenotyping methods非破壊形態計測からピーマンの地上部・果実の生重を推定するアロメトリック／機械学習／アンサンブル手法を比較・評価しており、表現型取得・推定手法が研究の中心である。
abstractThis study aimed to systematically evaluate modeling strategies for FW estimation in sweet pepper
Premise: Herbarium specimens are increasingly used to extract morphological traits for ecological and evolutionary studies, yet the effects of tissue desiccation on trait measurements remain poorly understood. Here, we tested whether higher tissue water content leads to greater measurement changes after herborization (H1) and whether fresh trait values can be reliably predicted from herbarium measurements (H2). Methods: We evaluated the reliability of herbarium-based measurements by comparing fresh and dried traits of leaves, flowers, fleshy fruits, and seeds across 262 individuals representing 133 Neotropical Myrtaceae species. Phylogenetic least square models and machine-learning regressions were used to test H1 and H2. Results: Leaves and flowers generally shrank after herborization, fruits size metrics tended to increase, and seeds were largely unaffected. Water content was significantly associated with the magnitude of herborization effects in flowers and some leaf and seed traits. Fresh trait values were accurately predicted from herbarium measurements. Prediction errors were lowest for leaf traits, followed by fruits, flowers, and seeds. Discussion: These results partially support H1 and support H2, indicating that herbarium specimens can be reliably used for trait analyses when organ-specific responses are considered, providing a practical framework to account for potential desiccation bias in functional trait research.
Why it matches plant phenotyping methodsハーバリウム標本による植物形態形質測定の信頼性評価と、生鮮形質の予測手法が研究の中心であり、植物フェノタイピング手法の検証に該当する。
abstractWe evaluated the reliability of herbarium-based measurements by comparing fresh and dried traits of leaves, flowers, fleshy fruits, and seeds across 262 individuals representing 133 Neotropical Myrtaceae species.
Reproduction assets foundThe authors explicitly state that the code used for the PGLS and machine-learning analyses is publicly available in a GitHub repository; raw phenotype data is promised only upon acceptance, so the code asset qualifies while the dataset is not yet actionable.Code · publicSupporting Information and the code used to perform the analyses are available at
https://github.com/ykilsztajn/fresh_dry_myrtaceae. All raw data will be made available in the
same repository upon acceptance for publication.Open asset ↗ykilsztajn/fresh_dry_myrtaceaepdf-page:9 lines:1-48Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 5 Sept 2026
3D plant phenotyping has garnered significant interest for its ability to quantify key structural traits such as plant volume and canopy architecture. However, standard monocular 3D reconstruction techniques suffer from inherent scale ambiguity, requiring an additional step to recover the true metric scale of the plants. Existing scale recovery methods, whether based on precisely fabricated 3D objects or planar patterns such as checkerboards, have been successfully applied in controlled environments but face practical constraints in certain real-world scenarios: some require costly fabrication or pre-reconstruction calibration, which can limit throughput in dynamic field environments. Here, we present MagicRing, a novel, affordable, and physically reliable post-reconstruction scale recovery approach that addresses these specific constraints and provides a complementary solution for high-throughput, mobile, and field-based phenotyping. MagicRing features a simple red ring printed on A4 paper with a known diameter. By leveraging color-based segmentation and geometric curve fitting, our approach automatically detects the ring within 3D point clouds, recovers the metric scale, and establishes a standardized world coordinate system without the need for pre-calibration. Its planar, isotropic design ensures robustness even under significant occlusion. We demonstrate the utility of MagicRing through MobilePheno3D, an integrated smartphone-based pipeline that performs fully automated 3D reconstruction, scale recovery, and phenotypic extraction from video sequences. This system, which was validated across multiple plant species, including vegetables, wheat, rice, and maize in both indoor and field settings, reliably reconstructs aboveground and root structures and supports continuous growth monitoring. MagicRing decouples data collection from data analysis, enabling a workflow transition from conventional step-by-step, scene-specific calibration toward more scalable, high-throughput 3D plant phenotyping.
Why it matches plant phenotyping methods植物の3D形態形質を抽出するためのスケール復元法とスマートフォン型フェノタイピング・パイプラインを開発し、複数植物種・環境で検証しており、手法が研究の中心である。
abstractHere, we present MagicRing, a novel, affordable, and physically reliable post-reconstruction scale recovery approach
This manuscript presents a transferable and reproducible methodology for quantitative 3D tree-structure mensuration and transparent, rule-based decision support for tree risk management. The workflow integrates (i) Structure-from-Motion/Multi-View Stereo (SfM–MVS) reconstruction from multi-view imagery, (ii) independent referencing to ensure metric scaling and a consistent local frame, and (iii) point cloud analytics to derive branch-level geometric descriptors (e.g., base diameter, length, inclination, slenderness, and projected reach). A clear rule-based layer operationalizes Tree Risk Assessment Qualification (TRAQ)-style risk components and As Low As Reasonably Practicable (ALARP) principles to map geometry and exposure into auditable management recommendations (e.g., monitoring intervals, pruning/weight reduction, supplemental support, and exclusion-zone planning). To provide a real-data example, the demonstration uses the public Fuji-SfM apple orchard dataset, including three neighboring trees with partially overlapping crowns for tree instance extraction and subsequent TRAQ/ALARP scenarios on an outer tree. The proposed decision layer is intentionally based on external geometry and exposure; internal decay indicators and species-specific mechanical properties (e.g., Modulus of Elasticity (MOE), Modulus of Rupture (MOR)) are outside this demonstration and should be incorporated via complementary diagnostics in operational deployments.
Why it matches plant phenotyping methodsSfM–MVSと点群解析による樹木・枝の3D形状形質抽出が中心的な方法論であり、実データでの適用も含むため。
abstractThis manuscript presents a transferable and reproducible methodology for quantitative 3D tree-structure mensuration
Summary statement Cotton ( Gossypium hirsutum ) drought sensitivity depends strongly on flowering stage, but stage‐resolved, non‐destructive detection remains limited. Using controlled short‐term droughts imposed at early, mid, or late flowering, we integrated multispectral and hyperspectral canopy phenotyping with physiology and explainable machine learning to identify spectral predictors of metabolic status and recovery. Early and mid‐flowering drought responses were largely recoverable, whereas late‐flowering drought caused the most potent and least reversible losses in photosynthesis, canopy structure, and fiber quality. These results highlight late flowering as a critical vulnerability window and provide a mechanistically grounded framework for rapid phenotyping of stage‐specific drought resilience.
Why it matches plant phenotyping methodsマルチスペクトル・ハイパースペクトルによる非破壊キャノピー表現型計測と説明可能な機械学習を中核に、乾燥耐性を迅速推定する方法・枠組みを提示している。
Abstract Fitness costs of plant disease defence are often subtle and difficult to quantify. In this study, we therefore used comparative high-throughput phenotyping in two independent facilities to assess growth, morphology and physiology of potato (cv. Désirée) with high time-resolution monitoring different defence mechanisms under pathogen-free conditions. Plants were either treated weekly with the resistance inducers β-aminobutyric acid (BABA; 10 mM) or potassium phosphite (KPhi; 36 mM) or comprised six transgenic lines expressing late blight resistance genes (single Rpi genes or a three-gene stack) or reduced jasmonate perception (StCOI1-RNAi). Over four weeks, image-derived traits revealed consistent cross-facility effects for plant height and colour: BABA treatment increased plant height but reduced canopy area and induced a paler greenness signature, whereas KPhi caused minimal and transient growth effects. Chlorophyll fluorescence at the NaPPI facility indicated reduced vitality (Rfd_Lss) in BABA-treated plants and increased Rfd_Lss following KPhi, while maximum PSII efficiency was largely unchanged. Several transgenic lines showed somewhat reduced above-ground biomass. Enzyme activity profiling produced distinct treatment and genotype signatures, but was strongly modulated by facility conditions that overrode these specificities. Overall, high-throughput phenotyping robustly detected subtle growth–defence trade-offs across platforms. Highlight High-throughput optical phenotyping validated across two independent research facilities reveals that stacked resistance genes and resistance inducers in potato trigger subtle growth trade-offs. Graphical abstracts Experimental timeline for high-throughput plant phenotyping platforms. Created in BioRender. Poque, S. (2026) https://BioRender.com/nmkve7g
Why it matches plant phenotyping methods二つの独立施設で高スループット光学フェノタイピングを比較・検証し、画像由来形質と蛍光指標の再現性を評価しており、フェノタイピング手法が研究の中心である。
titleComparative high-throughput phenotyping across two facilities reveals differential impact of defence mechanisms on plant growth and development
Precision greenhouse agriculture enhances plant health and crop yields by continuously monitoring key plant parameters. Stem diameter is such a parameter and is monitored to support decisions on plant care. However, traditional contact-based methods induce thigmomorphogenic effects that impact plant growth. Here, we introduce the Optical Caliper (OC), a novel contactless device for precise, non-invasive stem diameter measurement. The OC operates by projecting a collimated light beam to cast a shadow of the stem onto a high-resolution image sensor. The shadow size is a measure for the stem diameter. Controlled laboratory tests show the OC offers an accuracy comparable to that of a Digital Caliper (DC). Field trials on irregular tomato and cucumber stems demonstrate a repeatability of 0.1-0.2 mm. The OC's non-invasive design and high repeatability exceed the performance of a DC, making it particularly suited for accurately monitoring soft, variable plant structures. Bringing the advantage of avoiding thigmomophogenic effects and thus optimizing crop yield, the OC is a promising tool for high-throughput plant phenotyping and precision agriculture applications.
Why it matches plant phenotyping methods植物の茎径を非接触・高精度に測定する光学デバイスを開発し、実験室および圃場で精度・再現性を検証しており、植物表現型取得法が研究の中心です。
abstractHere, we introduce the Optical Caliper (OC), a novel contactless device for precise, non-invasive stem diameter measurement.
Reproduction assets foundThe paper's measurement data (optical caliper, digital caliper, and micrometer readings on reference cylinders and tomato/cucumber stems) is openly deposited on the SURF data repository of The Hague University of Applied Sciences. No author analysis code or trained models are explicitly deposited; other allowed URLs (DDataset · publicThe data gathered during this study is openly available via https://hhs.data.surf.nl/s/nqnFYBf42KPA75P (accessed on 10 February 2026).Open asset ↗hhs.data.surf.nlhtml-lines:282-314Code / dataset availability confirmedEurope PMC · bioRxiv · checked 5 Sept 2026
Grapevine Trunk diseases (GTDs) represent a major threat for the wine industry. Despite several break-through, their etiology remains unclear and no curative treatment is currently available. Wood anatomy and water transport contribute to the symptoms of young plant decline. This study investigates wood anatomical alterations in two Alsatian grapevine cultivars presenting different susceptibility to GTDs, focusing on wood structure over six months of vegetative growth and in response to infection. Using a validated FasGa staining protocol, wood sections from transverse, tangential, and radial directions were stained to differentiate lignified and cellulosic tissues. Microscopic analysis was performed at x4, x10, and x40 magnifications, yielding a dataset of 4771 images. To support this high-throughput quantitative analysis of microscopy images, a computational model was developed, enabling reliable and efficient assessment of anatomical traits. Pre-established woody tissues presented higher xylem vessels diameter in Gewurztraminer than Riesling, with a dorsoventral arrangement whereas the number of vessels remained the same all over the cross section. No significant anatomical changes were observed in established woody tissues, whereas newly formed xylem anatomy showed a possible rearrangement during infection, especially in Gewurztraminer cultivar. Furthermore, colorimetric analysis quantified the lignification of woody tissues in response to wounding damage compared to un-treated plants. While definitive conclusions remain limited due to the experimental timeframe and sample variability, the findings highlight the need for longer-term studies and broader cultivar evaluation. Code and microscopy images have been made publicly available, providing a scalable digital tool for future research in plant vascular systems.
Why it matches plant phenotyping methods植物組織画像から木部解剖形質と木化を定量する計算モデルを開発・検証し、大規模画像データセットと公開コードを提供しており、表現型取得手法が研究の中心である。
abstractTo support this high-throughput quantitative analysis of microscopy images, a computational model was developed, enabling reliable and efficient assessment of anatomical traits.
Reproduction assets foundThe paper's microscopy image dataset (4771 grapevine wood images) is publicly deposited on Zenodo with an explicit DOI matching an allowed URL. The authors also state their Python analysis pipeline is available at github.com/courbot/vineside, but that URL is not among the allowed URLs, so only the Zenodo image dataset,Dataset · publicThis database can benefit the research community, and is publicly available
online at https://doi.org/10.5281/zenodo.18850060 [35].Open asset ↗Zenodo · 10.5281/zenodo.18850060pdf-page:4 lines:1-56Code / 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:330-415Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Effective monitoring of planted and natural forests is critical for assessing stand development and ensuring long-term ecological and economic success. However, traditional field-based inventories are labor-intensive and costly, limiting their applicability across large or inaccessible areas. Although unmanned aerial vehicles (UAVs) photogrammetry provides a scalable alternative, accurately delineating individual tree crowns in diverse and complex stand structures remains a significant challenge. We introduce and validate a cost-effective framework for automated individual tree inventory by integrating high-resolution imagery from a consumer-grade UAV with a two-stage deep learning pipeline. The framework employs a YOLO-based object detection model to localize individual trees, subsequently using these detections to prompt the Segment Anything Model 2 for precise, zero-shot tree crown segmentation. The framework was validated across diverse subtropical forests, including orchards, plantations, and natural forests. The deep learning models achieved high accuracy in detection (mAP50 = 0.881) and segmentation (mIoU = 0.854). The framework demonstrated robust performance in estimating horizontal structural parameters, especially in managed stands (R2=0.83 for orchards; R2>0.75 for plantations), and robust accuracy for tree height (R2>0.59). This fusion of consumer UAVs and foundation models offers a powerful, scalable tool for individual-tree-level inventory, with significant implications for precision silviculture and monitoring in subtropical forests.
Why it matches plant phenotyping methodsUAV画像と深層学習による個体樹冠 segmentation・樹木位置検出・樹高および構造パラメータ推定が研究の中心で、森林植物の形態形質を技術的に開発・検証している。
abstractWe introduce and validate a cost-effective framework for automated individual tree inventory by integrating high-resolution imagery from a consumer-grade UAV with a two-stage deep learning pipeline.
Peanut (Arachis hypogaea L.), a major legume crop valued for its high oil content, displays complex genotypic-phenotypic interactions shaped by environmental influences, yet these relationships remain poorly understood. We present a high-throughput phenotyping framework that captures the geometry of peanut pods using digital microscopy or smartphone imaging integrated with manifold-learning for large-scale analysis and visualization. Using over 6500 pods collected across China, we identify a geographically distinct morphological signature and demonstrate accurate cultivar discrimination. This scalable approach establishes the foundation for a Large Geometric Model capable of predicting phenotypic traits and accelerating precision agriculture. Our pipeline offers a transformative tool for peanut breeding and sustainable crop improvement.
Why it matches plant phenotyping methodsデジタル顕微鏡・スマートフォン画像と多様体学習を統合し、ピーナッツ莢の形態を大規模に取得・解析する高スループット表現型解析フレームワークが中心である。
abstractWe present a high-throughput phenotyping framework that captures the geometry of peanut pods using digital microscopy or smartphone imaging integrated with manifold-learning for large-scale analysis and visualization.
Reproduction assets foundThe authors state that the peanut pod image dataset, extracted phenotypic trait data, and the Orange Data Mining workflow (.ows) used for analysis are publicly available in their GitHub repository.Dataset · publicThe image dataset of peanut pods analyzed in this study and the extracted phenotypic trait data are publicly available in the GitHub repository: https://github.com/pengwengkung/Complex-geometry-peanut .Open asset ↗pengwengkung/Complex-geometry-peanutlines:169-192Code / 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:384-403Plant phenotyping relevance match · UnverifiedarXiv · OpenAlex · checked 13 Sept 2026
Dense ground-truth disparity maps are practically unobtainable in forestry environments, where thin overlapping branches and complex canopy geometry defeat conventional depth sensors -- a critical bottleneck for training supervised stereo matching networks for autonomous UAV-based pruning. We present UE5-Forest, a photorealistic synthetic stereo dataset built entirely in Unreal Engine 5 (UE5). One hundred and fifteen photogrammetry-scanned trees from the Quixel Megascans library are placed in virtual scenes and captured by a simulated stereo rig whose intrinsics -- 63 mm baseline, 2.8 mm focal length, 3.84 mm sensor width -- replicate the ZED Mini camera mounted on our drone. Orbiting each tree at up to 2 m across three elevation bands (horizontal, +45 degrees, -45 degrees) yields 5,520 rectified 1920 x 1080 stereo pairs with pixel-perfect disparity labels. We provide a statistical characterisation of the dataset -- covering disparity distributions, scene diversity, and visual fidelity -- and a qualitative comparison with real-world Canterbury Tree Branches imagery that confirms the photorealistic quality and geometric plausibility of the rendered data. The dataset will be publicly released to provide the community with a ready-to-use benchmark and training resource for stereo-based forestry depth estimation.
Why it matches plant phenotyping methods樹木の枝・樹冠形状を対象とするステレオ深度推定データセットを開発し、画素単位の視差ラベルと実画像との比較検証を提供しており、植物構造の取得方法が中心である。
abstractWe present UE5-Forest, a photorealistic synthetic stereo dataset built entirely in Unreal Engine 5 (UE5).
Branching forms are ubiquitous in nature and have evolved repeatedly across scales and species. An important goal of developmental biology remains to identify similarities and differences in the regulatory mechanisms underlying branching. Here, we investigate the branching filaments that form upon spore germination in mosses, using Physcomitrium patens as a model species. To identify the macroscopic rules governing filament patterning, we developed a pipeline to acquire high-resolution 3D images of whole sporelings, reconstruct filament architecture at single-cell resolution, and formalize cell organization using mathematical tree representations. Our quantitative analysis reveals that branch patterning in moss filaments can be captured by a simple probabilistic model in which subapical cells have a near-equal probability of producing – or not producing – a side-branch between successive apical cell divisions. This framework provides a quantitative basis for comparing the developmental rules driving branching morphogenesis within and beyond the plant kingdom.
Why it matches plant phenotyping methodsコケ植物のフィラメント形態を対象に、高解像度3D画像取得、単一細胞レベルの構造再構築、数学的表現による定量化パイプラインを開発しており、植物表現型の取得・抽出手法が研究の中心です。
abstractwe developed a pipeline to acquire high-resolution 3D images of whole sporelings, reconstruct filament architecture at single-cell resolution, and formalize cell organization using mathematical tree representations
Precise monitoring of crop canopy cover (CC) is crucial for evaluating growth and health under diverse water and nutrient conditions. Although nano liquid urea has been promoted in India as an eco-friendly alternative to conventional nitrogen (N) fertilizers. Its effectiveness in potato cultivation, particularly for canopy development and yield, remains unclear. To address this gap, a field experiment was conducted during the 2024–25 winter season using three N treatments under a micro-irrigation system: T1-recommended granular urea (46% N), T2-without N, and T3-IFFCO nano liquid urea (4% w/v). Images were captured using a downward-looking smartphone camera positioned 2.5 meters above the crop near each treatment, serving as the primary input for estimating canopy cover. The images were processed using both a prompt-based segmentation model (SAM3) and an image-processing pipeline (Modified Excess Green Index + Otsu thresholding) to estimate CC. The SAM3 occasionally overestimated or failed to detect the potato canopy with the prompt “Green plants, leaves, vegetations, canopy cover”, whereas the image-processing approach consistently provided accurate CC estimates and was therefore used for subsequent analysis. The result revealed that the CC clearly showed differentiation among the treatments after 25 days after sowing (DAS). With this most of the treatment comparisons depict the CC peaking after 45 DAS, where the T1 recorded the highest canopy cover (~71%), indicating a healthy crop, while the pairwise comparisons showed CC values of ~55% (T1–T2), ~66% (T1–T3), ~33% (T2–T2), ~36% (T3–T3), and ~36% (T2–T3), depicting the nitrogen deficit. Similarly, the yield followed the same trend, with T1 producing the highest yield (26.78 t/ha), compared to 10.89 t/ha in T2 and 11.90 t/ha in T3. The results indicate that nano liquid urea does not supply sufficient nitrogen to support optimal potato canopy growth and productivity, resulting almost similar response to the no nitrogen application treatment. Increasing the nitrogen concentration in nano liquid urea formulations may improve their effectiveness. This study provides evidence to guide farmers in selecting appropriate nitrogen fertilizers for potato cultivation. In the future, such fertilizers should be evaluated across different crops to ensure their efficacy and to prevent farmers from adopting products that may not meet crop nutrient requirements.
Why it matches plant phenotyping methodsジャガイモのキャノピー被覆率という植物形質を、SAM3と画像処理パイプラインで推定し、両手法の性能を比較・評価しているため、施肥試験を含むがフェノタイピング手法が中心的です。
titleEvaluating SAM3 and Conventional Image Processing Method for Potato Canopy Cover Estimation as an Indicator of Crop Health
Aerial remote sensing efficiently surveys large areas, but accurate direct object-level measurement remains difficult in complex natural scenes. Advancements in 3D computer vision, particularly radiance field representations such as NeRF and 3D Gaussian splatting, can improve reconstruction fidelity from posed imagery. Nevertheless, direct aerial measurement of important attributes like tree diameter at breast height (DBH) remains challenging. Trunks in aerial forest scans are distant and sparsely observed in image views; at typical operating altitudes, stems may span only a few pixels. With these constraints, conventional reconstruction methods have inaccurate breast-height trunk geometry. TreeDGS is an aerial image reconstruction method that uses 3D Gaussian splatting as a continuous scene representation for trunk measurement. After SfM–MVS initialization and Gaussian optimization, we extract a dense point set from the Gaussian field using RaDe-GS’s depth-aware cumulative-opacity integration and associate each sample with a multi-view opacity reliability score. Then, we isolate trunk points and estimate DBH using opacity-weighted solid-circle fitting. Evaluated on 10 plots with field-measured DBH, TreeDGS reaches 4.79 cm RMSE (about 2.6 pixels at this GSD) and outperforms a LiDAR baseline (7.66 cm RMSE). This shows that TreeDGS can enable accurate, low-cost aerial DBH measurement.
Why it matches plant phenotyping methods樹木の胸高直径（DBH）という植物形態形質を、航空画像から3D再構成と信頼度重み付き推定で測定する手法を開発・検証しており、フェノタイピング手法が中心である。
abstractTreeDGS is an aerial image reconstruction method that uses 3D Gaussian splatting as a continuous scene representation for trunk measurement.
High-fidelity acquisition of canopy phenotypic data is critical for the advancement of orchard Artificial Intelligence (AI). Yet, an improper Light Detection and Ranging (LiDAR) installation height (IH) frequently induces data occlusion and substantial measurement errors. To address this limitation, this study developed an information collection vehicle (ICV) integrated with a 16-channel three-dimensional (3D) LiDAR to determine the optimal LiDAR IH. Three representative LiDAR IHs (1.4 m, 2.0 m, and 2.6 m) were evaluated on spindle-shaped cherry trees under both forward and reverse driving strategies. Subsequently, a novel 12-zone refined evaluation framework was introduced to quantify localized errors that are conventionally obscured by traditional whole-canopy metrics. Results demonstrated a profound nonlinear relationship between IH and measurement accuracy. Specifically, the 2.0 m IH (approximating the canopy’s geometric center) emerged as the optimal setup, maintaining relative errors (REs) below 5% with minimal dispersion. Conversely, the 2.6 m IH caused lower-canopy volume REs to surge beyond 16% owing to restricted downward viewing angles. Additionally, reverse driving at higher IHs exacerbated mechanical vibrations via the “lever arm effect”, thereby significantly degrading point cloud registration accuracy. Ultimately, these findings underscore the critical necessity of aligning sensors with the canopy geometric center, supplying essential theoretical guidelines for the hardware design of future orchard robots.
Why it matches plant phenotyping methods果樹キャノピー形質の高精度取得を目的に、LiDAR搭載車両、設置高さ、走行条件、局所誤差評価法を開発・比較検証しており、フェノタイピング手法が中心である。
abstractHigh-fidelity acquisition of canopy phenotypic data is critical for the advancement of orchard Artificial Intelligence (AI).
3D crop phenotyping technology provides critical support for screening morphology-related plant genes and identification of germplasm resource. Organ segmentation or recognition is the first key step in 3D crop phenotyping, where inductive deep learning currently dominates as the mainstream methodology. However, the high requirement for data annotation in inductive learning paradigm has transformed the manual data labeling into a labor-intensive task, thereby in turn restricting the progress of inductive learning. This problem has inspired us to leverage Graph Neural Networks (GNNs) as the transductive learning tool to directly segment organs on sparsely annotated crop point clouds. We propose a Dual-branch Graph Convolutional Network (DBGCN) that only requires sparse labels to perform organ instance inference directly on plant point clouds that have featureless point features. Different from existing graph-based networks, DBGCN not only carries out the static-feature-space graph convolutions that are good at mining and aggregating on local information on the point cloud, but also incorporates dynamic graph convolutions that captures the potential changes of the graph manifold in deep feature space. Extensive experiments prove that the fusion of two types of graph feature convolutions brings a high node (point) classification accuracy, outperforming mainstream GNNs and even several popular inductive deep architectures. On the PlantNet sub-dataset, DBGCN achieves an mAcc (mean accuracy of node classification) of 93.00% under 1.95% manual annotation ratio. On the Soybean-MVS sub-dataset, DBGCN achieves an mAcc of 91.05% under 4.88% manual annotation ratio. Furthermore, our DBGCN not only works well on crop 3D data but can also serve other applications such as the segmentation of point cloud data for large-scale street view. Our dataset and code can be found at https://github.com/chinazhouzhaoyi/DBGCN/tree/master/.
Why it matches plant phenotyping methods3D植物点群から器官を分割・推論する深層学習手法を開発し、植物フェノタイピングデータ上で精度検証しているため、表現型取得・抽出法が中心である。
abstractWe propose a Dual-branch Graph Convolutional Network (DBGCN) that only requires sparse labels to perform organ instance inference directly on plant point clouds
Reproduction assets foundThe authors explicitly state that their dataset (plant point clouds) and DBGCN code are publicly available on GitHub.Code · publicOur data and code are available at: https://github.com/chinazhouzhaoyi/DBGCN/tree/master/.Open asset ↗chinazhouzhaoyi/DBGCNhtml-lines:414-455Dataset · publicOur dataset and code can be found at https://github.com/chinazhouzhaoyi/DBGCN/tree/master/Open asset ↗chinazhouzhaoyi/DBGCNhtml-lines:88-94Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
As the key structure connecting the vegetative and reproductive organs of soybean plants, the main stem plays a crucial role, and its morphological parameters serve as core phenotypic indicators for evaluating plant growth, lodging resistance, and yield potential. At the mature stage, the main stem exhibits high similarity to pods in color and texture, along with complex curvature and severe occlusion by pods and leaves, making accurate and continuous extraction challenging for conventional segmentation methods. To address this, this study proposes RAM-UNet, a high-precision semantic segmentation model based on an improved U-Net architecture. The model adopts ResNet50 as the backbone and replaces standard convolutions with deformable convolutions to capture curved stem morphology and improve feature extraction for low-contrast edges. In the encoder, the Convolutional Block Attention Module (CBAM) is combined with an improved atrous spatial pyramid pooling (ASPP) module (C-ASPP) with four dilation rates, enhancing multi-scale feature representation compared to the original three-rate design. A multi-scale attention aggregation (MSAA) module in the decoder improves continuity and integrity of stem boundaries. During training, a composite loss function combining Dice loss and cross-entropy loss is employed to mitigate foreground pixel sparsity. Experimental results on a self-constructed dataset show that RAM-UNet achieves a mean Intersection over Union (mIoU) of 90.58%, with Recall and Precision reaching 94.99% and 94.58%, respectively. Compared with U-Net, DeepLabv3+, PSPNet, and SegNet, RAM-UNet improves mIoU by 6.41%, 10.51%, 22.41%, and 17.37%, respectively. Automatically measured stem lengths show high agreement with manual measurements (R² = 0.9746), validating practical applicability. RAM-UNet also generalizes well on the public PASCAL VOC 2012 dataset, achieving an mIoU of 73.14%. The results indicate that the proposed model enables high-precision and continuous segmentation of main stems in mature soybean plants, providing an effective technical solution for automated and non-destructive measurement of crop phenotypic parameters.
Why it matches plant phenotyping methods大豆主茎のセグメンテーションと長さ測定を目的とする画像解析手法を開発し、手動測定との一致性で検証しており、植物表現型取得が中心である。
abstractthis study proposes RAM-UNet, a high-precision semantic segmentation model based on an improved U-Net architecture.
Abstract Mobile laser scanning (MLS) provides detailed point cloud reconstructions of forest environments and has potential for operational forest sample-plot surveying. This study evaluated the accuracy of MLS in deriving forest inventory attributes, including basal area (G), number of trees per hectare (TPH), total stem volume (V), basal area-weighted mean tree diameter (Dg) and height (Hg), and dominant height (Hdom). Experiments were conducted in managed boreal forests across 44 sample plots (370–2000 m2) using a Faro Orbis MLS system. Field measurements collected tree-by-tree (n = 4472) with callipers and clinometers during the previous summer served as reference data. We compared two alternative MLS data acquisition trajectories—closed loops (MLS-loop) and line transects (MLS-line)—and two processing workflows: (i) manually assisted tree detection followed by automatic tree measurements, and (ii) a fully automatic workflow. MLS-line provided similar or marginally improved accuracy compared with MLS-loop; however, the substantially shorter acquisition time of MLS-loop (19.0 min per plot on average) favoured its operational use over MLS-line (30.5 min). Clearer differences emerged between processing workflows. The fully automatic workflow identified and measured 74.1% of trees with diameter at breast height (DBH) > 5 cm, whereas manual assistance in tree detection increased this proportion to 97.1%. DBH accuracy was similar for both workflows (root-mean-square-error [RMSE] ≈ 2.4 cm), but tree-height estimates were substantially less accurate under automatic processing (RMSE 6.2 m) than under the assisted workflow (RMSE 2.1 m). These differences propagated to plot-level estimates. Using the automatic workflow, RMSEs were 4.2 m2/ha for G, 610 trees/ha for TPH, 29.3 m3/ha for V, 2.3 cm for Dg, 1.6 m for Hg, and 1.9 m for Hdom. The assisted workflow notably improved accuracy, yielding RMSEs of 3.5 m2/ha for G, 54.0 trees/ha for TPH, 20.2 m3/ha for V, 1.2 cm for Dg, 1.3 m for Hg, and 1.2 m for Hdom when using closed-loop trajectories. Overall, the results emphasize the importance of assisted workflows for attributes sensitive to detection completeness, particularly TPH, while showing that kinematic MLS can efficiently capture forest structure for sample plot measurements.
Why it matches plant phenotyping methodsMLSによる樹木・林分構造形質の抽出精度を、取得経路と処理ワークフロー間で比較検証しており、フェノタイピング手法が中心である。
abstractThis study evaluated the accuracy of MLS in deriving forest inventory attributes, including basal area (G), number of trees per hectare (TPH), total stem volume (V), basal area-weighted mean tree diameter (Dg) and height (Hg), and dominant height (Hdom).
Abstract In cocoa agroforestry systems (AFS), tree-cover is a fundamental driver of ecosystem functioning and crop productivity. However, for smallholders, implementing specific cover targets remains challenging because tree-cover is difficult to measure without specialized equipment and technical skills. Consequently, sustainability standards and technical guidelines typically rely on simplified structural metrics, such as tree density or basal area, as proxies for cover. This study addresses the technical disconnect between these simplified metrics and actual canopy outcomes. Using data from 150 plots in Côte d’Ivoire, we developed a Beta regression model within a Bayesian framework to estimate tree-cover from standard inventory data. Our results show that tree density or basal area alone are unreliable predictors of cover, whereas a model integrating both variables achieves high accuracy (R2 = 0.89). Our results also demonstrate that existing guidelines can lead to highly variable canopy conditions that deviate markedly from intended cover targets. We propose a state-space representation as a decision-support tool to help smallholders estimate tree-cover and to provide a scientific basis for updating existing policies and standards. This framework allows smallholders to visualize management trajectories, driven by recruitment, tree growth, and mortality, to steer their systems toward ecologically meaningful cover targets.
Why it matches plant phenotyping methods樹冠被覆率という植物群落の構造形質を、標準インベントリデータから推定するベイズ回帰モデルを開発・評価しており、推定精度も検証されているため、方法が中心的です。
abstractwe developed a Beta regression model within a Bayesian framework to estimate tree-cover from standard inventory data.
Abstract To address the issues of detail loss and matching difficulties in fruit tree 3D reconstruction caused by complex branch–leaf morphology, fruit occlusion, and illumination variations, this paper proposes an end-to-end cross-scale collaborative attention multi-view stereo network, termed MSA-MVSNet, for high-quality 3D reconstruction of orchard trees, while integrating semantic segmentation for fruit counting. A multi-scale feature enhancement module is designed to adaptively fuse deep semantic features and shallow fine-grained details through a spatial–channel collaborative attention mechanism, thereby enhancing the network’s capability to represent multi-scale structures such as trunks, branches, and leaves. Multi-branch dilated convolutions are introduced to enlarge the receptive field, and deformable convolutions are incorporated to adaptively capture the irregular geometric shapes of fruits, improving modeling robustness. In addition, a feature matching transformer is introduced to strengthen long-range global contextual correlations within and across images via intra-attention and inter-attention mechanisms, thereby improving matching stability in low-texture and repetitive-texture regions.To validate the effectiveness of the proposed method, experiments are conducted on self-collected real orchard dataset and public benchmark datasets. The results demonstrate that MSA-MVSNet outperforms baseline models by 8.2% in terms of 3D reconstruction quality. Finally, by combining depth filtering with the semantic segmentation results of YOLOv11-Seg, a semantic-guided fruit reconstruction and counting framework is constructed. This framework achieves an overall counting F1-score of 92.8% on the self-collected dataset with varying scene sparsity and 93.5% on the public Fuji-sfm dataset, demonstrating its effectiveness and generalization capability.
Why it matches plant phenotyping methods果樹の3D再構成と果実カウントという植物形質取得を目的に、マルチビュー再構成ネットワークとセグメンテーション統合手法を開発・検証しており、フェノタイピング手法が中心である。
abstractthis paper proposes an end-to-end cross-scale collaborative attention multi-view stereo network, termed MSA-MVSNet, for high-quality 3D reconstruction of orchard trees, while integrating semantic segmentation for fruit counting.
Time-series point clouds have emerged as an effective approach for precise, continuous crop monitoring and quantitative growth analysis. This study constructed a spatiotime-series point cloud dataset containing four species and eleven plant varieties, exploring crop organ instance segmentation, phenotypic parameter extraction, growth quantification, and canopy photosynthesis assessment. A skeleton-based framework for organ-level instance segmentation and time-series analysis is proposed, demonstrating robust performance across all four crops. To fully utilize the time-series data, a novel time-series leaf matching method was introduced, achieving a matching accuracy, defined as the proportion of correctly matched leaves, of over 0.823 for all species. By integrating the matching results with phenotypic parameter extraction, time-series phenotypic data were generated, and a phenotypic variation rate was defined as a suitable metric for quantifying crop growth. Furthermore, these results were integrated into a canopy photosynthesis model to derive key time-series photosynthetic metrics, including photosynthetic rate, absorbed light quantity, light energy utilization efficiency, and each crop organ's contribution to photosynthesis. These metrics provide insights into the crop's growth patterns and photosynthetic strategy. This study offers refined quantitative analysis of crop morphology and photosynthetic parameters through time-series point cloud segmentation, contributing valuable data for advancing plant biology research and enhancing the understanding of crop growth dynamics.
Why it matches plant phenotyping methods時系列点群から作物器官をセグメンテーションし、葉追跡、形態形質、成長量、光合成関連指標を抽出する手法が研究の中心であるため。
abstractA skeleton-based framework for organ-level instance segmentation and time-series analysis is proposed
Reproduction assets foundThe paper's Data availability statement explicitly provides authors' public URLs for a subset of the analysis code (GitHub) and the complete time-series 3D crop point cloud dataset (Baidu pan), both directly supporting this paper's phenotyping measurements and analysis.Code · publicA subset of the code and dataset used in this study is publicly available on our GitHub repository: https://github.com/JiarenZhou/LTPCDCCM .Open asset ↗JiarenZhou/LTPCDCCMlines:578-686Dataset · publicThe complete time-series 3D crop point cloud dataset can be downloaded from https://pan.baidu.com/s/1mNSDz4F0ZjOwmqzMuXozSQ?pwd=1234 .Open asset ↗lines:578-686Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Volume is an important shape descriptor in postharvest quality evaluation and breeding programs of sweetpotatoes and is also valuable for other agricultural engineering applications. Traditional volume measurement methods based on water displacement are, however, laborious, destructive, and unsuitable for high-throughput online scenarios. To address this gap, this dataset was developed to support the advancement of non-destructive, automated online volume estimation using a LiDAR (light detection and ranging)-based three-dimensional (3-D) machine vision system. A total of 200 sweetpotato storage roots of the cultivar "Beauregard" were collected for constructing a 3-D multi-view imagery dataset. Each sample was imaged online using a short-range LiDAR camera (Intel RealSense™ L515) while traveling on a custom-built roller conveyor system that enables simultaneous translation and rotation for full-surface coverage. The curated dataset comprises raw color images (1280 × 720 pixels, .png format) and corresponding raw and segmented point clouds (1280 × 720 pixels, .laz format) for individual samples, alongside the reference volume measurements obtained using the standard water displacement method. In addition, to illustrate the modeling pipeline for volume prediction, the dataset provides the extracted geometric features derived from the segmented two-dimensional (2-D) masks and point clouds, and volume prediction results obtained through regression modeling. As the first publicly available LiDAR-based dataset for sweetpotato volume estimation, this dataset provides a valuable resource for developing and validating image processing pipelines, optimizing machine learning models, and advancing 3-D vision technologies for non-destructive, rapid measurement of the volume of irregularly shaped agricultural products.
Why it matches plant phenotyping methodsサツマイモ貯蔵根の体積という植物器官形質をLiDAR 3D画像から推定する公開データセットであり、取得系・参照測定・特徴抽出・予測結果を含むため、フェノタイピング手法とデータセットが中心です。
abstractthis dataset was developed to support the advancement of non-destructive, automated online volume estimation using a LiDAR (light detection and ranging)-based three-dimensional (3-D) machine vision system.
Reproduction assets foundThe paper's own LiDAR sweetpotato dataset (images, point clouds, ground-truth volumes, feature data, and Python modeling scripts) is publicly deposited on Zenodo with an explicit DOI. The librealsense GitHub link is a generic camera SDK, not a paper-specific asset.Dataset · publicDirect URL to data: https://doi.org/10.5281/zenodo.18378019Open asset ↗Zenodo · 10.5281/zenodo.18378019html-lines:90-113Code · publicThe complete Python modeling script and the associated feature datasets have been included in the public dataset repository [13] to facilitate reproducibility and provide a benchmark for future algorithm development.Open asset ↗html-lines:168-182Plant phenotyping relevance match · UnverifiedCrossref · checked 5 Sept 2026
Fractional vegetation cover of crops (CropFVC) is a critical indicator for remote sensing-based crop monitoring. However, existing inversion models are largely developed for general vegetation types, limiting their effectiveness for crop-specific applications. Here, we developed a gap-fraction-refined hybrid CropFVC model that integrates crop-specific PROSAIL calibration, an ALA (averages of leaf angle) -based dynamic projection function, and a Random Forest model. The model was validated with 43343 CropFVC samples of four major crops (winter wheat, rice, maize, and soybean) across China during March to August 2024, spanning key phenological stages, and further compared against SNAP (10 m) and GEOV3 (300 m) products. Results showed that (1) the proposed model achieved stable performance across diverse canopy structures, with average RMSE
Why it matches plant phenotyping methods作物の葉面積被覆率という明示的な植物キャノピー形質を推定するハイブリッドモデルを開発し、多数のサンプルと既存プロダクトで検証しており、測定・推定手法が研究の中心である。
abstractHere, we developed a gap-fraction-refined hybrid CropFVC model that integrates crop-specific PROSAIL calibration, an ALA (averages of leaf angle) -based dynamic projection function, and a Random Forest model.
The wheat canopy genome harbors abundant yet untapped genetic variation that could be harnessed to enhance yield potential. The green area index (GAI) is a structural metric that reflects the photosynthetically active canopy surface and is closely linked to final grain yield. Current image-based GAI retrieval methods often suffer from signal saturation and coarse structural depiction, constraining downstream genetic analyses. To address this limitation, we constructed a comprehensive image dataset spanning eight field experiments across China and France, encompassing approximately 600 genotypes under six distinct management regimes. Leveraging this diverse data, we developed a multimodal deep-learning framework augmented by simulated-to-realistic (sim2real) synthetic data transfer. This framework fuses nadir and oblique RGB images with accumulated thermal time to produce high-precision, time-series GAI estimates. Validated on independent testing datasets from both China and France, the multimodal approach demonstrated robust performance with an accuracy of R 2 = 0.88 and an RMSE of 0.49 m 2 m -2 , representing an improvement of about 22% over the traditional gap fraction method. In three site-year field experiments involving 565 genotypes, the GAI dynamics derived from the multimodal approach showed higher broad-sense heritability (0.20-0.48) than those from the gap fraction approach (0.02-0.13) and stronger genotypic correlations with yield (0.19-0.40 versus 0.09-0.31). Furthermore, genetic analysis confirmed the biological fidelity of the estimated traits, identifying loci that co-localize with known architectural regulators such as Rht-D1 , TaTB1-4D , and TaBGC1-4D . Consistently, the multimodal-derived phenotypes were specifically enriched in cell-wall remodeling and hormonal signaling pathways (e.g., brassinosteroid) that directly regulate canopy expansion. Overall, the proposed method offers a powerful tool for unlocking genetic gain in canopy architecture and accelerating canopy-targeted wheat improvement.
Why it matches plant phenotyping methodsデュアルアングルRGB画像と熱時間を統合してGAIを推定する深層学習法を開発し、独立データで検証しているため、植物形質取得法が研究の中心です。
abstractwe constructed a comprehensive image dataset spanning eight field experiments across China and France
Reproduction assets foundThe paper publicly releases its pre-trained multimodal GAI-estimation model weights and inference code on Hugging Face, directly reproducing this paper's phenotyping analysis. The raw image and phenology datasets are not public and require contacting the authors.Code · publicThe pre-trained model weights, inference code, and usage instructions are publicly available in the Hugging Face repository at https://huggingface.co/PheniX-Lab/GAI-Estimation/tree/main .Open asset ↗PheniX-Lab/GAI-Estimationlines:259-277Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · checked 14 Sept 2026
Maize phenotyping remains a major bottleneck in genetic analysis and breeding. Despite advances in drones, field robots, and gantry phenotyping systems, ultra-affordable, high-throughput, field-based maize phenotyping at single-plant resolution is still lacking, largely due to the high cost, complex deployment, and limited flexibility of existing platforms under heterogeneous field conditions. To address these challenges, we propose a novel paradigm that integrates DIY imaging devices with customized computer vision–based analytics, and present GLiMPSe ( G iraffe + Li zard M aize P henotyping S yst e m), including two end-to-end phenotyping modules for maize plant architecture (the Giraffe module) and leaf traits (the Lizard module). The imaging device in the Giraffe module are built from modular electronics and 3D-printed parts from local retailers to achieve high-quality image acquisition. The Giraffe and the Lizard modules operate at speeds of 15 seconds and 8 seconds per sample, with costs of $379.1 and $241.1, respectively. Both modules feature fine-tuned YOLOv11x segmentation models for reliable and robust target segmentation, followed by customized Python-based analytical pipelines that enable precise extraction and quantification of phenotypic traits. This methodology achieves high accuracies ( R² ) for five key traits, including plant height (0.928), heights of above-ear leaves (0.87∼0.958), ear height (0.925), above-ear leaf number (0.837), and leaf width (0.937). To enhance accessibility, we developed user-friendly graphical interfaces and publicly released manually annotated datasets and source code to support broader adoption and further innovation. This work provides a practical and accessible solution for high-throughput field phenotyping and offers new opportunities for democratizing crop phenomics through affordable, open-source technologies.
Why it matches plant phenotyping methods低コストな撮像装置、コンピュータビジョン解析、形質抽出パイプライン、GUI、データセットとコードを統合したトウモロコシ表現型測定システムの開発・検証が研究の中心である。
abstractwe propose a novel paradigm that integrates DIY imaging devices with customized computer vision–based analytics, and present GLiMPSe
The diameter of natural rubber trees serves as a critical crop parameter, not only for determining whether rubber trees meet tapping requirements and assessing their growth status, but also playing a significant role in calculating parameters such as tapping angle, trajectory, depth and yield prediction. To further advance the intelligent production level of natural rubber trees and achieve low-cost, automated diameter measurement methods, this study proposes a new approach that combines YOLO11s instance segmentation algorithms with a monocular RGB camera to enable non-contact and non-fixed distance diameter measurement of natural rubber trees. The YOLO11-seg is used to obtain masks and bounding boxes for the ID, trunk, and tapped area. This method employs image processing techniques such as contour smoothing, trunk skeleton extraction, angle calculation, and localization. With using the tree ID tag as the primary dimensional reference and incorporating the segmentation contours of trunk categories, it achieves the measurement and calculation of rubber tree trunk diameter. The results demonstrated that among the compared instance segmentation models, the highest segmentation accuracy mAP50-95ˢᵉᵍ reached 0.934. The diameter estimation based on this segmentation and geometric correction process achieved a root mean square error (RMSE) of 2.85 cm and a mean absolute percentage error (MAPE) of 12.58 % under the original measurement conditions. After error compensation, the RMSE and MAPE decreased to 2.13 cm and 8.68 %, respectively. The proposed method can accurately measure the diameter of natural rubber trees, significantly reducing the hardware cost. It provides a new approach for measuring the diameter of natural rubber trees, and also provides both theoretical support and practical basis for the intelligent production and precision agriculture in natural rubber cultivation.
Why it matches plant phenotyping methodsゴム樹の幹径という植物形態形質を、RGB画像とインスタンスセグメンテーションで非接触・自動推定する手法を開発し、精度検証まで行っており、フェノタイピング手法が中心である。
abstractthis study proposes a new approach that combines YOLO11s instance segmentation algorithms with a monocular RGB camera to enable non-contact and non-fixed distance diameter measurement of natural rubber trees.
Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 5 Sept 2026
Accurate prediction of phenotypes across genotypes and environments is crucial for accelerating crop improvement. Process-based crop growth models (CGMs) can capture complex genotype-by-environment interactions, but their use is limited by labor-intensive genotypic parameter measurements. Here, we developed a faster data assimilation pipeline integrating high-throughput phenotyping (HTP) observations with the SiriusQuality wheat model to efficiently estimate key genotypic parameters and predict genotype performance. Using time-series RGB imagery from a ground-based Phenomobile, we assimilated intercepted photosynthetically active radiation (fIPAR), heading date, and final grain yield to jointly assimilated to calibrate twelve genotypic parameters governing phenology, canopy development, light interception, biomass accumulation, and grain filling. Two data assimilation strategies—a Bayesian DREAM (zs) algorithm and a lookup table (LUT) inversion—were compared through both in silico experiment and eight years of multi-environment field trials of nine durum wheat cultivars. The LUT method demonstrated superior computational efficiency, with prediction accuracy comparable to Bayesian inference on real field data. Multi-year field trials showed that two environments (year / site) were sufficient to reliably characterize genotypic parameters and predict performance across environments. By combining time-series HTP data with ecophysiological modeling, our data assimilation pipeline offers breeders a powerful tool for genotype characterization. It streamlines the process of capturing environmental variance and phenotypic stability, reducing time and effort in crop improvement.
Why it matches plant phenotyping methodsHTP画像を作物成長モデルへ統合するデータ同化パイプラインを開発し、複数アルゴリズムと実圃場データで性能比較・検証しており、表現型取得・推定手法が研究の中心である。
abstractHere, we developed a faster data assimilation pipeline integrating high-throughput phenotyping (HTP) observations with the SiriusQuality wheat model to efficiently estimate key genotypic parameters and predict genotype performance.
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:133-158Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 15 Sept 2026
Reconstruction of crop three-dimensional (3D) point clouds is essential for monitoring phenotypic parameters, like plant height and leaf area index (LAI), which is a critical phenotype predictor for smart crop breeding. The main 3D reconstruction technologies include image-based approaches, laser scanning, and depth camera methods. Among these methods, image-based structure-from-motion (SfM) is widely used due to its low cost and high accuracy. However, field crop canopy image data for high-resolution point cloud construction are often large-scale, unordered, and uncalibrated. Conventional SfM methods struggle with 3D reconstruction due to high computational costs and long processing times, delaying phenotypic analysis. To address this issue, we developed an improved global SfM algorithm, which increases the point cloud reconstruction speed by an average of 1.39 times compared to traditional incremental SfM methods and by more than 10 % on average compared to two mainstream global SfM algorithms. In addition, we integrated three types of predictors, point cloud features, color indices and texture features, through multi-feature data fusion and machine learning. A random forest algorithm for the prediction of LAI for a combined data set of four different crops, and using all three categories of predictors, achieved higher monitoring accuracy compared to using a single feature category (R²=0.78 vs R²=0.71–0.74). This new method, which includes an improved global SfM algorithm and a three-predictor fusion-based LAI monitoring approach, offers an efficient and reliable solution for precise crop phenotyping and continuous growth monitoring in complex field environments, enabling accurate assessment of crop morphology and developmental dynamics.
Why it matches plant phenotyping methods改良したSfMによる3D再構成と、特徴量融合・機械学習によるLAI推定を開発・評価しており、植物表現型取得手法が研究の中心である。
abstractwe developed an improved global SfM algorithm
Accurate retrieval of plant functional traits is critical for monitoring crop growth and improving agronomic management. Canopy structural parameters, such as leaf area index (LAI) and leaf inclination distribution function (LIDFa), strongly influence inversion accuracy. Quantifying canopy structural uncertainties and developing strategies to improve retrieval accuracy are crucial. In this study, we developed an inversion framework based on the Soil Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) model, integrating reflectance and solar-induced fluorescence (SIF) data. Using both simulation modelling and field measurements in NEON STER crop field, we introduced multi-level prior noise and evaluated how uncertainties in LAI and LIDFa propagate into the retrieval of chlorophyll content (Cab), maximum carboxylation rate (Vcmax), and fluorescence quantum efficiency (fqe). To assess the influence of canopy structure and improve retrieval accuracy, three inversion strategies—Prior-Matched (PM), Regularized (RI), and No-Prior (NP)—were designed and tested for their accuracy and robustness. The results showed that second-order Sobol’ indices (S2) captured interactions among canopy structural parameters and functional traits, particularly between Cab-LAI, Cab-LIDFa and fqe-LAI, with sensitive spectral ranges at 680–740 nm (fluorescence) and 600–720 nm (reflectance). Error amplification analysis under six noise levels showed that structural uncertainties significant amplified reflectance and fluorescence variations, with red-edge shifts (ΔRE) and 740 nm fluorescence changes (ΔF740) being most sensitive. Incorporating prior canopy structure information improved inversion accuracy by up to 7.93 % in R² and reduced RMSE by 21.25 %, although this advantage diminished under high noise levels. LAI uncertainty had a greater impact than LIDFa, and additive noise introduced more uncertainty than multiplicative noise. Comparison of the inversion strategies revealed that the RI strategy achieved higher accuracy (simulated data: R²=0.954; measured data: R²=0.799) and greater robustness to noise than the PM strategy. These findings demonstrate the value of integrating canopy structure into computational inversion models to enhance the reliability of remote sensing trait retrieval, supporting precision agriculture and sustainable crop production.
Why it matches plant phenotyping methods植物機能形質のリモートセンシング推定を対象に、SCOPEモデルに基づく反演フレームワークと複数の反演戦略を開発・比較し、ノイズへの頑健性と精度を検証している。形質取得・推定手法が研究の中心である。
abstractwe developed an inversion framework based on the Soil Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) model, integrating reflectance and solar-induced fluorescence (SIF) data.
Wildfires are the leading cause of forest degradation and reduce the carbon sequestration capacity of forest ecosystems. As climate change intensifies fire regimes, accurately mapping burn severity and biomass loss remains difficult, particularly in complex mountainous terrain. Traditional remote sensing approaches based on optical data often fail to capture sub-canopy and structural variations, while field-based methods are constrained by scale, access, and logistical complexity. To address these limitations, this study utilises NASA’s Global Ecosystem Dynamics Investigation (GEDI) LiDAR data to capture three-dimensional forest structure and monitor changes in aboveground biomass. Focusing on the Indian Himalayan Region (IHR), we utilised GEDI structural metrics for fire severity classifications derived from NASA’s Fire Information for Resource Management System (FIRMS) data. Spatial fire density mapping revealed that Evergreen Needleleaf (ENL), Evergreen Broadleaf (EBL), and Deciduous Broadleaf (DBL) plant functional types (PFTs) are particularly vulnerable to recurring fires. The GEDI Level 4 A AGBD product, when tested in the IHR, showed a tendency to underestimate biomass relative to regional field studies, indicating the need for localised calibration in rugged terrain. We found that the pre-monsoon fire season resulted in a biomass loss of 14.33 Mg/ha for the ENL PFT, while the EBL PFT showed a biomass gain of 3.15 Mg/ha. A Random Forest model utilising GEDI structural inputs for fire severity classification achieved an overall accuracy of 85 %. We found that vegetation structural complexity, rather than canopy height, is a more important predictor of high fire severity. We also evaluated the Waveform Structural Complexity Index (WSCI), a novel GEDI metric that effectively characterised fire severity across the under-storey vegetation. These findings underscore the importance of incorporating 3D vegetation structure into fire impact assessments, moving beyond the limitations of conventional two-dimensional optical datasets.
Why it matches plant phenotyping methodsGEDI LiDARによる森林構造・バイオマス損失・火災重症度の推定を中心に、既存AGBD製品の地域較正と新規指標WSCIの評価を行っており、植物状態の取得・推定手法が中核である。
abstractwe utilised GEDI structural metrics for fire severity classifications
Lodging is a complex trait that limits wheat (Triticum aestivum L.) yield potential, and no single trait can fully capture lodging resistance. Identifying key traits and developing reliable, field-applicable indicators are crucial for breeding lodging-resistant cultivars. In this study, lodging resistance was systematically assessed in 274 wheat varieties across three consecutive growing seasons (2022–2024). Genotype, growing season, growth stage, and their interactions significantly affected lodging-associated traits, with a clear temporal alignment between meteorological conditions and lodging events. Comparative analysis between lodged and non-lodged plants revealed that lodging negatively influenced spike and kernel traits. Multivariate analyses indicated that height-related traits accounted for nearly 50 % of the phenotypic variance related to lodging resistance and showed negative correlations, while traits related to stem weight and fullness explained 24 % and 8 %, respectively. Among these, stem wall thickness (SWT), second basal internode fullness (SBF), single stem elasticity (SSE), and stem strength (SS) emerged as key positive contributors, whereas plant height (PH), center of gravity height (CGH), and basal internode lengths were negatively associated. Stepwise regression and path analyses further identified SWT and SBF as primary determinants of SS, while CGH was the key factor influencing SSE. Structural equation modeling demonstrated that height-related traits exerted significant negative effects on stem anatomical structure, mechanical traits, and lodging index. Furthermore, a novel lodging index, defined as the SSE-to-CGH ratio, was proposed. It exhibited a strong correlation with the comprehensive lodging score (D value) and high consistency with clustering results, providing a practical assessment tool. These findings provide valuable insights for assessing lodging resistance and guiding strong-stem breeding strategies in wheat.
Why it matches plant phenotyping methods小麦の倒伏抵抗性を複数形質から統合的に評価し、新規倒伏指数を提案して既存スコアやクラスタリング結果で検証しているため、形質評価手法の開発・検証が中心である。
abstractFurthermore, a novel lodging index, defined as the SSE-to-CGH ratio, was proposed.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 5 Sept 2026
Abstract Advances in automation, imaging, and artificial intelligence have enabled large-scale plant phenotyping, but image analysis remains a critical bottleneck for crop improvement and biological discovery. We developed an integrated multispectral phenotyping framework using imagery from the Texas A&M AgriLife Precision Automated Phenotyping Greenhouse and expanded Plant Growth and Phenotyping (PGP v2) data across maize, cotton, rice, and sorghum. The pipeline integrates pseudo-RGB generation, plant detection and segmentation, image stitching, vegetation-index analysis, texture analysis, morphological trait extraction, and temporal comparison of image-derived features to quantify changes in plant structure, spectral reflectance, and texture over time. Among the evaluated segmentation approaches, SAM v3 provided the highest and most consistent accuracy across diverse crop structures, although it required greater computational time than classical methods. SAM2Long maintained plant-instance associations across vertically stacked frames, while Scale-Invariant Feature Transform (SIFT)-based stitching reconstructed plant mosaics when individual plants extended beyond a single field of view. For each plant and imaging date, the pipeline generated an 863-dimensional feature vector spanning vegetation indices, spectral statistics, texture descriptors, and morphological traits. The framework was evaluated through two case studies: treatment-level temporal analysis of mutagenized sorghum lines and cold-stress phenotyping of maize using a separate imaging system. In both studies, the extracted features supported statistical and multivariate analyses of phenotypic variation and enabled separation of plants based on treatmentor stress-related responses. The combined dataset and workflow provide structured, automated, and well-documented phenotypic analysis across multiple crops, experimental settings, and imaging systems for controlledenvironment plant science and crop improvement. Plain Language Summary Temporal imaging of plants in controlled environments helps scientists better understand growth and biological processes. However, analyzing large volumes of images has been limited by a lack of automated tools. Multispectral imagery captures additional information about plant pigments, structure, and stress beyond standard color images. We developed an automated analysis pipeline that identifies individual plants, tracks their growth over time, and measures traits such as height, area, shape, texture, and vegetation indices. Using artificial intelligence, the system efficiently processes thousands of images to provide consistent and repeatable measurements. By integrating engineering and plant biology, this work supports data-driven decisions for crop improvement and agricultural research.
Why it matches plant phenotyping methods植物画像から形態・スペクトル・テクスチャ形質を抽出する統合パイプラインの開発と評価が中心であり、植物フェノタイピング手法として明確に該当する。
abstractWe developed an integrated multispectral phenotyping framework
Rice stem performs assimilate transport and promises sturdiness due to cell wall structure and composition. However, less is known about the genetic basis of its structural characteristics. In this study, for the first time, the scanning electron microscope (SEM) imaging technique was developed to capture digital phenotypes to assess 18 straw traits collected from the cross-sections of 147 rice accessions. Genome-wide association studies (GWAS) identified 54 significant single-nucleotide polymorphisms (SNPs; integrated into 28 quantitative trait loci) residing in the genic sequences of rice (promoter and coding DNA sequence), and classified into three groups: 1) cell wall-defining genes, 2) cell size-defining genes, and 3) transcription factors. DUF246 and DUF1218 , galactose oxidase , mitochondrial Rho GTPase , WUSCHEL-related homeobox 5 and scarecrow-like 9 are the novel genes identified among the 21 candidate genes. These genes may play roles in stem development traits, specifically the distance from the vascular bundle to the end of the parenchymal cells (DVBEPC) and the thickness of the straw cell wall in the protruding part (TSCWP). Post-GWAS analyses showed one significant haplotype on chromosome 4 and 25 significant epistatic interactions. Most notably, nine TF families were repeatedly detected among the significant QTL. Os07g0644300 (XPA-binding protein 2), located in the q7-1 genomic segment and associated with DVBEPC, was found to have a missense mutation. Phenotyping via SEM imaging provides precise genome-phenome association in understanding rice stem cell size and cell wall architecture, which ultimately can define biomass and lodging resistance. systematic scheme of the current study • This study pioneers the use of SEM imaging to digitally phenotype rice stem traits, revealing the genetic basis of cell size and wall structure using GWAS. • The GWAS studies identified 28 QTLs and 21 candidate genes, including novel ones linked to stem strength and architecture. • The findings from the study enhance our understanding of rice stem development and provide a foundation for improving biomass and lodging resistance through precise genome-phenome associations.
Why it matches plant phenotyping methodsSEM画像を用いたイネ茎のデジタル表現型取得法の開発が研究の中心で、18形質を定量化しGWASに適用しているため。
abstractthe scanning electron microscope (SEM) imaging technique was developed to capture digital phenotypes to assess 18 straw traits
Field / plotNeRF / 3D Gaussian SplattingPhotogrammetry / SfM / MVSLiDAR / point cloudLeafStem / branchWhole plant / canopy / plot / fieldMorphology / geometry measurement2D/3D reconstructionSkeletonization / topology
Saplings are key indicators of forest regeneration and overall forest health. However, their fine-scale architectural traits are difficult to capture with existing 3D sensing methods, which make quantitative evaluation difficult. Terrestrial Laser Scanners (TLS), Mobile Laser Scanners (MLS), or traditional photogrammetry approaches poorly reconstruct thin branches, dense foliage, and lack the scale consistency needed for long-term monitoring. Implicit 3D reconstruction methods such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) are promising alternatives, but cannot recover the true scale of a scene and lack any means to be accurately geo-localised. In this paper, we present a pipeline which fuses NeRF, LiDAR SLAM, and GNSS to enable repeatable, geo-localised ecological monitoring of saplings. Our system proposes a three-level representation: (i) coarse Earth-frame localisation using GNSS, (ii) LiDAR-based SLAM for centimetre-accurate localisation and reconstruction, and (iii) NeRF-derived object-centric dense reconstruction of individual saplings. This approach enables repeatable quantitative evaluation and long-term monitoring of sapling traits. Our experiments in forest plots in Wytham Woods (Oxford, UK) and Evo (Finland) show that stem height, branching patterns, and leaf-to-wood ratios can be captured with increased accuracy as compared to TLS. We demonstrate that accurate stem skeletons and leaf distributions can be measured for saplings with heights between 0.5m and 2m in situ, giving ecologists access to richer structural and quantitative data for analysing forest dynamics.
Why it matches plant phenotyping methodsNeRF・LiDAR SLAM・GNSSを融合した幼木の3D再構成・定位パイプラインを開発し、樹高、分枝、葉対木質比などの植物形質をTLSと比較検証しており、表現型取得手法が中心である。
abstractIn this paper, we present a pipeline which fuses NeRF, LiDAR SLAM, and GNSS to enable repeatable, geo-localised ecological monitoring of saplings.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
Heat stress damage leads to yield penalties in many wheat-growing areas. Climate change models predict warmer scenarios and more frequent heat shocks. Consequently, wheat breeders need to develop more productive varieties for warm conditions, and therefore, the identification of heat-tolerance traits is needed. Albedo is an integrative trait of the optical properties of the canopy defined as the ratio of reflected light to total light received. High albedos in warm conditions may help reduce damaging radiation. Despite its potential relevance for heat avoidance, albedo has been little explored in wheat breeding. In this work, a selection of 30 wheat ( Triticum aestivum L.) genotypes of diverse origin were sown at two sowing dates in Australia (NSW) in 2018 and 2019. A high-throughput phenotyping method based on spectroradiometer measurements [Analytical Spectral Devices (ASD)] to measure canopy albedo was developed to explore its relationship with temperature and other heat tolerance-related traits. ASD albedo was validated via continuous albedometer measurements on a subset of genotypes. Data were captured at flowering (one of the most critical periods for heat-related damage). Genotypic differences for albedo were found in most environments. However, genotypic effects were most noticeable at noon in optimally sown materials (H 2 0.71–0.86). Albedo was directly related to canopy architecture and light interception (r = 0.74) and varied depending on genotype and genotype by environment interaction. Air temperature in the canopy profile and canopy temperature (CT) were also monitored continuously in a subset of genotypes to explore the relationship between albedo and canopy micrometeorology. Canopies with higher albedos had larger air temperature differences across the canopy profile at the flowering stage (r = 0.48). However, canopy temperature was not related to albedo, even though it was strongly correlated (r = 0.99) with air temperature around the spike. Overall, these results indicate that canopy architecture is the primary influence on albedo under warm conditions. Although higher albedo was not associated with lower canopy temperature, its influence on canopy micrometeorology suggests that albedo may contribute to heat avoidance and could therefore be considered an additive trait for phenotyping and breeding for environments under high temperatures.
Why it matches plant phenotyping methods小麦群落アルベドを分光放射計で測定するハイスループット表現型計測法を開発し、連続アルベドメーターで検証しているため、方法が研究の中心である。
abstractA high-throughput phenotyping method based on spectroradiometer measurements [Analytical Spectral Devices (ASD)] to measure canopy albedo was developed
Accurate per-branch 3D reconstruction is a prerequisite for autonomous UAV-based tree pruning; however, dense disparity maps from modern stereo matchers often remain too noisy for individual branch analysis in complex forest canopies. This paper introduces a progressive pipeline integrating DEFOM-Stereo foundation-model disparity estimation, SAM3 instance segmentation, and multi-stage depth optimization to deliver robust per-branch point clouds. Starting from a naive baseline, we systematically identify and resolve three error families through successive refinements. Mask boundary contamination is first addressed through morphological erosion and subsequently refined via a skeleton-preserving variant to safeguard thin-branch topology. Segmentation inaccuracy is then mitigated using LAB-space Mahalanobis color validation coupled with cross-branch overlap arbitration. Finally, depth noise - the most persistent error source - is initially reduced by outlier removal and median filtering, before being superseded by a robust five-stage scheme comprising MAD global detection, spatial density consensus, local MAD filtering, RGB-guided filtering, and adaptive bilateral filtering. Evaluated on 1920x1080 stereo imagery of Radiata pine (Pinus radiata) acquired with a ZED Mini camera (63 mm baseline) from a UAV in Canterbury, New Zealand, the proposed pipeline reduces the average per-branch depth standard deviation by 82% while retaining edge fidelity. The result is geometrically coherent 3D point clouds suitable for autonomous pruning tool positioning. All code and processed data are publicly released to facilitate further UAV forestry research.
Why it matches plant phenotyping methods樹木の枝を対象に、ステレオ深度推定・セグメンテーション・深度最適化による枝単位の3D形状抽出手法を開発・評価しており、植物器官の表現型取得が中心である。
abstractThis paper introduces a progressive pipeline integrating DEFOM-Stereo foundation-model disparity estimation, SAM3 instance segmentation, and multi-stage depth optimization to deliver robust per-branch point clouds.
Lignin plays a central role in the formation and function of secondary cell walls in vascular plants. However, the structural consequences of lignin modification for cell wall properties and cellular function in grasses remain poorly understood. Here, we investigated how cinnamyl alcohol dehydrogenase (CAD) deficiency alters vascular cell architecture in Sorghum bicolor, using the brown midrib-6 (bmr6) mutant as a model system. Biochemical and histochemical analyses confirmed altered lignin chemistry in bmr6, including increased incorporation of hydroxycinnamaldehyde residues and reduced tricin levels. We applied ptychographic X-ray computed tomography (PXCT) to quantify the cell wall geometry, in three dimensions, at nanometer-scale resolution. PXCT enabled measurements of wall thickness distribution and lumen shape along tracheary elements. Analyses revealed no significant differences in wall thickness between wild-type and bmr6 plants. However, three-dimensional morphometric descriptors indicated reduced lumen convexity in bmr6, suggesting localized modifications not detectable by conventional two-dimensional imaging. Water flow numerical simulations through PXCT-derived images indicated reduced vessel permeability and simulated hydraulic conductivity in bmr6, suggesting that subtle geometric changes may influence performance. These findings highlight the value of three-dimensional imaging for resolving cell wall organization and provide new insight into the architectural resilience of grass xylem in response to targeted lignin modification. HighlightThree-dimensional X-ray nano-imaging reveals alterations in the cell wall architecture that affect simulated hydraulic performance under reduced CAD activity in sorghum.
Why it matches plant phenotyping methods植物の木部細胞壁形状をナノスケール3D画像から定量化するPXCT手法が研究の中心であり、壁厚・内腔形状・形態記述子を抽出しているため、植物表現型計測の実質的な適用に該当する。
abstractWe applied ptychographic X-ray computed tomography (PXCT) to quantify the cell wall geometry, in three dimensions, at nanometer-scale resolution.
We present a novel method for accurately counting winter wheat tillers based on RGB images from hand-collected samples. An efficient sample preparation method assembles wheat tillers into bundles from which individual tillers are robustly detected automatically, using classical image analysis. A custom-made user interface ('TillerCounter' program) allows adjusting the automatic detections interactively, which leads to highly accurate tiller counts comparable to the ground truth obtained by manual counting. The key contributions of our work include:1.An efficient method for imaging straw tillers based on bundle assembly.2.An extensive study of the obtained image quality and comparison with the ground truth data from manual counting.3.Demonstration of the approach's high accuracy using correlation analysis (Pearson correlation coefficient R = 0.973 compared to ground truth) and error analysis (root mean squared relative errors below 5 %).
Why it matches plant phenotyping methods小麦分げつ数という植物形態形質を、画像取得・古典的画像解析・専用ソフトウェアで自動推定し、手動計数を基準に精度検証しているため、フェノタイピング手法が中心です。
abstractWe present a novel method for accurately counting winter wheat tillers based on RGB images from hand-collected samples.
Reproduction assets foundThe paper's authors publicly released the TillerCounter GUI source code on GitHub, which implements the Hough-transform-based tiller counting analysis used in this study. The paper also cites original image/count data at Zenodo (10.5281/zenodo.14446564), but no Zenodo URL is present in the allowed URL list, so only theCode · publicThe source code of the TillerCounter GUI is given at https://github.com/agroscope-ch/TillerCounterGui. Original data is given at Zenodo repository: 10.5281/zenodo.14446564Open asset ↗agroscope-ch/TillerCounterGuihtml-lines:163-195Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Cuscuta spp. are stem holoparasitic plants that use haustoria to draw water, photosynthates, and nutrients from host plant vascular systems. Cuscuta has served as a model plant for understanding plant-plant interactions and haustoria development of stem parasitic plants; however, studies of the three-dimensional (3D) internal host-parasite interface and interconnections are limited due to their unique structures developed inside host stems. This study investigates laser ablation tomography (LATscan) technology, which generates 3D reconstructions from stacked high-resolution 2D cross-sectional images. LATscan imaging of Cuscuta invading Arabidopsis (Arabidopsis thaliana) and beet (Beta vulgaris) stems yielded 3D renderings and detailed images of the anatomy of Cuscuta-host tissue interactions, including Cuscuta searching hyphae penetrating the host vasculature. Laser-tissue interactions generated color contrast and facilitated easy differentiation between Cuscuta and host tissues in 3D renderings and 2D images, demonstrating that LATscan technology can be an efficient tool to investigate the development and function of host-parasitic plant interactions.
Why it matches plant phenotyping methodsレーザーアブレーション断層撮影による植物組織の3D画像化・再構成が研究の中心であり、宿主—寄生植物組織の形態・構造状態を取得する手法を実証している。
abstractThis study investigates laser ablation tomography (LATscan) technology, which generates 3D reconstructions from stacked high-resolution 2D cross-sectional images.
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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does it matter? Trends in plantOpen asset ↗LatifaGreche/3D-WheatSpikeMorphologyExtractionpdf-raw-page:26 lines:1-57Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Plant microtechnique involves a sequence of skill-intensive histological procedures that often yield poorly reproducible images and limited quantitative information. Nevertheless, it provides an essential cellular and tissue context needed to understand biological functions. In this work, we present an optimized resin-based microtechnique that replaces paraffin embedding, incorporates a chemically activated adhesive treatment for glass slides, and develop a trichrome stain for resin sections. All these improvements enhanced section stability and image reproducibility, enabled a broader color palette with sharp contrast of tissues, cells and organelles, and selected ultrastructural features using light microscopy. Based on these preparations, a quantitative micrograph analysis workflow was developed based on image segmentation and feature extraction using MATLAB (R2024a) and Adobe Photoshop (CS6). This approach enables the measurement of a wide range of morphometric and compositional features, generating structured histological datasets that we refer to as plant histolomes. As an illustrative application, this workflow was applied to leaves from several model plants species and integrated multiple anatomical traits into a composite feature, the “C4 Kranz-anatomy level”, enabling quantitative comparison along the C3-C4 anatomical transition. The resin-based microtechnique and the histolomics framework developed in this work provide a robust and reproducible basis for the quantitative plant histology, bridging classical microscopy with a data-driven tissue analysis.
Why it matches plant phenotyping methods樹脂包埋・染色による植物組織画像の再現性向上と、画像セグメンテーション／特徴抽出による形態・組成形質の定量化が研究の中心であり、植物フェノタイピング手法の開発・検証・適用に該当する。
abstractIn this work, we present an optimized resin-based microtechnique
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
BACKGROUND: Kale (Brassica oleracea var. acephala) is a high value leafy vegetable with an extensive domestication history and germplasm diversity, making it an ideal target for genetic improvement. To meet growing food security needs particularly with controlled environment agriculture (CEA) systems, specialized breeding strategies are required. The goal of this study was to survey the phenotypic architecture of a global kale germplasm collection under commercial CEA conditions. This study establishes a phenotypic baseline and serves as a hypothesis generating resource for future genetic and physiological studies in kale and other leafy vegetables grown under CEA. RESULTS: A total of 203 kale accessions were phenotyped for 113 quantitative traits using high-throughput phenotyping methods. Significant differentiation was observed across all traits, with coefficient of variation ranging from 2.5% to 180.7%, confirming broad genetic variability among accessions. Trait correlation networks and hierarchical clustering grouped phenotypes into seven biologically corresponding modules including leaf, stem and root morphology, plant architecture, hyperspectral indices, and seedling growth. These modules highlight coordinated phenotypic patterns among traits. Integrative yield analyses combining partial least squares variable importance in projection with differential trait analysis identified 28 phenotypes most strongly associated with total aboveground fresh weight, a robust proxy for CEA vegetative yield. Principal component analysis further distilled these traits into three orthogonal components explaining 87.1% of total yield variation. These components represented modules related to plant organ size, canopy structure, and density, emphasizing their biological contribution to harvestable biomass. CONCLUSIONS: This study generates a foundational phenomics resource and comprehensive dissection of kale’s yield architecture under CEA conditions. The composition of traits identified constitutes a targeted set of breeding traits to be further validated for improved leafy vegetable yield. By integrating large-scale germplasm resources with phenomics, this work establishes the utility of a high-throughput phenotypic analysis for further leafy crop research and improvement.
Why it matches plant phenotyping methods大規模なハイスループット植物表現型解析を中核とし、113形質の取得、統合解析、再利用可能なフェノミクス資源の構築を行っているため。
abstractA total of 203 kale accessions were phenotyped for 113 quantitative traits using high-throughput phenotyping methods.
Abstract PlantCV is an open‐source Python project aimed at developing tools to address a range of image‐based, plant phenotyping questions. PlantCV has been used for more than 10 years to automate trait collection from image data, and the newest release, PlantCV version 4, continues to lower the barrier to entry for users without substantial coding experience through extensive example use‐case tutorials and simplified installation. In addition to usability, we document added functionality since the release of PlantCV v2, including support for more image types such as fluorescence, thermal, and hyperspectral data. Finally, we describe the development of a new subpackage focused on morphological trait measurements like leaf angle, and demonstrate its utility as compared to more manual methods of data collection.
Why it matches plant phenotyping methodsPlantCV v4は、画像から植物形質を自動抽出するオープンソースソフトウェアの開発・機能拡張・比較評価を主題としており、植物フェノタイピング手法が中心である。
abstractPlantCV is an open‐source Python project aimed at developing tools to address a range of image‐based, plant phenotyping questions.
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the authors' analysis scripts on GitHub (danforthcenter/plantcv-4-paper), which directly reproduces this paper's phenotyping analyses.Code · publicerest.
DATA AVA I L A B I L I T Y S TAT E M E N T
Links to code, tutorials, documentation, and other resources
are available on the PlantCV homepage at https://plantcv.org. PlantCV source code is available on GitHub at https://
github.com/danforthcenter/plantcv. Scripts used for analyses
in this paper are available on GitHub at https://github.com/danforthcenter/plantcv-4-paper.O RC I D
HaleySchuhl https://orcid.org/0000-0002-8825-8297
KeelyE. Brown https://orcid.org/0000-0002-5371-5830
ParagK. Bhatt https://orcid.org/0000-0002-0396-6412
DominikSchneider https://orcid.org/0000-0002-5846-5033
Anna L. Casto https://orcid.org/0000-0002-9597-0514
Lucia Acosta-Gamboa https://orcid.org/0000-0001-77Open asset ↗danforthcenter/plantcv-4-paperpdf-raw-page:15 lines:1-97Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Analyzing morphological parameters and growth at the cell level is crucial for a better understanding of organ development. The most popular approach for quantitative analyses of development relies on confocal imaging of an organ expressing a fluorescent membrane marker over several time points, and analyzing the confocal dataset to quantify changes in morphological parameters and growth rate. These analyses are commonly done on the surface, with the assumption that changes in the surface of a cell reflect faithfully changes of the whole, volumetric cell. However, this assumption has not yet been systematically and explicitly tested. It is also not clear how the correlation between areal and volumetric measurements would change over time. Here, we combined time-series live imaging and three-dimensional reconstruction to compare surface and volumetric size and growth of cotyledon and sepal epidermal cells in Arabidopsis thaliana. We found that on average, surface area is tightly correlated with volume in both cell size and growth, supporting the use of surface area as a good proxy for volume at the population level. However, cells with similar surface areas or surface growth can display substantial differences in volume and volumetric growth. This happens due to variation in cell thickness, which in turn is controlled by microtubules. These findings highlight limitations of surface-based metrics, and call for volumetric analyses if a more accurate assessment of cell parameters is needed.
Why it matches plant phenotyping methods植物細胞の表面積・体積・成長をライブイメージングと3次元再構成で比較し、表面積を体積の代理指標として検証することが中心である。
abstractHere, we combined time-series live imaging and three-dimensional reconstruction to compare surface and volumetric size and growth of cotyledon and sepal epidermal cells in Arabidopsis thaliana.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
Despite their relevance to postharvest engineering and cultivar improvement, the genotype- and environment-dependent variation of the physical and geometrical traits of onion bulb remains poorly characterized in the Republic of Korea. The study evaluated these traits in six commercial onion cultivars grown across two distinct production regions (Muan and Changnyeong), using a randomized complete block design with three replications. A standardized phenotyping workflow combined with image acquisition and imageJ-based trait extraction was employed to measure linear dimensions, including polar and equatorial diameters, neck and bulb thickness. Linear mixed models were used to partition genotype (G), location (L), and G x L interaction effects. Most traits exhibited significant G, L, and G×L effects, indicating strong environmental sensitivity alongside genetic control Combined heritability estimates were high for bulb thickness (0.83), bulb weight (0.66), and diameter- and area-related traits (0.54–0.68), while broad-sense heritability across locations was consistently high (0.71–0.99), particularly for single bulb weight and size traits. Spring Breeze, Katamaru, and Healthy Q consistently produced larger bulbs, while Cheonjujeok and Eomji Nara exhibited smaller bulb dimensions. Trait responses varied markedly between environments, with changes ranging from reduction of approximately 80% to increases exceeding 160%, highlighting pronounced genotype × environment interactions. Hierarchical cluster heatmap analysis revealed strong associations among bulb size–related traits and distinct genotype groupings, with clear location-dependent differences in trait expression between Muan and Changnyeong. These findings demonstrate the utility of image-based phenotyping for robust environment-aware assessment of onion bulb geometry and provides a quantitative basis for region-specific cultivar selection, postharvest system design, and future multi-site breeding evaluations.
Why it matches plant phenotyping methods画像取得とImageJによる形質抽出を組み合わせた標準化フェノタイピングワークフローが、タマネギ球の形態形質測定の中心的手法として明示されているため。
abstractA standardized phenotyping workflow combined with image acquisition and imageJ-based trait extraction was employed to measure linear dimensions
The canopy characteristics of crops are essential aspects for assessing crop growth status and conducting phenotype analysis. As one of the key indicators to measure crop growth situation, accurate canopy coverage assessment can provide a strong foundation for crop growth and yield monitoring. Considering plant growth differences, this study investigated the statistical method for assessing canopy coverage using visual technology, focusing on lettuce as the research subject. Firstly, a multi-variety and multi-growth stage hydroponic lettuce image dataset was constructed, which lays a data foundation for the construction of a semantic segmentation model. Secondly, in order to ensure the precision of semantic segmentation, this study proposed a Channel-Axial-Spatial attention mechanism module from the perspective of feature enhancement. To satisfy the lightweight demands of practical model deployment, this study replaced the original backbone network of PSPNet with MobileNetv3, greatly reduced model complexity while minimizing model performance degradation. Finally, we developed a group lettuce canopy coverage acquisition system by employing Python in conjunction with PyQt5 and embedded the pre-trained models CAS-PSPNet and MobileNetv3-PSPNet into the system for effectiveness verification. By integrating the proposed attention mechanism module with PSPNet, the integrated model outperformed FCN, Unet, SegNet, Deeplabv3+, GCN, ExFusion, ENet, BiseNet, FusionNet, LinkNet, RefineNet, LWRefineNet, and PSPNet in semantic segmentation of lettuce plant groups, achieving a Mean Intersection over Union of 0.9832. The Mean Intersection over Union of PSPNet based on lightweight improvement is 0.9717, and the model size is 9.3M. The results show that the proposed semantic segmentation method can accurately capture the crop canopy coverage, offering a feasible solution for real-time crop growth monitoring.
Why it matches plant phenotyping methodsレタス群落のキャノピー被覆率という植物形質を画像セグメンテーションで推定する手法を開発し、データセット構築、モデル比較、システム実装・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 investigated the statistical method for assessing canopy coverage using visual technology
Estimating canopy structure - leaf inclination distribution (LIDFa), leaf area index (LAI), and fractional vegetation cover (FCover) - is vital for breeding, yet the added value of multi-angular UAV sensing over nadir-only baselines remains insufficiently quantified. This study developed a UAV-based multi-angular inversion framework that derived high-resolution bidirectional reflectance factors (BRF) from oblique photogrammetry and fitted a kernel-driven BRDF model to characterize reflectance anisotropy. Using transfer learning across cultivars and dates, we compared the retrieval performance of multi-angle versus nadir-only baselines for LIDFa, LAI, and FCover. BRDF model simulations agreed well with airborne BRF (optimal R 2 > 0.80, RRMSE R 2 = 0.59 vs. 0.38 for the best MA and NAD models, respectively) and LIDFa ( R 2 = 0.46 vs. 0.37). For FCover, both configurations achieved high accuracy ( R 2 ≥ 0.73), with MA models providing marginal gains ( R 2 = 0.75). Methodologically, CNN-based transfer learning proved most effective for LAI and FCover, while a Random Forest model using raw multi-angle spectra yielded the best results for LIDFa. Optimal viewing configurations were trait-dependent, generally favoring forward scattering directions with zenith angles between 15° and 45°. These results indicate that kernel-driven BRDF modeling effectively captures spectral anisotropy in dense wheat canopies, and that multi-angular observations provide a distinct advantage for retrieving structural parameters with complex scattering behaviors, such as LAI and LIDFa.
Why it matches plant phenotyping methods小麦育種材料のキャノピー構造形質を対象に、UAVマルチアングルセンシング、BRDFモデル、CNN/RFによる推定フレームワークを開発・比較しており、形質取得手法が研究の中心である。
abstractThis study developed a UAV-based multi-angular inversion framework that derived high-resolution bidirectional reflectance factors (BRF) from oblique photogrammetry and fitted a kernel-driven BRDF model to characterize reflectance anisotropy.
Reproduction assets foundThe paper's data availability statement explicitly deposits the complete source code for BRDF modeling and the transfer learning pipeline, plus a subset of preprocessed field data, in a public GitHub repository matching an allowed URL. Additional data are available only on request.Code · publicThe complete source code for BRDF modeling and the transfer learning pipeline, along with a subset of the preprocessed field data used in this study, are openly available in the GitHub repository at https://github.com/ZWM-RS/UAV-multi-angle-inversion-of-canopy-structure-parameters-in-wheat-breeding-materials.git . Any additional data supporting the findings of this study are available from the corresponding author upon reasonable request.Open asset ↗ZWM-RS/UAV-multi-angle-inversion-of-canopy-structure-parameters-in-wheat-breeding-materialslines:451-474Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 13 Sept 2026
Published12 Feb 2026The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciencesCited by 0 · OpenAlex ↗
Abstract. The article presents an approach for multisensors integrating data, namely terrestrial laser scanning (TLS; Leica RTC360), the MandEye mobile SLAM system equipped with a Livox MID-360 LiDAR sensor, and multi-temporal RGB and colour-infrared (CIR) aerial imagery supported by Airborne Laser Scanning (ALS) point clouds, for the detailed inventory of historical gardens and monitoring the process of rebuilding the bosquets in the Lower Gardens of the Royal Castle in Warsaw. The Castle Gardens constitute a unique cultural landscape of exceptional historical and symbolic value, where fragments of pre-war hornbeam bosquets have survived and now form the basis for contemporary restoration efforts. The study demonstrates how the integration of complementary active and passive sensing techniques enables a multi-scale, three-dimensional documentation of vegetation structure, capturing both fine-scale geometric details and broader spatial context. TLS data provide high-precision representations of tree geometry and hedge structure, while mobile SLAM measurements allow rapid mapping of garden interiors and hard-to-access areas. These ground-based datasets are complemented by ALS and photogrammetric point clouds derived from archival and contemporary aerial imagery, enabling the analysis of canopy structure and long-term vegetation growth. Additionally, CIR images were utilised to derive vegetation indices, supporting the assessment of plant vitality and temporal changes in biological condition. The results demonstrate that the proposed multi-source integration framework allows effective monitoring of spatial development, height growth, and health condition of reconstructed bosquets. The approach provides a robust methodological basis for heritage greenery inventory and long-term conservation monitoring, supporting informed decision-making in the management of historic gardens.
Why it matches plant phenotyping methods複数の3D・航空画像・CIRセンサーを統合し、植生構造、樹高成長、植物活力・健康状態を抽出・監視する方法論が研究の中心であるため。
abstractThe study demonstrates how the integration of complementary active and passive sensing techniques enables a multi-scale, three-dimensional documentation of vegetation structure, capturing both fine-scale geometric details and broader spatial context.
Green canopy cover dynamics extracted from high-throughput RGB imagery via automated labeled segmentation enabled nonlinear modeling that accounted for short-term weather variation, explaining up to 56% of protein yield variation in 150 soybean genotypes across ten seasons and enabling prediction in new environments as well as identification of tolerant genotypes.
Why it matches plant phenotyping methods高スループットRGB画像から自動セグメンテーションでキャノピー被覆率を抽出する手法が研究の中心であり、気象変動を考慮したモデル化と新環境での予測まで行っているため、植物表現型手法として採用。
abstractGreen canopy cover dynamics extracted from high-throughput RGB imagery via automated labeled segmentation
The growth status of Kenaf ( Hibiscus cannabinus L.) seedlings directly impacts its yield and quality. Addressing the challenges of inefficient monitoring and quantitative assessment of Kenaf seedlings under saline-alkali conditions, this study developed an automated method for plant identification and canopy coverage estimation during the seedling stage. This approach leverages high-resolution visible light imagery captured by unmanned aerial vehicles (UAVs) combined with deep learning semantic segmentation techniques. First, a UAV imagery dataset of Kenaf seedlings was constructed through geometric and radiometric calibration, image cropping, and sample annotation. Subsequently, three classical semantic segmentation models-FCN, U-Net, and DeepLabV3+-were trained and compared using image enhancement strategies. Model performance was quantitatively evaluated using metrics including Intersection over Union (IoU), accuracy, precision, and F1 score. Results indicate that all three models effectively segmented Kenaf plants from soil backgrounds. U-Net demonstrated optimal overall accuracy and detail retention, DeepLabV3+ exhibited advantages in small-scale object recognition, while FCN offered high computational efficiency, making it suitable for applications demanding real-time processing. Building upon this, the U-Net architecture was enhanced by incorporating a Self-Attention (SE) channel mechanism, further improving model performance to achieve an IoU of 85.99% and an Dice of 92.44%. Based on segmentation results from the enhanced UNet, plant canopy coverage during the Kenaf seedling stage was calculated. Combined with measured dry bark yield per mu, this enabled analysis of growth performance across varieties under saline-alkali conditions, identifying Xiao 3, K5, and Xiao 2 as materials exhibiting strong saline-alkali adaptability. The study demonstrates that this method enables high-precision identification and quantitative analysis of Kenaf seedlings, providing effective technical support for monitoring seedling growth and variety selection in saline-alkali soils.
Why it matches plant phenotyping methodsUAV画像と意味論的セグメンテーションを用いてケナフ幼苗を識別し、キャノピー被覆率という植物形質を推定する手法を開発・比較・改良しており、方法が研究の中心です。
abstractthis study developed an automated method for plant identification and canopy coverage estimation during the seedling stage.
Modeling the time-varying 3D appearance of plants during growth poses unique challenges: unlike most dynamic scenes, plants continuously generate new geometry as they expand, branch, and differentiate. Existing dynamic scene representations are ill-suited to this setting: deformation fields provide insufficient constraints to yield physically plausible scene dynamics, and 4D Gaussian splatting represents the same physical structures with different Gaussian primitives at different times, breaking temporal consistency. We introduce GrowFlow, a dynamic representation that couples 3D Gaussian primitives with a neural ordinary differential equation to model plant growth as a continuous flow field over geometric parameters (position, scale, and orientation). Our representation enables consistent appearance rendering and models nonlinear, continuous-time growth dynamics with full temporal correspondences for every primitive. To initialize a sufficient set of Gaussian primitives, we first reconstruct the mature plant and then learn a reverse-growth process, effectively simulating the plant's developmental history in reverse. GrowFlow achieves superior image quality and geometric coherence compared to prior methods on a new, multi-view timelapse dataset of plant growth, and provides the first temporally coherent representation for appearance modeling of growing 3D structures.
Why it matches plant phenotyping methods植物の成長を対象に、4D再構成と連続的な成長表現を開発し、幾何学的整合性と画像品質を既存手法・新規データセットで比較評価しているため、植物フェノタイピング手法が中心です。
abstractWe introduce GrowFlow, a dynamic representation that couples 3D Gaussian primitives with a neural ordinary differential equation to model plant growth as a continuous flow field over geometric parameters (position, scale, and orientation).
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract Accurate three-dimensional (3D) nuclear instance segmentation is a prerequisite for quantitative phenotyping in volumetric microscopy, yet remains challenging in densely packed tissues, irregular nuclear morphologies, and across heterogeneous imaging modalities. Here we present NucVerse3D, a deep-learning framework for generalized 3D nuclei instance segmentation that combines a residual attention 3D U-Net architecture with a reversible gradient-field representation for robust centroid-aware instance reconstruction. NucVerse3D is trained end to end in 3D using modality-agnostic preprocessing and isotropic scale normalization, enabling deployment across confocal microscopy, two-photon microscopy, light-sheet microscopy, micro–computed tomography, and scanning electron microscopy volumes. We benchmarked NucVerse3D on seven volumetric datasets spanning multiple species and tissues, comprising more than forty thousand manually annotated nuclei, including newly released ground-truth datasets of mouse liver tissue (control and hepatocellular carcinoma) and Drosophila brain glial nuclei. Across datasets, NucVerse3D achieved consistently high precision, recall, F1-score, and average precision, and outperformed the state-of-the-art methods particularly in dense and irregular settings, while remaining competitive on simpler cases. A single generalized model trained on pooled data matched the performance of dataset-specific models, and ablation experiments demonstrated that preprocessing and scale normalization substantially contribute to performance under strict intersection-over-union criteria. To demonstrate the biomedical utility of NucVerse3D, we applied it to three-dimensional liver images from a mouse model of hepatocellular carcinoma (HCC). High-fidelity, nucleus-by-nucleus segmentation enabled the quantification of the Nuclear Decoupling Score (NDS), which captures deviations in nuclear DNA–volume coupling at the single-nucleus level. NDS analysis revealed a progressive increase in nuclear abnormalities within tumor regions, forming spatially coherent domains of dysregulated nuclei and highlighting NDS as a potential quantitative biomarker of dysplastic and tumor tissue. Together, NucVerse3D provides a robust and generalizable solution for 3D nuclear instance segmentation and enables quantitative nuclear phenotyping across imaging modalities. Highlights - NucVerse3D provides accurate 3D nuclear instance segmentation across modalities - Residual attention and gradient fields enable robust separation of dense nuclei - New 3D annotated datasets of mouse liver and Drosophila brain are released - A generalized model achieves performance comparable to dataset-specific training - 3D nuclear phenotyping reveals spatially organized nuclear abnormalities in HCC
Why it matches plant phenotyping methods3D核インスタンスセグメンテーション手法を開発し、多数のデータセットでベンチマークするとともに、核形態状態の定量的フェノタイピングへ応用しているため。
abstractHere we present NucVerse3D, a deep-learning framework for generalized 3D nuclei instance segmentation
Analyzing three-dimensional (3D) phenotypic parameters of maize seedlings is of significant importance for maize cultivation and selection. However, existing methods often struggle to balance cost, efficiency, and accuracy, particularly when capturing the complex morphology of seedlings characterized by slender stems. To address these issues, this study proposes a novel end-to-end automated framework for extracting phenotypes using only consumer-grade RGB cameras. The pipeline initiates with Instant-NGP to rapidly reconstruct dense point clouds, establishing the 3D data foundation for phenotypic extraction. Subsequently, we formulate a directed topological graph-based mechanism. By mathematically defining bifurcation constraints via vector analysis, this mechanism guides a depth-first traversal strategy to explicitly disentangle stem and leaf skeletons. Building upon these decoupled skeletons, organ-level point cloud segmentation is achieved through constraint-based expansion, followed by density-based spatial clustering (DBSCAN) to detect individual leaves. Algorithms combining point cloud geometry with 3D Euclidean distance are also implemented to calculate key phenotypes including plant height and stem width. Finally, single-leaf skeleton fitting is used to estimate leaf length, and principal component analysis (PCA) is adopted to determine the stem–leaf angle, realizing the comprehensive automatic extraction of maize seedling phenotypes. Experiments show that the proposed method achieves high accuracy in extracting key phenotypic parameters. The mean relative errors for plant height, stem width, leaf length, stem-leaf angle, and leaf area are 0.76%, 2.93%, 1.26%, 2.13%, and 3.33%, respectively. Compared with existing methods as far as we know, the proposed method significantly improves extraction efficiency by reducing the processing time per plant to within 5 min while maintaining such high accuracy.
Why it matches plant phenotyping methodsRGBカメラとNeRF・点群処理・骨格解析を統合し、トウモロコシ幼苗の形質を自動抽出する手法を開発・精度評価した研究であり、フェノタイピング手法が中心である。
abstractthis study proposes a novel end-to-end automated framework for extracting phenotypes using only consumer-grade RGB cameras.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
3D phenotyping refers to the quantitative characterization of a plant's structural and morphological traits in three-dimensional space, allowing for a detailed analysis of plant architecture and growth patterns. In recent years, rapid advancements in non-destructive, high-throughput 3D imaging technologies have enabled the precise measurement of these traits. Initially focused on single-plant traits under controlled conditions, the field has now expanded towards robust applications in real-world field environments, enabling large-scale analyses of plant canopies and complex structures. This study focuses on the recent advancements in 3D crop phenotyping using point cloud technologies. It compares sensor technology and its application in controlled environments (Chamber-Crop Phenotyping, CCP) and field conditions (Field-Crop Phenotyping, FCP). Technologies such as Multiview stereo (MVS) reconstruction, LiDAR, and laser triangulation have enhanced plant phenomics by enabling high-throughput, non-destructive measurements of key traits such as canopy structure, leaf area, and stem diameter. This review highlights the strengths of the CCP, where environmental variables and flexibility are tightly controlled, facilitating precise trait measurement, and contrasts it with the challenges of the FCP, where unpredictable factors, such as occlusion, wind, light variability, and terrain complexity, complicate data acquisition. Various sensor platforms, including ground-based robotic systems and unmanned aerial vehicles (UAVs), have been discussed regarding their ability to overcome occlusion and limited sensor range in real-world conditions. The need to transition these technologies from laboratory environments to real-world agricultural applications is emphasized, highlighting their potential to improve crop management and plant breeding through accurate phenotypic trait extraction. Finally, current research gaps and future directions for integrating advanced sensor platforms and analytical techniques in both CCP and FCP settings are identified, emphasizing the need to enhance the scalability and robustness of 3D phenotyping for field applications.
Why it matches plant phenotyping methods3D作物フェノタイピングのセンサー技術、点群処理、対象形質、検証上の課題を中心に扱う方法論レビューであり、植物形質の取得手法が明確に中心である。
abstractThis study focuses on the recent advancements in 3D crop phenotyping using point cloud technologies.
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:192-218Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
Accurate measurement of posture traits, such as curvature and droop, can provide critical insight into the structural phenotype and stress response of tall grasses, including key perennial bioenergy crops. These posture traits vary across genotypes and growth stages and can influence both productivity and harvestability. This study builds on previous efforts to quantify whole-plant posture, extending the analysis to incorporate deep learning and multi-view UAV imagery. High-resolution oblique, sideways, and nadir images were collected over experimental plots comprising multiple grass genotypes with diverse morphologies. A deep learning framework, incorporating instance segmentation models, was developed to classify plants based on posture metrics extracted from multiple viewing angles. By training on oblique and nadir views together, this work explores the degree to which plant posture can be inferred from each perspective, helping evaluate the contribution of each data acquisition approach and exploring the possibility of combined data acquisitions. Validation against manually annotated datasets evaluates the ability of the approach to differentiate posture traits at high resolution, enabling non-destructive, plant-specific phenotyping at scale. The work supports longitudinal monitoring of canopy structure and facilitates the integration of posture descriptors into breeding programs. It also provides a framework for future fusion with LiDAR and Structure-from-Motion (SfM) data to enhance 3D modeling of genotype-specific canopy architectures in switchgrass or other tall grasses.
Why it matches plant phenotyping methods深層学習と多視点UAV画像から植物姿勢形質を抽出・分類する手法を開発し、手動アノテーションで検証しているため、植物フェノタイピング手法が中心である。
abstractA deep learning framework, incorporating instance segmentation models, was developed to classify plants based on posture metrics extracted from multiple viewing angles.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
Efficient and non-destructive cotton organ extraction is crucial for automatic cotton phenotyping. However, limited by leaf occlusion, large model parameters, and inefficient manual observation, it fails to meet current high-throughput phenotyping demands. To address these challenges, this paper propose ELGCot3D, a lightweight 3D point cloud-based cotton organ segmentation method, enabling high-precision segmentation in resource-constrained environments. First, a new module called ELG3D replaces traditional Set Abstraction structures, enhancing local cotton data learning capability via multi-mechanism feature fusion and boosting segmentation accuracy. Second, a cotton-specific feature enhancement module is proposed to secondary optimize the features output from the Feature Propagation layer. This module significantly increases feature discriminability while substantially reducing redundant and high consumption network layers, achieving a balance between performance and efficiency. Finally, a cotton point cloud-adapted training strategy improves model training stability and prediction accuracy. Experimental results on the Crops3D dataset show ELGCot3D achieves 76.7% mIoU and 86.1% OA for cotton segmentation, Meanwhile, the number of parameters and computational complexity are reduced by 50.1% and 50.7%, respectively, demonstrating the model's lightweight characteristics. Notably, it performs well in segmenting other Crops3D crops and exhibits strong generalization on the other cotton point cloud datasets. The proposed method offers a reliable approach for cotton phenotyping and precision agriculture. Future work will extend its high-throughput extraction capability for individual plant organs in large cotton fields, providing breeders with accurate data to support efficient breeding and new variety development.
Why it matches plant phenotyping methods綿花器官の3D点群セグメンテーション手法を開発し、精度・計算量・汎化性能を評価しており、植物表現型取得の方法が中心である。
abstractEfficient and non-destructive cotton organ extraction is crucial for automatic cotton phenotyping.
3D phenotyping of seedlings is crucial to tomato cultivation in greenhouse facilities. Current studies focus on high-quality point cloud reconstruction and artificial intelligence (AI) 3D segmentation to derive phenotypic traits like plant height and crown width, which heavily rely on manual annotation and possess high complexity in deployment. This study proposes a novel AI framework from pixels to points, for efficient 3D plant phenotyping of tomato seedlings. Through the integration of 2D-3D coordinate mapping and AI vision language models, the proposed method enables accurate reconstruction and analysis of 3D phenotypic traits from single-view data. Top-down RGB images and corresponding point clouds with spatial alignment are captured using a binocular camera. Vision language models are employed with the text prompt “plant” to automatically generate bounding boxes and masks, thereby minimizing manual annotation. These outputs are further transferred to a lightweight YOLO11-segment model. The core innovation is established in our 2D-3D mapping strategy, through which plant-specific 3D points are efficiently extracted using only 2D masks. Non-plant points within initial masks are repurposed to determine ground height for improved plant height estimation, while masks are refined using the Excess Green Index to enhance crown width measurement. An mAP₅₀ of 96.0% is achieved by the YOLO11-segment model. Concerning sparse canopy, highly accurate results are yielded by our phenotyping approach, with RMSE values of 1.7 cm for plant height and 1.0 cm for crown width, and R 2 values of 0.93 and 0.95 against manual measurements. For dense canopy, the usage of a reference chessboard improves the performance (RMSE was reduced from 9.57 cm to 2.07 cm). Annotation dependency is significantly reduced, computational complexity is decreased, edge deployment is supported, and efficient technology transfer is enabled by the presented method. Considerable potential is offered for high-throughput screening of elite tomato varieties with desirable agronomic traits. • Real-time low-cost 3D phenotyping of tomato plants is proposed. • Weak labels simplify the 3D plant segmentation. • Segment the 3D point cloud using 2D pixel-masks with spatial alignment. • Vision language models and knowledge transfer further simplify the AI application.
Why it matches plant phenotyping methodsトマト苗の3D表現型を抽出する画像・点群・AI統合手法を開発し、手動測定との精度検証も行っており、表現型取得法が研究の中心である。
abstractThis study proposes a novel AI framework from pixels to points, for efficient 3D plant phenotyping of tomato seedlings.
Abstract Accurate and efficient assessment of forest structure is crucial for both ecological research and effective forest management. This paper introduces DendRobot, an innovative software pipeline developed to automate the inventory of forest sample plots or entire forest stands using terrestrial LiDAR scans or ground-based photogrammetric point clouds. DendRobot incorporates a novel 2D density-based tree-detection algorithm (Detection Rate = 93%) alongside a new vertical clustering approach for estimating tree height. Both methods are implemented together with established and widely trusted methods to process three-dimensional data into GIS layers. By leveraging these algorithms, DendRobot derives key forest inventory metrics of individual trees, including diameter at breast height (Mean Absolute Error = 3.4 cm), tree height (Mean Absolute Error = 0.7 m), tree locations, and crown projection areas at a fine spatial scale with the resolution of individual trees. Additionally, it produces Digital Terrain Models (DTMs), Digital Surface Models, and Canopy Height Models (CHMs) with user-defined resolution, supporting advanced spatial analyses of forest environments and providing information for forest management planning. Optionally, these data can be enriched with individual-tree point clouds, segmented by a novel approach. Designed as a comprehensive tool for forest researchers, managers, and students, DendRobot supports efficient, data-driven decision-making with minimal manual intervention. Initial tests conducted in complex forest environments demonstrate its capacity to streamline workflows and generate forest-stand-scale inventory data with accuracy comparable to state-of-the-art methods and software. DendRobot (available at https://www.dendrobot.czu.cz/) is a user-friendly, free and open-source solution for the practical application of terrestrial LiDAR scanning in real-world forestry challenges.
Why it matches plant phenotyping methodsLiDAR・写真測量点群から個体樹木の胸高直径、樹高、位置、樹冠投影面積を抽出する新規アルゴリズムとソフトウェアを開発・検証しており、植物形質取得が中心である。
abstractThis paper introduces DendRobot, an innovative software pipeline developed to automate the inventory of forest sample plots or entire forest stands using terrestrial LiDAR scans or ground-based photogrammetric point clouds.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 15 Sept 2026
Hyperspectral imaging (HSI) has emerged as a powerful tool for precision agriculture, enabling the non-destructive monitoring of crop biochemical and physiological traits. However, HSI alone lacks structural context, which limits its ability to accurately capture complex canopy architectures and organ-level traits. Integrating HSI with depth-sensing modalities such as Light Detection and Ranging (LiDAR), Red, Green, Blue, and Depth (RGB-D) cameras, and computational reconstruction technique such as photogrammetry enables the generation of three-dimensional hyperspectral point clouds, combining spectral richness with geometric fidelity. This multi-modal fusion enhances crop trait estimation, including biomass, leaf chlorophyll content, canopy height, leaf area, and stress indicators, while improving the robustness of phenotyping under occlusions, shadows, and varying illumination. Dimensionality reduction, feature selection, and machine learning approaches, including deep learning and explainable AI, are useful for handling high-dimensional hyperspectral data and extracting actionable agronomic insights. Moreover, the integration of thermal, radar, and Global Navigation Satellite System (GNSS) data further expands the capabilities of multi-modal sensing, enabling continuous, all-weather crop monitoring and accurate spatial referencing. Despite these advances, most studies to date focus on controlled environments, highlighting the need for field-based validation to ensure the reliability and scalability of HSI-depth fusion techniques. This review consolidates current knowledge on multi-modal hyperspectral and 3D crop reconstruction, highlighting methods, applications, and challenges, and outlines future directions for implementing high-throughput, real-time phenotyping and precision agriculture solutions.
Why it matches plant phenotyping methods植物形質推定のためのハイパースペクトル・深度センシング融合と3D再構成を中心に扱うレビューであり、フェノタイピング手法の方法論的整理が主題。
abstractThis review consolidates current knowledge on multi-modal hyperspectral and 3D crop reconstruction, highlighting methods, applications, and challenges
• ToF camera was evaluated for 3D phenotyping and canopy volume estimation. • Strong correlation was observed between ToF imagery and manual methods. • ToF technology could accurately estimate canopy volume in horticultural crops. • Study highlights the potential of ToF imagery for smart farm management. Noninvasive and accurate quantification of plant architectural traits remains a persistent challenge in horticultural crop breeding and high-throughput phenotyping (HTPP), largely due to labor constraints and limited availability of scalable 3D sensing technologies. Time-of-Flight (ToF) imaging offers a promising active sensing approach capable of generating high-resolution digital replicas of plant canopies for structural trait analysis. This study evaluated the feasibility of deploying 3D ToF imaging for digital phenotyping of three horticultural and ornamental crops, guava, brinjal, and jasmine with a focus on canopy volume estimation. High-resolution 3D point clouds were acquired for five plants per crop across three growth stages during the summer season of 2024. Structural parameters including plant height, width North–South (W NS ), width East–West (W EW ), and 3D aspect ratio were extracted and used to compute canopy volume via a voxel grid method. To validate accuracy, ToF-derived canopy volumes were compared against manually measured volumes estimated using the prolate spheroid volume (PSV) method. The ToF-based digital phenotyping framework demonstrated strong agreement with manual measurements across all crops. Guava exhibited the highest accuracy at the intermediate growth stage (R² = 1.0; RMSE = 0.0004), while brinjal (R² = 0.908; RMSE = 0.004) and jasmine (R² = 0.935; RMSE = 0.4351) showed robust performance at the full-grown stage. Those regression relationships were statistically significant ( p < 0.05), confirming the reliability of ToF-based canopy reconstruction for structural phenotyping. Overall, the findings demonstrate that ToF imaging provides a robust, noninvasive, and scalable framework for digital canopy phenotyping in horticultural crops, supporting precision breeding and structural trait monitoring applications in field environments.
Why it matches plant phenotyping methodsToFによる3D画像取得とキャノピー体積・構造形質の抽出を開発・評価し、手動測定との比較検証を行っており、植物表現型取得法が研究の中心である。
abstractToF camera was evaluated for 3D phenotyping and canopy volume estimation.
Vertical farming offers a promising solution to global food security and urbanization challenges, yet its widespread adoption is hindered by high costs, particularly for lighting. Addressing this requires enhancing light use efficiency (LUE) through intelligent control strategies. While numerous studies have investigated the effects of light intensity on lettuce growth, relatively few have explored the potential benefits of stage-specific light regulation. In this study, we first developed an automated 3D phenotyping pipeline based on multi-view reconstruction to quantify canopy morphology and light interception. Utilizing this quantitative framework, we conducted a dynamic light experiment with lettuce in a commercial plant factory to evaluate four dynamic light-intensity strategies. The proposed 3D phenotyping pipeline demonstrated promising performance for canopy information extraction, with RMSEs for plant height, canopy diameter, and projected leaf area of 0.79 cm, 1.05 cm, and 44.3 cm², respectively. The “high-low-high” dynamic lighting strategy, applying higher light intensity during the early and late growth stages and lower intensity during the mid-growth stage, successfully optimized canopy morphology for better light capture. This treatment significantly increased shoot fresh and dry weights by 28 % and 65 %, respectively, compared to constant lighting. Furthermore, it enhanced LUE based on incident and intercepted light integrals by 67 % and 19 %, while reducing electricity consumption per unit of fresh weight by 24 %. Nutritional quality analysis showed the treatment increased soluble sugars and starch contents. By integrating advanced 3D phenotyping with dynamic light intensity control, this study demonstrates a prototype for intelligent decision-making to enhance yield and energy use efficiency in practical vertical farming.
Why it matches plant phenotyping methods自動3Dフェノタイピングパイプラインを開発し、マルチビュー再構成で植物体形態と光遮断を定量化、精度評価も実施しており、フェノタイピング手法が研究の中心である。
abstractwe first developed an automated 3D phenotyping pipeline based on multi-view reconstruction to quantify canopy morphology and light interception.
In this work, we present a flexible and eco-friendly humidity sensor suitable for early plant leaf stress monitoring. The humidity sensor was fabricated using graphene/PEDOT:PSS hybrid ink deposited via drop-casting method on interdigitated electrodes (IDEs) screen printed on a eco-friendly paper substrate. Contact angle measurement, scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDX) studies were performed to demonstrate hydrophilic nature, surface morphology and elemental analysis, respectively, of the sensing layer. The sensor exhibited excellent sensing performance in the measured relative humidity (%RH) range from 25% RH to 94% RH having a maximum % response of 226.5%. The sensor demonstrated a nearly linear response (adj. R² = 0.99) in the considered range with a slope observed as 3.21%/%RH. Multi-cyclic repeatability and reproducibility analysis further confirmed high reliability and consistent performance of the developed sensor. Furthermore, the capability of the sensor was successfully evaluated through capturing variations in plant physiological health status (under different environmental conditions, such as un-watered, water availability and solar irradiation) via monitoring microclimatic relative humidity (%RH) variations on plant (Epiremnun aureum) leaves. Through establishing the %RH values for healthy crops or plants under normal (well-watered) and stress conditions (un-watered or excessive solar irradiations), sensor seems to demonstrate strong potential for smart agriculture i.e., detecting early plant leaf stress.
Why it matches plant phenotyping methods植物葉のストレス状態を相対湿度センサーで取得するセンサー開発と性能評価が中心であり、植物生理状態の早期モニタリングへ実証適用している。
abstractwe present a flexible and eco-friendly humidity sensor suitable for early plant leaf stress monitoring
Flow disturbances induced by mechanical weeding significantly affect the mechanical behavior of rice seedlings, posing a critical challenge to the mechanization of paddy field operations. To elucidate the mechanisms by which weeding blades affect the mechanical properties of rice seedlings, we developed a coupled CFD model integrating air–water–mud three-phase flow with the flexible structure of rice seedlings. The model accounts for both hydrodynamic forces and the biomechanical properties of seedlings, enabling accurate prediction of deflection, displacement, and stress responses under blade-induced disturbance. Validation experiments, including seedling deflection under steady flow and field measurements of flow fields around operating blades, demonstrated that the model’s predictions deviate from measured data by less than 10 %, confirming its accuracy and robustness. The results indicate that, during operation, maximum seedling stress reached 8.37 MPa, and maximum intra-row displacement was 53.34 mm (≈ 20 % of seedling height) at 10 days after transplanting, decreasing by 29.9 % by 30 days as stiffness increased. Stress concentration occurred primarily at the seedling base and near the water–air interface, indicating critical regions for structural failure. These findings provide new mechanistic insight into the coupled dynamics of seedlings, fluid, and weeding blades, establishing a quantitative foundation for optimizing blade spacing, rotational speed, and working depth to minimize seedling damage during mechanical weeding.
Why it matches plant phenotyping methodsイネ苗の変位・たわみ・応力という植物状態を推定する連成CFDモデルを開発し、実測値で検証しており、植物表現型の取得・推定手法が研究の中心である。
abstractwe developed a coupled CFD model integrating air–water–mud three-phase flow with the flexible structure of rice seedlings
Accurate estimation of tree diameter at breast height (DBH) is essential for forest monitoring, biomass modeling, and carbon accounting. While DBH is traditionally measured in the field, this approach is labor-intensive and costly, especially at large scales. In contrast, tree height can now be efficiently obtained from remote sensing platforms such as airborne LiDAR and photogrammetry, creating opportunities to estimate DBH indirectly. To address this, we developed a species-specific nonlinear framework to predict DBH from tree height and stand-level relative density (RD) in the mixed temperate forests of New Brunswick, Canada. Our analysis used 1807 trees from 653 permanent sample plots (1985–2014), representing six dominant species: Abies balsamea, Acer rubrum, Acer saccharum, Picea mariana, Picea rubens, and Picea glauca. Allometric (height-only) models explained part of DBH variation, with R² ranging from 0.15 to 0.35 (broadleaves) and 0.41–0.74 (conifers), but predictive accuracy was notably low for Acer rubrum and Acer saccharum. Incorporating RD as a competition index substantially improved model performance, with R² increasing to 0.85–0.89 (broadleaves) and 0.72–0.88 (conifers). Prediction errors (RMSE and MAE) consistently decreased, with broadleaves showing the greatest improvement compared to conifers, reflecting their stronger sensitivity to stand density. These findings demonstrate that combining tree height with RD provides reliable estimates of DBH across diverse species. The framework bridges ground-based inventory with remote sensing applications, offering a scalable approach for biomass estimation, stand density analysis, and sustainable forest management in temperate mixed-species forests.
Why it matches plant phenotyping methods樹木のDBHという明示的な植物形質を、樹高と林分密度から推定する種別非線形モデルを開発しており、形質推定手法が研究の中心である。
abstractwe developed a species-specific nonlinear framework to predict DBH from tree height and stand-level relative density (RD)
Cultivation of dedicated bioenergy crops on marginal lands is critical for sustainable development, yet breeding climate-resilient genotypes remains a major bottleneck. This study introduces a powerful, integrated framework for accelerating the selection of high-performance Miscanthus hybrids adapted to saline-alkaline soils. The framework uniquely combines phenotypic classification (scatter vs. cespitose plant types) with explainable machine learning (ML). An Extra Trees Regressor (ETR) model, optimized through hyperparameter tuning, achieved high predictive accuracy for biomass (R² = 0.916) in a diverse hybrid population. Crucially, SHapley Additive exPlanations (SHAP) analysis revealed two distinct, data-driven ideotypes: the optimal cespitose type maximizes tiller number, whereas the optimal scatter type prioritizes individual tiller biomass and stem diameter. SHAP also uncovered critical physiological trade-offs, identifying potential thresholds for key traits beyond which biomass gains diminish. Applying this framework, we screened 216 genotypes and identified five superior lines. Subsequent two-year field trials on both farmland and saline-alkaline soil validated our approach, with genotype S88 demonstrating superior yield and stability under stress. This study not only establishes a scalable strategy for industrial crop breeding but also offers novel insights into the architectural determinants of yield under marginal conditions.
Why it matches plant phenotyping methods植物形態形質と機械学習を統合した選抜フレームワークが研究の中心で、バイオマス予測、形態型分類、SHAPによる形質解釈、圃場検証まで実施しているため、単なる生物学的実験のルーチン測定ではない。
abstractThis study introduces a powerful, integrated framework for accelerating the selection of high-performance Miscanthus hybrids adapted to saline-alkaline soils.
The study of annual growth rings in Vitis vinifera has recently emerged as a promising framework to explore long-term vine responses to climate and management. This review synthesizes current knowledge on grapevine wood anatomy, xylem functionality, and isotopic signatures, highlighting their role as archives of environmental and agronomic information. Grapevine growth rings provide high-resolution records of climate signals, including drought and heat stress, and reveal cultivar-specific hydraulic strategies shaped by soil conditions and management practices. Stable isotope analyses further complement anatomical chronologies by integrating physiological responses to water availability. Together, these approaches offer valuable insights into the structural memory and plasticity of grapevine xylem, with direct implications for vineyard sustainability and climate adaptation. We also examine the impact of viticultural practices such as irrigation, pruning, grafting, and rootstock choice on xylem architecture, emphasizing how agronomic decisions leave long-lasting anatomical imprints in wood. Finally, we outline future research perspectives, including the integration of dendrochronological, isotopic, and high-resolution imaging techniques, to fully exploit grapevine chronologies as tools for understanding vine resilience and for guiding varietal selection, breeding, and precision viticulture under changing environmental conditions.
Why it matches plant phenotyping methodsブドウの年輪解剖、安定同位体、画像解析を用いて環境応答や木部機能などの植物形質・状態を評価する方法群をレビューしており、測定・解析手法が中心的である。
abstractGrapevine growth rings provide high-resolution records of climate signals, including drought and heat stress, and reveal cultivar-specific hydraulic strategies shaped by soil conditions and management practices.
Rooting systems of plants perceive environmental stimuli and flexibly regulate their growth. Therefore, understanding stimulus perception and response mechanisms is essential for optimizing cultivation. During the transition from aquatic to terrestrial environments, land plants have acquired mechanisms to adapt to gravitational force on land. Thus, elucidating gravity responses of rhizoids in bryophytes, early diverging land plants, provides important insights into how gravity-response mechanisms were established during land plant evolution. Analyzing rhizoid morphology under microgravity, where gravitational effects are largely eliminated, provides an effective approach to examine the gravity-response mechanisms that evolved after terrestrialization. In this study, to elucidate microgravity effects on rhizoid growth of Physcomitrium patens , we analyzed 3D datasets obtained by refraction-contrast micro-CT using synchrotron radiation after fixation and embedding of samples from the Space Moss experiment conducted on the International Space Station. Because each CT volume contains numerous rhizoids, we optimized a WEKA-based machine-learning segmentation approach by improving preprocessing, training, and postprocessing steps, resulting in a significantly improved segmentation accuracy. Comparison of 3D morphological indices between manually segmented rhizoids and predicted results supported the validity of the proposed method for morphological analysis. Morphological analyses revealed that, compared with both ground and artificial 1 × g conditions, rhizoid elongation and gravitropic responses were suppressed under microgravity, leading to reduced vertical growth. These findings indicate that gravity plays a fundamental role in rhizoid morphogenesis, and their absence affects growth orientation and elongation. This study provides foundational data for research on the rooting systems of bryophytes in space.
Why it matches plant phenotyping methodsマイクロCT画像からコケ植物の根茎の3D形態を抽出する機械学習セグメンテーション法を開発・最適化し、手動セグメンテーションとの比較で妥当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す。
abstractwe optimized a WEKA-based machine-learning segmentation approach by improving preprocessing, training, and postprocessing steps, resulting in a significantly improved segmentation accuracy.
With rapid advancements in breeding technologies, phenomics, and artificial intelligence, crop breeding is progressively entering an era of greater precision and efficiency. In this context, three-dimensional (3D) phenotyping techniques-leveraging multidimensional spatial resolution capabilities-have overcome the limitations of two-dimensional (2D) phenotyping in breeding analyses, enabling precise characterization of crop spatial interactions, spatial distribution of plant architecture, and complex 3D structural traits. Recent breakthroughs in computer technology for 3D reconstruction and 3D segmentation have provided robust technical support for crop 3D phenotypic analysis. Furthermore, effective integration of extracted 3D phenotypic data with genotypic data serves as a powerful tool for future research on crop gene function and genomics-assisted breeding. This review systematically examines major advances in 3D phenotyping techniques and their representative applications, with particular emphasis on innovations in 3D phenotyping and analytical methodologies. In parallel, we describe the latest interdisciplinary advances in 3D phenotyping within crop functional genomics research and genomics-assisted breeding. We objectively evaluate the advantages and limitations of 3D phenotyping compared with 2D approaches to assist breeders in selecting appropriate technologies. Finally, we propose future perspectives to promote deeper integration of phenomics and breeding technologies. Despite existing conceptual and technical challenges, it is foreseeable that cross-disciplinary integration of phenomics and genomics will offer promising prospects for crop breeding.
Why it matches plant phenotyping methods3D植物フェノタイピング技術と解析手法の進展を体系的にレビューしており、植物形態・構造形質の取得方法が中心である。
abstractThis review systematically examines major advances in 3D phenotyping techniques and their representative applications, with particular emphasis on innovations in 3D phenotyping and analytical methodologies.
The leaf blade and vasculature develop together within a shared morphological space. Despite shared molecular patterning pathways, it is unknown if developmental and evolutionary variation affect these tissues separately or together in a coordinated way. Grapevine leaves have a morphometric history and abundant data measuring the shape of the blade and vasculature together. Using a combination of topological data analysis and deep learning, we perform reciprocal semantic segmentation of leaf blade and vasculature. Each tissue contains sufficient information to predict the other. We hypothesize that this is due to a one-to-one relationship between blade and vein. Using thin plate splines to swap and warp different combinations of blade and vein shapes, we show that a set of leaves with a many-to-one relationship of blade and vein are distinguishable from true leaves. We also swap blade and vein across the developmental series and between species and show that only reversing the developmental series disrupts the relationship between blade and vasculature. We end by discussing the evolutionary and developmental implications that there is a unique, one-to-one mapping between blade and vein that allows each to be predicted from the other. Author summary Leaves are made of two closely connected parts: the flat blade that captures light and the network of veins that transports water, nutrients, and developmental signals. Although these tissues grow together and share common molecular patterning pathways, it has remained unclear whether a particular blade shape is uniquely linked to a specific vein pattern. In this study, we use grapevine leaves as a model system and combine mathematical shape analysis with deep learning to examine this relationship. We show that the shape of the blade alone can accurately predict the vein network, and that the vein network can likewise predict the blade. This finding suggests a near one-to-one relationship between these two tissues. To test this idea, we created artificial leaves in which blade and vein shapes were deliberately mismatched. Although these synthetic leaves appeared realistic at a global level, a neural network was able to distinguish them from real leaves based on subtle differences. We further show that this tight coupling is maintained by the developmental sequence of leaf growth rather than by species identity, revealing a conserved constraint linking leaf form and internal structure.
Why it matches plant phenotyping methods葉身と葉脈の形状を深層学習による相互セグメンテーションと形状解析で抽出・予測する方法が研究の中心であり、植物形態表現型の方法開発に該当する。
abstractUsing a combination of topological data analysis and deep learning, we perform reciprocal semantic segmentation of leaf blade and vasculature.
Reproduction assets foundThe Data Availability Statement explicitly lists three public Zenodo deposits containing data and code to reproduce the paper's analyses: reciprocal U-Net blade/vein prediction, the two-tower CNN 1:1 vein:blade relationship, and interspecies/intraspecies developmental series swaps. All URLs are in allowed_urls and the Code · public393
which the relationship between blade and vasculature is conserved or diversified across the
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spectacular variety of leaf shapes remains to be seen.
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Data and code to reproduce this work can be found for the following analyses: Reciprocal
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prediction of vein and blade from the other, https://zenodo.org/records/16920155; Two tower CNN
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1:1 vein:blade relationship, https://zenodo.org/records/17014105; Interspecies and Intraspecies
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developmental series swaps, https://zenodo.org/records/17013783
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Conflict of Interest Statement
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CC-BY-NC-ND 4.0 International license
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(which was not certified by peer review) is the aOpen asset ↗zenodo · 16920155pdf-raw-page:22 lines:1-53Code · publicd across the
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spectacular variety of leaf shapes remains to be seen.
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Data and code to reproduce this work can be found for the following analyses: Reciprocal
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prediction of vein and blade from the other, https://zenodo.org/records/16920155; Two tower CNN
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1:1 vein:blade relationship, https://zenodo.org/records/17014105; Interspecies and Intraspecies
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developmental series swaps, https://zenodo.org/records/17013783
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Conflict of Interest Statement
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CC-BY-NC-ND 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 perpetuitOpen asset ↗zenodo · 17014105pdf-raw-page:22 lines:1-53Code · publicment
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Data and code to reproduce this work can be found for the following analyses: Reciprocal
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prediction of vein and blade from the other, https://zenodo.org/records/16920155; Two tower CNN
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1:1 vein:blade relationship, https://zenodo.org/records/17014105; Interspecies and Intraspecies
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developmental series swaps, https://zenodo.org/records/17013783
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Conflict of Interest Statement
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CC-BY-NC-ND 4.0 International license
available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprint
this version posted January 29, 2026.
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https:Open asset ↗zenodo · 17013783pdf-raw-page:22 lines:1-53Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
BACKGROUND: Phenotypic diversity arises from the process of development and is shaped by genomic variation in plants. However, the genetic basis of growth dynamics remains poorly understood in maize. RESULTS: Here, we analyze 679 maize inbred lines derived from a synthetic CUBIC population with approximately 2.8 million SNPs, leveraging high-throughput phenotyping to capture 1,002,240 RGB images across 18 growth stages. We quantify 67 image-based traits (i-traits), revealing distinct dynamic patterns throughout development. Genome-wide association studies identify 857 quantitative trait loci (QTLs) influencing growth variation, with 88.6% classified as period-specific dynamic QTLs exhibiting modest effects, and 11.4% as conservative QTLs with sustained effects. Notably, 1.5% of cryptic pleiotropic QTLs spanning different growth stages suggest genetic relocations during development. These QTLs enhance heritability estimates for mature traits by an average of 6.2%. We further characterize the novel function of key genes linked with these QTLs, including BRD1 with the pleiotropic effects on plant height and perimeter of convex hull and ZmGalOx1 with the broad-spectrum regulation of plant architecture. Developmental rewiring of epistatic networks shapes maize growth, underscoring the vitality of temporal genetic regulation. Trajectory modeling of i-traits across periods decodes the growth variation patterns, supporting the ontogenic hypothesis driven predictive breeding strategies. CONCLUSION: The findings elucidate the genetic architecture underlying growth dynamics from a spatial-temporal perspective, offering novel insights for maize improvement.
Why it matches plant phenotyping methods大規模RGB画像から67の画像形質を抽出し、発育段階ごとのトレイト動態を解析する高スループット植物表現型解析が研究の中核であるため、方法応用として収録する。
abstractleveraging high-throughput phenotyping to capture 1,002,240 RGB images across 18 growth stages
Reproduction assets foundThe paper's own phenotyping assets are publicly available: selected RGB plant images on Zenodo (record 18150504), and the image-analysis/i-trait extraction pipeline code on GitHub with a Zenodo mirror (record 18151471). The NCBI BioProject and MaizeGDB are prior-study/generic resources, not paper-specific.Code · publicThe image analysis and i-trait extraction pipeline and codes followed the previous procedure [ 18 ] without any modifications and has been publicly released at Github [ 49 ] and Zenodo [ 50 ] platform, all code in the repository are released under the MIT License.Open asset ↗GitHublines:195-202Code · publichas been publicly released at Github [ 49 ] and Zenodo [ 50 ] platform, all code in the repository are released under the MIT License.Open asset ↗Zenodolines:195-202Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Introduction Plant disease segmentation in real-world agricultural environments poses significant technical challenges, including complex backgrounds, diverse lesion morphologies, and extreme class imbalance. Methods In this paper, we propose an integrated solution, STAR-Net, which combines a novel network architecture with a dynamic training strategy. The architecture features an innovative Heterogeneous Branch Attention Aggregation (HBAA) module to robustly represent multi-scale and multi-morphology features. The training strategy employs a Dynamic Phase-Weighted Loss (DPW-Loss) to navigate the complexities of imbalanced data. Results Our method achieves a state-of-the-art average mIoU of 93.36% on the NLB dataset. This result demonstrates its superior ability to precisely segment diseases with specific elongated morphologies. Furthermore, the model obtains a competitive average mIoU of 41.13% on the highly challenging PlantSeg dataset. This result validates its robustness in complex 'in-the-wild' scenarios. Discussion Our work presents a powerful, well validated, and synergistic solution for plant disease segmentation. It also paves the way for practical applications in precision agriculture.
Why it matches plant phenotyping methods植物病徴を画像からセグメンテーションする新規ネットワークと学習法を開発し、複数データセットで検証しているため、病害状態の表現型抽出が中心である。
abstractwe propose an integrated solution, STAR-Net, which combines a novel network architecture with a dynamic training strategy.
Rubber tree phenotyping is transitioning from labor-intensive manual techniques toward high-throughput intelligent sensing platforms. However, the advancement of high-throughput phenotyping remains hindered by complex canopy architectures and pronounced seasonal morphological variations. To address these challenges, this paper introduces a unified phenotyping framework that leverages a novel Wood Salient Keypoint (WSK)-based registration algorithm to achieve seamless data fusion from unmanned aerial vehicle laser scanning (ULS) and handheld laser scanning (HLS) systems. The proposed approach begins by extracting stable wooden structures through a region-of-interest (ROI) segmentation process. Repeatable WSKs are then generated using a newly proposed wood structure significance (WSS) score, which quantifies and identifies salient regions across multi-view data. For transformation estimation, descriptor matching, WSS constraints, and geometric consistency optimization are integrated into a fast global registration (FGR) pipeline. Extensive evaluation across 25 plots covering 5 sites at the National rubber plantation base in Danzhou, Hainan, China, demonstrates that the method achieves a mean co-registration accuracy of 9 cm. Further analysis under varying seasonal canopy complexities confirms its robustness and critical role in enabling high-precision rubber tree phenotyping.
Why it matches plant phenotyping methodsゴム樹の表現型取得を目的に、UAVおよびハンドヘルドLiDARのデータ融合・位置合わせ手法を開発し、複数圃場で精度と季節変動下の頑健性を評価しているため、方法が中心的である。
abstractthis paper introduces a unified phenotyping framework that leverages a novel Wood Salient Keypoint (WSK)-based registration algorithm to achieve seamless data fusion from unmanned aerial vehicle laser scanning (ULS) and handheld laser scanning (HLS) systems.
Reproduction assets foundThe authors publicly released the ULS-HLS rubber plantation point cloud dataset (25 plots, 5 sites, leaf-on/leaf-off, >400M points) used for this paper's phenotyping/registration analysis, via an explicit Data Availability Statement with a Hugging Face URL matching an allowed URL.Dataset · publicThe dataset is available at https://huggingface.co/datasets/TanJunxiang/ULS-HLS-Rubber/tree/main (accessed on 20 January 2026).Open asset ↗TanJunxiang/ULS-HLS-Rubberlines:670-670Plant phenotyping relevance match · UnverifiedOpenAlex · checked 13 Sept 2026
Vegetation volume is a useful indicator for assessing canopy structure and supporting vineyard management tasks such as foliar applications and canopy management. The photogrammetric processing of imagery acquired using unmanned aerial vehicles (UAVs) enables the generation of dense point clouds suitable for estimating canopy volume, although point cloud quality depends on spatial resolution, which is influenced by flight height. This study evaluates the effect of three flight heights (30 m, 60 m, and 100 m) on grapevine canopy volume estimation using convex hull, alpha shape, and voxel-based models. UAV-based RGB imagery and field measurements were collected during three periods at different phenological stages in an experimental vineyard. The strongest agreement with field-measured volume occurred at 30 m, where point density was highest. Envelope-based methods showed reduced performance at higher flight heights, while voxel-based grids remained more stable when voxel size was adapted to point density. Estimator behavior also varied with canopy architecture and development. The results indicate appropriate parameter choices for different flight heights and confirm that UAV-based RGB imagery can provide reliable grapevine canopy volume estimates.
Why it matches plant phenotyping methodsUAV画像からブドウ樹のキャノピー体積を推定する手法を、飛行高度・推定モデル間で評価し、実測値と比較しているため、植物形質取得法の技術的検証が中心です。
abstractThis study evaluates the effect of three flight heights (30 m, 60 m, and 100 m) on grapevine canopy volume estimation using convex hull, alpha shape, and voxel-based models.
Xanthium sibiricum Patrin ex Widder and Xanthium italicum Moretti are morphologically similar fructus that are frequently misidentified. Xanthium italicum Moretti may possess inherent toxicity, and its adulteration of genuine medicinal materials poses a threat to clinical drug safety. Macroscopic observation and three microscopic techniques including stereo microscope, optical microscope, and 3D X-ray microscope were used for morphological identification of Xanthium sibiricum Patr ex Widder and Xanthium italicum Moretti in this study. 3D X-ray microscopy was applied as a novel tool for non-destructive, high-resolution discrimination of the two taxa. Intact fructus (n = 30 per species) were first screened macroscopically, then examined by stereo microscopy, optical microscopy, and 3D X-ray microscopy (0.3, 0.7, 1.5, 3.5, 18.06, 20.01 μm voxel size, Zeiss Xradia 520 Versa). The results showed that stereo microscopy, optical microscopy, and 3D X-ray microscopy collectively confirm the same conclusion from three distinct physical perspectives: surface topography, internal two-dimensional structure, and internal three-dimensional density distribution. The two Xanthium species differ significantly in burr spine morphology, fructus size and shape, the architecture and distribution of non-glandular and glandular trichomes, cotyledon conformation, and seed-coat cell patterning. In particular, 3D X-ray microscopy clearly resolves internal cotyledon spatial configurations and involucral cavity architectures, which furnishes critical endomorphic characters for taxonomic diagnosis. 3D X-ray microscopy provides unprecedented volumetric contrast of surface spines and internal seed architecture, permitting confident, non-destructive species identification. This study provides a basis for the safe clinical use of Xanthium sibiricum Patrin ex Widder. The frontier of 3D X-ray microscopy in plant systematics offers a novel, rapid, accurate and non-destructive protocol for the discrimination of morphologically elusive species.
Why it matches plant phenotyping methods3D X線顕微鏡を含む複数の画像計測法を用いて果実・種子の形態形質を抽出し、近縁2分類群の非破壊識別プロトコルとして比較・検証しており、植物フェノタイピング手法が中心である。
abstract3D X-ray microscopy was applied as a novel tool for non-destructive, high-resolution discrimination of the two taxa.
Sahelian ecosystems play a crucial role in climate regulation, particularly through their capacity to store atmospheric carbon. However, their carbon sequestration potential remains poorly quantified due to the high cost and logistical constraints of conventional field-based methods. This study investigates the use of low-cost unmanned aerial vehicles (UAVs) to assess dendrometric parameters and estimate the carbon storage capacity of woody vegetation within a 10-hectare Sahelian plot in Senegal. High-resolution aerial imagery was acquired using a DJI Phantom 4 Pro+ drone and processed with photogrammetric software. Tree height and crown diameter were extracted and validated using field measurements, showing a strong correlation for tree height (R² = 0.94) and a moderate correlation for crown diameter (R² = 0.74). Spectral index analysis enabled the identification of dominant woody species, including Balanites aegyptiaca, Sclerocarya birrea, Acacia raddiana, and Acacia senegal.The results indicate that Balanites aegyptiaca contributes the highest amount of carbon storage per hectare (1520.76 ± 185.5 kg ha⁻¹), mainly due to its high stem density (23.3 individuals ha⁻¹), whereas Sclerocarya birrea exhibits the highest average carbon stock per individual tree (206.8 ± 107.8 kg). These findings highlight the significant role of these species in climate change mitigation strategies. By demonstrating the effectiveness of low-cost UAVs for rapid and non-destructive assessment of Sahelian ecosystems, this study provides a promising approach for large-scale ecological monitoring and sustainable natural resource management. It also identifies methodological improvements required to enhance crown dimension estimates. Overall, this UAVbased approach represents a reliable alternative to destructive methods and is well suited to the ecological and operational challenges of Sahelian environments, with strong potential for application in similar regions.
Why it matches plant phenotyping methodsUAV画像から樹高・樹冠径という植物形態形質を抽出し、地上測定で検証する手法が研究の中心であるため、植物フェノタイピング手法として含める。
abstractTree height and crown diameter were extracted and validated using field measurements, showing a strong correlation for tree height (R² = 0.94) and a moderate correlation for crown diameter (R² = 0.74).
Non-structural carbohydrates (NSC) stored in the stem play a crucial role in supporting yield formation in rice. However, internode morphological determinants of NSC accumulation are unclear. This study aimed to clarify the relationship between internode morphology and NSC accumulation and to identify a robust morphological indicator for evaluating NSC accumulation capacity. Two years of field experiments were conducted using multiple cultivars. The NSC content was quantified for individual internodes and at the whole-plant culm level, and its relationships with internode morphological traits were analyzed. Since the upper internodes (UIN; first and second internodes) and lower internodes (LIN; third and subsequent internodes) exhibited contrasting roles in NSC accumulation, a novel index was introduced, the volume composition ratio (VCR) of UIN/LIN, which represents their relative volumetric contributions within a culm. The VCR of UIN/LIN showed the strongest correlation with culm NSC and high reproducibility across years, outperforming simple morphological traits. Manipulation of internode development using plant growth regulators demonstrated that altering VCR effectively modified culm NSC accumulation. Accordingly, the VCR of UIN/LIN serves as a robust morphological indicator of culm NSC accumulation capacity, providing a practical framework for improving NSC accumulation to achieve high and stable yield performance in rice. Highlight This novel internode structural index robustly predicts the culm non-structural carbohydrate accumulation capacity, providing a practical morphological indicator for improving yield stability in rice.
Why it matches plant phenotyping methods稲の節間形態から茎のNSC蓄積能力を評価する新規指標VCRを導入し、複数年・品種で再現性と予測性能を検証しているため、形態表現型の測定・評価法が研究の中心である。
abstracta novel index was introduced, the volume composition ratio (VCR) of UIN/LIN, which represents their relative volumetric contributions within a culm.
Individual tree structure plays a key role in forest monitoring, biomass estimation, and ecological assessment. However, ground-based remote sensing methods such as terrestrial and mobile laser scanning frequently produce incomplete point clouds due to occlusion, particularly in the upper canopy. This limits the accuracy of derived structural metrics such as tree height or crown volume. In this study, we present a novel deep learning-based method to reconstruct the outer crown shape of coniferous trees from incomplete point clouds. Instead of completing the full tree structure, we focus on predicting the alpha-shape of the crown, enabling a more efficient and generalizable approach for structural reconstruction. We train a geometry-aware transformer model (AdaPoinTr) on synthetically generated partial tree crowns and evaluate its performance across three independent datasets encompassing different forest types and acquisition conditions. The model consistently improved crown shape similarity metrics and reduced height estimation errors compared to using partial data alone (reduced bias from -11% to -3.5%). Our results demonstrate that this shape-based strategy enables the extraction of key tree-level parameters from incomplete data, offering a practical solution for gaining improved 3D forest structural information from cost-sensitive or logistically constrained forest monitoring acquisitions.
Why it matches plant phenotyping methods不完全な点群から樹冠形状を再構成し、樹高などの樹木形質を推定する深層学習手法の開発と複数データセットでの評価が中心であるため、植物フェノタイピング手法に該当する。
abstractwe present a novel deep learning-based method to reconstruct the outer crown shape of coniferous trees from incomplete point clouds.
Strawberries are among the most economically significant fruits in the United States, generating over $2 billion in annual farm-gate sales and accounting for approximately 13% of the total fruit production value. Plant phenotyping plays a vital role in selecting superior cultivars by characterizing plant traits such as morphology, canopy structure, and growth dynamics. However, traditional plant phenotyping methods are time-consuming, labor-intensive, and often destructive. Recently, neural rendering techniques, notably Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), have emerged as powerful frameworks for high-fidelity 3D reconstruction. By capturing a sequence of multi-view images or videos around a target plant, these methods enable non-destructive reconstruction of complex plant architectures. Despite their promise, most current applications of 3DGS in agricultural domains reconstruct the entire scene, including background elements, which introduces noise, increases computational costs, and complicates downstream trait analysis. To address this limitation, we propose a novel object-centric 3D reconstruction framework incorporating a preprocessing pipeline that leverages the Segment Anything Model v2 (SAM-2) and alpha channel background masking to achieve clean strawberry plant reconstructions. This approach produces more accurate geometric representations while substantially reducing computational time. With a background-free reconstruction, our algorithm can automatically estimate important plant traits, such as plant height and canopy width, using DBSCAN clustering and Principal Component Analysis (PCA). Experimental results show that our method outperforms conventional pipelines in both accuracy and efficiency, offering a scalable and non-destructive solution for strawberry plant phenotyping.
Why it matches plant phenotyping methods植物の3D再構成、背景除去、クラスタリングを統合し、草丈やキャノピー幅を自動推定するフェノタイピング手法の開発が中心である。
abstractwe propose a novel object-centric 3D reconstruction framework incorporating a preprocessing pipeline that leverages the Segment Anything Model v2 (SAM-2) and alpha channel background masking to achieve clean strawberry plant reconstructions.
Three-dimensional electron microscopy (3D EM) enables the quantitative analysis of cellular ultrastructure. However, large-scale segmentation of whole-cell volumes poses a significant challenge, especially in biologically diverse systems. Unlike medical and animal cell imaging, which often benefit from temporal redundancy and relatively uniform morphology, studies of microbial and microalgal biodiversity must rely on static snapshots. These snapshots exhibit high variability in cell shape, organelle organisation, and image contrast. Consequently, robust AI-assisted segmentation in this context requires models that learn directly from morphological features and can adapt to heterogeneous sample preparation. In this paper, we present a systematic framework for AI-assisted segmentation of Focused Ion Beam-Scanning Electron Microscopy (FIB-SEM) datasets. This framework is specifically designed to address the challenges posed by morphological diversity and contrast variability while remaining within realistic computational constraints. We evaluate multiple lightweight 3D encoder-decoder architectures and identify VNet as the best option for balancing computational efficiency and volumetric accuracy in whole-cell segmentation. Using datasets from two strains of Phaeodactylum tricornutum and extending our analysis to cross-species comparisons, we demonstrate that training on specific regions of interest can lead to an overestimation of model performance. In contrast, performing whole-cell segmentation uncovers significant differences in architectural robustness. Moreover, we show that transfer learning and contrast-aware hybrid strategies allow for efficient adaptation to previously unseen datasets with minimal annotation. The incorporation of boundary-aware loss functions significantly enhances the delineation of closely associated organelles, such as chloroplasts and mitochondria, in multi-class segmentation tasks. Together, these findings establish a scalable, reproducible, and biologically informed AI framework for 3D FIB-SEM segmentation. This framework enables high-throughput analysis of cellular ultrastructure across diverse species and imaging conditions. Author SummaryCells exhibit a wide range of shapes, sizes, and internal structures, particularly among various microbial species. These morphological differences are not arbitrary; they indicate how cells adapt to their environments and manage essential biological functions. Modern three-dimensional electron microscopy can capture this structural diversity at the nanometre scale, but analysing the resulting data is often slow. This is due to the time-consuming process of manually outlining cellular structures, which also requires expert knowledge. Artificial intelligence (AI) has made significant advances in accelerating image analysis in medical and animal cell studies, typically by learning from repeated observations over time. However, studies focusing on microbial and microalgal biodiversity often rely on single snapshots of diverse cells prepared under varying imaging conditions. This complicates automated analysis since AI systems must learn from morphology directly rather than from temporal repetition. In this study, we developed and evaluated an AI-assisted segmentation framework specifically for whole-cell 3D electron microscopy data. By systematically comparing efficient neural network architectures and incorporating transfer learning and contrast-aware strategies, we demonstrate that accurate segmentation can be achieved even with limited training data and standard computing resources. Our approach facilitates faster, scalable, and reproducible analysis of cellular ultrastructure, paving the way for large-scale investigations into cell morphology, adaptation, and diversity across species. Significance statementQuantitative analysis of cellular ultrastructure across species is currently limited by challenges in segmenting large three-dimensional electron microscopy datasets. Unlike medical imaging, which often benefits from artificial intelligence due to its use of temporal repetition and consistent morphology, studies of microbial biodiversity depend on single snapshots that display extreme variations in cell shape and image contrast. This work presents a scalable, morphology-driven AI framework for whole-cell 3D segmentation that is resilient to biological diversity and variations in sample preparation. By enabling accurate analysis with minimal annotations and standard computational resources, this approach enhances access to high-throughput ultrastructural studies and facilitates comparative investigations of cellular adaptation across different species.
Why it matches plant phenotyping methods珪藻の細胞形態・細胞内構造を定量化する3D電子顕微鏡画像のAIセグメンテーション手法を開発・比較検証しており、表現型取得・抽出が研究の中心である。
abstractIn this paper, we present a systematic framework for AI-assisted segmentation of Focused Ion Beam-Scanning Electron Microscopy (FIB-SEM) datasets.
Modern maize stems possess a well-developed vascular bundle system, which is critical for providing mechanical support and lodging resistance. However, characterization of the microanatomical features of vascular bundles and their functional implications in stem mechanics remains challenging, primarily due to technical limitations in high-throughput microanatomical analysis of stem tissues. We thus constructed data sets consisting of over 500,000 maize stem CT images from a maize diversity panel of 383 inbred lines. We evaluated 32 microanatomical phenotypes of maize basal internodes across two environments in different years. By incorporating engineering mechanics parameters, we calculated novel characteristics of the vascular bundles, including the moment of area (MOA) and the polar moment of inertia (PMOI). Through the high-density phenotypic data set, we identified multiple stem microanatomical phenotypes strongly associated with lodging resistance, particularly of vascular bundle mechanical traits. By integrating population genetic profiling, we discovered and confirmed that ZmLSM2 (U6 small nuclear ribonucleoprotein specific Sm-like 2) serves as a key regulator of stem mechanical strength, might function in RNA processing and maturation within vascular stem cells, identifying novel genetic targets for improving maize lodging resistance. This approach demonstrates the value of combining advanced phenotyping with multi-omics analyses for crop improvement. These discoveries will deepen the understanding of plant stem biomechanical principles and provide novel targets for enhancing lodging resistance in crop breeding programs.
Why it matches plant phenotyping methodsトウモロコシ茎のCT画像から微細構造形質を高スループットに抽出する表現型解析基盤とデータセットが研究の中心であり、単なる生物学的測定ではない。
abstracttechnical limitations in high-throughput microanatomical analysis of stem tissues
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicCT cross‐section images of the third internode from 383 maize inbred lines grown in Beijing and Sanya during two growing seasons can be downloaded via the link: https://pan.baidu.com/s/1CP2kkAmTvy1zi3QJGtKSWQ?pwd=JIPB . Extraction code: JIPB.Open asset ↗lines:204-306Plant phenotyping relevance match · UnverifiedOpenAlex · arXiv · checked 13 Sept 2026
Aerial remote sensing efficiently surveys large areas, but accurate direct object-level measurement remains difficult in complex natural scenes. Advancements in 3D computer vision, particularly radiance field representations such as NeRF and 3D Gaussian splatting, can improve reconstruction fidelity from posed imagery. Nevertheless, direct aerial measurement of important attributes like tree diameter at breast height (DBH) remains challenging. Trunks in aerial forest scans are distant and sparsely observed in image views; at typical operating altitudes, stems may span only a few pixels. With these constraints, conventional reconstruction methods have inaccurate breast-height trunk geometry. TreeDGS is an aerial image reconstruction method that uses 3D Gaussian splatting as a continuous scene representation for trunk measurement. After SfM--MVS initialization and Gaussian optimization, we extract a dense point set from the Gaussian field using RaDe-GS's depth-aware cumulative-opacity integration and associate each sample with a multi-view opacity reliability score. Then, we isolate trunk points and estimate DBH using opacity-weighted solid-circle fitting. Evaluated on 10 plots with field-measured DBH, TreeDGS reaches 4.79 cm RMSE (about 2.6 pixels at this GSD) and outperforms a LiDAR baseline (7.66 cm RMSE). This shows that TreeDGS can enable accurate, low-cost aerial DBH measurement .
Why it matches plant phenotyping methods樹木のDBHという明示的な植物形質を、航空画像と3D Gaussian splattingから推定する新規手法を開発し、実測値およびLiDARと比較検証しているため、方法中心の植物フェノタイピング研究として含める。
abstractTreeDGS is an aerial image reconstruction method that uses 3D Gaussian splatting as a continuous scene representation for trunk measurement.
Abstract The northern hardwood forests of Eastern Canada, particularly stands dominated by sugar maple (Acer saccharum Marsh.), are facing ongoing decline due to historical and contemporary environmental pressures. Traditional ground-based crown assessments for tree health are essential for management, but can be subjective, costly, and limited by their viewing perspective. While conventional remote sensing methods can effectively capture tree crown structure from above, and terrestrial approaches can capture the stems and crowns from beneath, occlusion by the dense crowns of mature sugar maples makes capturing reliable estimates challenging. We examine the potential of intra-canopy aerial drone-based photogrammetry, involving flights beneath, within, and above tree crowns, to generate detailed 3D point clouds of 29 sugar maple trees in Quebec, Canada. From these point clouds, we derived estimates of key structural attributes including diameter at breast height (DBH), tree height, and crown base height (CBH). We used ray-marching to quantify crown transparency across 162 viewing angles, forming a sphere around the crown, and compared predictions to ground-based visual estimates and health categories. Photogrammetric estimates had significant correlations with ground-measured attributes, including DBH (r = 0.82), tree height (r = 0.55), and CBH (r = 0.73 and 0.78 across two distinct definitions). Modeled crown transparency correlation was also significant when compared to ground-based visual assessments (ρ = 0.54), suggesting that intra-canopy drone-based photogrammetry can offer rapid and objective assessment of crown condition.
Why it matches plant phenotyping methods樹冠内ドローン画像から3D点群を生成し、樹高・DBH・樹冠基部高・透明度などの樹木形質を推定して地上測定と検証しており、フェノタイピング手法が中心である。
abstractWe examine the potential of intra-canopy aerial drone-based photogrammetry, involving flights beneath, within, and above tree crowns, to generate detailed 3D point clouds of 29 sugar maple trees in Quebec, Canada.
Achieving an optimal plant architecture is a key objective in cotton breeding for enhancing yield potential, and accurate quantification of the fruit branch angle (FBA) is essential for understanding genotype–phenotype relationships and advancing ideotype breeding. However, in-field FBA measurement remains technically challenging due to severe occlusion, variable illumination, and background interference. To overcome these limitations, we propose a streamlined 3D phenotyping framework that integrates 3D Gaussian Splatting (3DGS) with a novel structural segmentation model, the Linear Point Cloud Reverse Model (LPCRM). The framework decomposes reconstructed cotton point clouds into linear micro-elements using RANSAC, followed by geometric clustering via K-means to identify and separate the main stem and fruit branches. This process operates without topological priors or large annotated datasets. Model fidelity assessment shows that 80% of point pairs between the LPCRM and the original 3DGS reconstruction exhibit Euclidean distances ≤ 0.5 cm. Phenotypic validation using 268 fruit branches from 25 cultivars demonstrates high measurement accuracy, achieving an R² of 0.874 and an RMSE of 4.01° for FBA extraction. Plant height estimation also shows strong agreement with manual measurements (R² = 0.915, RMSE = 3.858). Overall, this study presents a lightweight and robust solution for extracting 3D structural traits of field-grown cotton. The proposed framework reduces data dependency, adapts well to complex field conditions, and offers an efficient approach for high-throughput phenotyping and cotton ideotype breeding.
Why it matches plant phenotyping methods綿花の果枝角度などの3D植物形質を抽出する画像ベース表現型解析フレームワークを開発し、複数品種・枝で精度検証しており、方法開発と技術検証が研究の中心である。
abstractwe propose a streamlined 3D phenotyping framework that integrates 3D Gaussian Splatting (3DGS) with a novel structural segmentation model, the Linear Point Cloud Reverse Model (LPCRM).
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:217-246Plant phenotyping relevance match · UnverifiedCrossref · checked 13 Sept 2026
Field / plotLiDAR / point cloudWhole plant / canopy / plot / fieldMorphology / geometry measurementArchitecture / morphology / geometry
Accurately measuring the three-dimensional green quantity (3DGQ) of urban trees is crucial for quantifying carbon sequestration benefits (CSB) in high-density cities. In this study, 540 street trees across 18 species (30 per species) in Shanghai were analyzed to evaluate an Improved Simpson Model (ISM) for UAV-derived crown volume estimation against a traditional Approximate Geometry Model (AGM) and a LiDAR-based point cloud method (PCM). The ISM integrates UAV imagery, edge-based canopy profiling, and Simpson’s numerical integration to account for irregular crown shapes and internal leaf-stem gaps. Results show that ISM achieved consistently lower estimation errors than the benchmark methods. Overall, ISM’s 3DGQ estimates had a root mean square error (RMSE) of approximately 5.2 m3 and a mean absolute error (MAE) of about 4.1 m3, indicating a close match with PCM reference values. This represents a dramatic error reduction, on the order of 90%–95% improvement in RMSE, compared to the conventional AGM approach. Broadleaf species with dense, regular canopies (e.g., Cinnamomum camphora and Platanus × acerifolia) exhibited the highest accuracy, with ISM-predicted volumes deviating only ~1%–2% from field measurements. Even for species with more irregular or porous crowns, the ISM maintained robust performance, yielding smaller errors than AGM and nearly matching the LiDAR-based PCM “ground truth.” These findings demonstrate that the proposed ISM can provide highly accurate 3D crown volume and carbon sequestration estimates in complex urban environments, outperforming existing geometric models and offering a practical, efficient alternative to labor-intensive LiDAR surveys.
Why it matches plant phenotyping methodsUAV画像から樹冠の3次元緑量・体積という植物形質を推定する新手法を開発し、既存モデルおよびLiDARと比較検証しており、フェノタイピング手法が中心である。
abstractevaluate an Improved Simpson Model (ISM) for UAV-derived crown volume estimation against a traditional Approximate Geometry Model (AGM) and a LiDAR-based point cloud method (PCM)
Mountain treelines are among the most climate-sensitive ecosystems on Earth, yet their fine-scale structural and species level dynamics remain poorly resolved in the Himalayas. In particular, the absence of three-dimensional, crown level measurements have hindered the detection of structural thresholds and species turnover that often precede treeline shifts. To bridge this gap, we introduce UAV LiDAR—applied for the first time in the Hindu Kush Himalayas—to quantify canopy structure and tree species distributions across a steep treeline ecotone in the Manang Valley of central Nepal. High-density UAV-LiDAR data acquired over elevations of 3504–4119 m was used to quantify elevation-dependent changes in canopy stature and cover from a canopy height model derived from the 3D point cloud, while individual tree segmentation and species classification were performed directly on the 3D, height-normalized point cloud at the crown level. Individual trees were delineated using a watershed-based segmentation algorithm while tree species were classified using a random forest model trained on LiDAR-derived structural and intensity metrics, supported by field-validated reference data. Results reveal a sharply defined treeline characterized by an abrupt collapse in canopy height and cover within a narrow ~60–80 m vertical interval. Treeline “threshold” was quantified as a breakpoint elevation from a piecewise model of tree cover versus elevation, and the elevation span over which modeled cover and height distributions rapidly declined from forest values to near-zero. Segmented regression identified a distinct structural breakpoint near 3995 m elevation. Crown-level species predictions aggregated by elevation quantified an ordered turnover in dominance, with Pinus wallichiana most frequent at lower elevations, Abies spectabilis peaking mid-slope, and Betula utilis concentrated near the upper treeline. Species classification achieved high overall accuracy (>85%), although performance varied among taxa, with broadleaf Betula more difficult to discriminate than conifers. These findings underscore UAV LiDAR’s value for resolving sharp ecological thresholds, identifying elevation-driven simplification in forest structure, and bridging observation gaps in remote, rugged mountain ecosystems.
Why it matches plant phenotyping methodsUAV LiDARによる樹冠高・被覆、個体樹木セグメンテーション、種分類を中心的に実施し、植物の構造形質と状態を定量化・検証しているため。
abstractwe introduce UAV LiDAR—applied for the first time in the Hindu Kush Himalayas—to quantify canopy structure and tree species distributions
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:1-60Plant phenotyping relevance match · UnverifiedbioRxiv · checked 15 Sept 2026
Field / plotLiDAR / point cloudStem / branchMorphology / geometry measurementArchitecture / morphology / geometry
Tree inventories require rapid, accurate measurements of stem diameter at breast height (DBH) and precise tree locations to support monitoring, planning, and informed decision-making. We evaluated a smartphone-based LiDAR app (SBLA), Forest Scanner, against (i) a diameter tape for DBH and (ii) a Vertex ultrasonic device for spatial coordinates. Across DBH of 725 trees, the LiDAR closely matched diameter tape measurements: discrepancies >5 cm occurred in 10.5% and > 10 cm in 3.5% of trees. Errors were concentrated in trees with smaller DBH, where occasional overestimation by SBLA arose from point-cloud misfitting. For medium and large trees, agreement was consistently high. Tree coordinates from SBLA and the ultrasonic device were broadly comparable at fine scales. Field efficiency was substantially improved: a 1,000 m2 plot with 70-80 trees required [~]2 hours using an ultrasonic device and diameter tape versus [~]20 minutes (one person) with SBLA, an [~]85-90% reduction in person-hours. Current limitations of SBLA are primarily software-related (stability, data handling, low-light performance). Overall, SBLA offers an efficient, auditable, and operationally relevant tool for tree inventories, with utility for rapidly updating DBH and spatial data used in management, planning, and asset databases.
Why it matches plant phenotyping methodsスマートフォンLiDARによる樹木の胸高直径と位置測定法を既存機器と比較検証しており、植物形質取得法が研究の中心である。
abstractWe evaluated a smartphone-based LiDAR app (SBLA), Forest Scanner, against (i) a diameter tape for DBH and (ii) a Vertex ultrasonic device for spatial coordinates.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Generating accurate and visually realistic 3D models of plants from single-view images is crucial yet remains challenging due to plants' intricate geometry and frequent occlusions. This capability matters because it supplements current plant datasets and enables non-destructive, high-throughput phenotyping for crop breeding and precision agriculture. More broadly, 3D reconstruction is particularly important because plant morphology is inherently three-dimensional, while 2D representations miss occluded leaves, branching geometry, and volumetric traits. However, plants present unique challenges compared to common rigid objects, and most current generative methods have not been systematically tested in this domain, leaving a gap in understanding their reliability for realistic plant reconstruction. This study systematically evaluates six advanced generative techniques-Hunyuan3D 2.0, Trellis (Structured 3D Latents), One2345++, InstantMesh, Direct3D and Unique3D-using the existing PlantDreamer dataset. Specifically, this research reconstructs mesh models from images of Bean plants and quantitatively assesses each method's performance against ground-truth models using Chamfer Distance, Normal Consistency, F-Score, PSNR, LPIPS, and CLIP Score. The paper also presents qualitative results of Kale and Mint plants. The results indicate that Hunyuan3D 2.0 achieves superior performance overall, suggesting its effectiveness in capturing complex plant structures. This work provides valuable insights into strengths and limitations of contemporary 3D generative approaches, guiding future improvements in realistic plant digitisation.
Why it matches plant phenotyping methods植物画像からの3D再構成手法を体系的に比較・定量評価し、植物形態の非破壊・高スループット表現型解析への利用可能性を検証しており、方法が研究の中心です。
abstractThis study systematically evaluates six advanced generative techniques-Hunyuan3D 2.0, Trellis (Structured 3D Latents), One2345++, InstantMesh, Direct3D and Unique3D-using the existing PlantDreamer dataset.
Sustainable agriculture in arid regions faces critical challenges due to water scarcity, high temperatures, and inefficient traditional farming practices. This study presents an AI-enabled smart farming framework for optimizing date palm (Phoenix dactylifera) cultivation through the integration of Machine Learning (ML) and Internet of Things (IoT) technologies. A structured multimodal dataset comprising biometric features palm height, trunk diameter, and leaf number, environmental parameters soil moisture, temperature, and humidity, and categorical attributes variety and health status was analyzed to classify palm health and support data-driven irrigation management. Four ML algorithms Random Forest (RF), Gradient Boosting Machine (GBM), Artificial Neural Network (ANN), and Support Vector Machine (SVM) were developed and optimized using grid search with five-fold cross-validation. Among them, the Random Forest model achieved the highest classification accuracy of 95.3%, demonstrating strong robustness for heterogeneous agricultural data. Feature importance analysis highlighted soil moisture, humidity, trunk diameter, and leaf number as key contributors to palm health prediction. The proposed AI-IoT framework enables real-time monitoring, predictive diagnostics, and automated decision support for sustainable water use and crop management, aligning with Saudi Vision 2030 objectives for technology-driven and resource-efficient agriculture.
Why it matches plant phenotyping methodsヤシの生体特徴から健康状態を分類する機械学習手法を開発・比較し、分類性能を評価しているため、植物状態推定が中心的な方法的貢献である。
abstractThis study presents an AI-enabled smart farming framework for optimizing date palm (Phoenix dactylifera) cultivation through the integration of Machine Learning (ML) and Internet of Things (IoT) technologies.
Introduction In precision agriculture, accurate measurement of maize stem diameter during the jointing stage is crucial for lodging resistance assessment and yield prediction. However, existing methods have certain limitations: manual measurement is time-consuming and highly subjective, while two-dimensional image recognition can only capture local features and fails to reconstruct the true three-dimensional structure of the stem. Therefore, there is a critical need for an accurate and automated three-dimensional stem diameter measurement approach. Methods This study proposes a three-dimensional stem diameter measurement method that integrates an improved PointNet++ segmentation network with structural feature fitting, focusing on the position of the second above-ground internode of maize plants. Specifically, multi-view image reconstruction is employed to generate three-dimensional point clouds of maize stems, and Relative Position Encoding, the Local Group Rearrangement Module, and the Local Region Self-Attention mechanism are incorporated into the PointNet++ network to achieve precise segmentation of stems from the ground. On this basis, a structural feature fitting strategy is applied, where principal axis analysis and ellipse fitting are utilized to extract cross-sectional features, thereby obtaining the major axis and minor axis parameters for stem diameter estimation. Results Experimental results demonstrate that the proposed method maintains high accuracy under complex field conditions, achieving a mean absolute error (MAE) of 1.27 mm (R² = 0.87) for major-axis stem diameter and 1.38 mm (R² = 0.82) for minor-axis stem diameter. Discussion The proposed method effectively overcomes the limitations of traditional manual and two-dimensional measurement techniques. It provides a robust and accurate solution for maize stem diameter measurement during the jointing stage. This approach offers technical support for intelligent maize growth monitoring, lodging resistance analysis, and three-dimensional phenotypic trait extraction.
Why it matches plant phenotyping methodsトウモロコシ茎径という植物形態形質を、3D再構成、点群セグメンテーション、構造特徴フィッティングで自動推定する手法が研究の中心であり、精度検証も行っている。
abstractThis study proposes a three-dimensional stem diameter measurement method that integrates an improved PointNet++ segmentation network with structural feature fitting
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:306-323Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Photosynthesis supplies energy not only for plant biomass production but also for symbiotic processes such as nitrogen (N) fixation. Whereas the potential for further genetic gains in productivity of major crops from improved light interception and harvest index has largely been exhausted, naturally occurring or induced genetic variation in photosynthetic traits still offers considerable potential for further yield improvement. However, since photosynthesis is highly dynamic under fluctuating field conditions, it is difficult to conduct a targeted selection for photosynthetic performance unless high spatial and temporal resolution data are available. To bridge this gap, we installed a light-induced fluorescence transient (LIFT) device on an autonomous field robot to measure the quantum efficiency of photosystem II (Fq'/Fm'), which has been shown to be well correlated with overall photosynthetic performance. The LIFT method uses sub-saturating flashes at a fast repetition rate to induce maximum fluorescence, enabling measurements in less than 1 ms from a distance of up to 1 m. The robot moves at a speed of 0.5 m s -1 , autonomously navigating the entire field based on global navigation satellite system (GNSS) coordinates. Spectral measurements and stereo red, green, and blue (RGB) cameras provide additional information about three-dimensional (3D) plant architecture-related traits, such as leaf angle and light intensity on the target leaf. The resulting high spatiotemporal resolution maps of photosynthetic efficiency provide detailed information about the growth performance of plants in agronomic field trials or plant breeding nurseries.
Why it matches plant phenotyping methods自律走行ロボットに搭載したLIFTセンサーで光合成効率を高スループット測定し、作物の生理形質と構造形質を圃場でマッピングする方法・プラットフォームが研究の中心である。
abstractwe installed a light-induced fluorescence transient (LIFT) device on an autonomous field robot to measure the quantum efficiency of photosystem II (Fq'/Fm')
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:
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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:151-171Plant phenotyping relevance match · UnverifiedOpenAlex · checked 13 Sept 2026
This study develops a voxel-based leaf area estimation framework and validates it using a three-year multi-temporal dataset (2022–2024) of pergola-trained grapevines. The workflow integrates 2D image analysis, ExGR-based leaf segmentation, and 3D reconstruction using Structure-from-Motion (SfM). Multi-angle canopy images were collected repeatedly during the growing seasons, and destructive leaf sampling was conducted to quantify true leaf area across multiple vines and years. After removing non-leaf structures with ExGR filtering, the point clouds were voxelized at a 1 cm3 resolution to derive structural occupancy metrics. Voxel-based leaf area showed strong within-vine correlations with destructively measured values (R2 = 0.77–0.95), while cross-vine variability was influenced by canopy complexity, illumination, and point-cloud density. In contrast, optical LAI tools (DHP and LAI–2000) exhibited negligible correspondence with true leaf area due to multilayer occlusion and lateral light contamination typical of pergola systems. This expanded, multi-year analysis demonstrates that voxel occupancy provides a robust and scalable indicator of canopy structural density and leaf area, offering a practical foundation for remote-sensing-based phenotyping, yield estimation, and data-driven management in perennial fruit crops.
Why it matches plant phenotyping methodsブドウ樹の葉面積・樹冠構造を推定する画像解析、SfM、ボクセル化ワークフローを開発し、破壊測定および既存LAI手法と比較検証しており、植物表現型取得法が研究の中心である。
abstractThis study develops a voxel-based leaf area estimation framework and validates it using a three-year multi-temporal dataset (2022–2024) of pergola-trained grapevines.
The distribution of spikelets significantly affects wheat (Triticum aestivum L.) spike architecture. However, traditional methods lack the precision to study spikelet distribution effectively. We developed RachisSeg, a deep learning-based phenotyping pipeline that automatically measures traits from scanned rachis images. In addition to traditional spikelet number per spike (SNS), rachis length (RL), and spikelet density (SD, SNS/RL), we introduced spikelet distribution traits based on rachis internode lengths, providing quantitative insights into spike architecture. RachisSeg showed high consistency with manual measurements for SNS and RL, with the R2 values of 0.975 and 0.998, respectively. Using RachisSeg, we analyzed spikelet distribution patterns across wheat germplasm and found that traits such as spikelet distribution index (SDI) and apical-to-basal spikelet number ratio (AVB_SNS) were moderately correlated with grain yield per spike (GYPS) (r = 0.57 and 0.53, respectively), while internode width (IW) showed a strong positive correlation with GYPS (r = 0.75). Specifically, a denser spikelet arrangement in the upper spike negatively impacted grain number and weight in that section. Furthermore, comparative analysis revealed distinct spikelet distribution patterns among landraces, American cultivars, and Chinese cultivars. In a recombinant inbred line population, we identified 46 quantitative trait loci (QTLs) associated with rachis traits. A major QTL controlling SDI was detected on chromosome 6B, explaining up to 24.8% of the phenotypic variance. Candidate gene analysis suggested TraesCS6B02G417000 as a potential gene, whose mutant exhibited significant changes in RL and SDI. RachisSeg is a powerful tool for quantifying spikelet distribution, facilitating wheat genetic analysis, gene discovery, and breeding.
Why it matches plant phenotyping methodsRachisSegは、スキャン画像からコムギ穂軸・小穂分布形質を自動抽出する深層学習フェノタイピング手法として開発・検証されており、方法が研究の中心です。
abstractWe developed RachisSeg, a deep learning-based phenotyping pipeline that automatically measures traits from scanned rachis images.
Reproduction assets foundThe paper's authors publicly released the RachisSeg phenotyping pipeline (deep learning node detection and internode segmentation code) together with sample rachis images via their GitHub repository, explicitly stated in the Implementation and Data availability sections.Dataset · publicRachisSeg and sample rachis images is freely available online ( https://github.com/Jiang-Phenomics-Lab/RachisSeg ).Open asset ↗Jiang-Phenomics-Lab/RachisSeglines:514-549Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
ABSTRACT Three‐dimensional measurement technology based on point clouds can effectively solve the problem of plant occlusion and is a hot research direction for plant phenotyping methods. Rapid and low‐cost 3D reconstruction and accurate 3D point cloud segmentation are two major challenges in 3D phenotyping technology. Taking watermelon seedlings as an example, we proposed a new phenotyping method that uses the Instant‐NGP for 3D reconstruction and the improved PointNet++ for 3D point cloud segmentation. We performed dynamic downsampling and filtering based on the point cloud scale and designed different phenotypic measurement methods for hypocotyl and leaf point clouds. To overcome the difficulty of measuring the hypocotyl caused by slenderness, curvature and inclination, we proposed a segmented stem 3D point cloud skeleton extraction algorithm. The experimental results show that our method achieved satisfactory measurement results for the seedling phenotypes of four growth stages. The detection accuracy of the number of cotyledon leaves and the number of true leaves both exceed 95% and the coefficient of determination ( R 2 ) of leaf area, hypocotyl length and stem diameter phenotypes are all beyond 0.8. The proposed method provides a novel, efficient and precise 3D plant phenotyping solution, with good application and promotion value.
Why it matches plant phenotyping methods3D再構成、点群セグメンテーション、骨格抽出、形質測定を統合した植物フェノタイピング手法の開発が中心であり、精度評価も実施している。
abstractwe proposed a new phenotyping method that uses the Instant‐NGP for 3D reconstruction and the improved PointNet++ for 3D point cloud segmentation.
Background Root hairs play a key role in plant nutrient and water uptake. Historically, root hair traits have largely been quantified manually. As such, this process has been laborious and low-throughput. However, given their importance for plant health and development, high-throughput quantification of root hair morphology could help underpin rapid advances in the genetic understanding of these traits. With recent increases in the accessibility and availability of artificial intelligence (AI) and machine learning techniques, the development of tools to automate plant phenotyping processes has been greatly accelerated. Results We present pyRootHair, a high-throughput, AI-powered software application to automate root hair trait extraction from microscope images of plant roots grown on agar plates. pyRootHair is capable of batch processing over 600 images per hour without manual input from the end user. In this study, we deploy pyRootHair on a panel of 24 diverse wheat (Triticum aestivum and Triticum turgidum ssp. durum) cultivars and uncover a large, previously unresolved amount of variation in many root hair traits. We show that the overall root hair profile falls under 2 distinct shape categories and that different root hair traits often correlate with each other. We also demonstrate that pyRootHair can be deployed on a range of plant species, including oat (Avena sativa), rice (Oryza sativa), teff (Eragrostis tef), and tomato (Solanum lycopersicum). Conclusions The application of pyRootHair enables users to rapidly screen a large number of plant germplasm resources for variation in root hair morphology, supporting high-resolution measurements and high-throughput data analysis. This facilitates downstream investigation of the impacts of root hair genetic control and morphological variation on plant performance. pyRootHair is installable via PyPI (https://pypi.org/project/pyRootHair/) and can be accessed on GitHub at https://github.com/iantsang779/pyRootHair.
Why it matches plant phenotyping methods植物根毛形態を顕微鏡画像から自動抽出するAIソフトウェアを開発し、複数作物で適用・実証しており、表現型取得手法が研究の中心である。
abstractWe present pyRootHair, a high-throughput, AI-powered software application to automate root hair trait extraction from microscope images of plant roots grown on agar plates.
Reproduction assets foundThe paper's root hair phenotyping software (pyRootHair) is publicly available on GitHub and PyPI, the data and notebooks used to generate the manuscript figures are deposited in the repository's paper_data folder, and the software is annotated in the DOME-ML registry. The GigaDB deposit (10.5524/102771) is referenced,但Code · publicregression lines were computed using statsmodels (v0.14.4). Scikit-learn (v.1.5.2) was used for quality control of segmented images. nnU-Netv2 (v2.5.1) was used to create the image segmentation model with PyTorch (v.2.5.1) and CUDA (v.12.6).
Availability of Source Code and Requirements
Project name: pyRootHair
Project homepage: https://github.com/iantsang779/pyRootHair
Operating system(s): Linux, MacOS, Windows
Programming language: Python
License: MIT License
Supplementary Material
giaf141_Supplemental_File giaf141_Authors_Response_To_Reviewer_Comments_Original_Submission giaf141_GIGA-D-25-00279_Original_Submission giaf141_GIGA-D-25-00279_Revision_1 giaf141_Reviewer_1_Report_Original_SubmisOpen asset ↗github.com/iantsang779/pyRootHairlines:250-287Dataset · publicThe source jupyter notebook and data used to generate all figures in the manuscript have been deposited on GitHub [ 39 ].Open asset ↗lines:400-405Code · publiclarge number of plant germplasm resources for variation in root hair morphology, supporting high-resolution measurements and high-throughput data analysis. This facilitates downstream investigation of the impacts of root hair genetic control and morphological variation on plant performance. pyRootHair is installable via PyPI ( https://pypi.org/project/pyRootHair/ ) and can be accessed on GitHub at https://github.com/iantsang779/pyRootHair .
Keywords: root hairs, plant phenotyping, machine learning, computer vision, AI, U-Net, wheat, roots, software
status released display-pdf yes is-olf no is-manuscript no is-preprint no is-journal-matter no is-scanned no is-retracted no
Received 2025 JOpen asset ↗lines:1-34Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Tree growth determines how much CO2 is sequestered from the atmosphere and temporarily stored in woody biomass. At the same time tree growth is affected by increasing temperatures, more frequent drought periods, late frosts and other extreme events associated with climate change. While continuous measurements of radial (secondary) tree growth using dendrometers are well established, monitoring of shoot elongation (primary growth) has largely been neglected because suitable measurement techniques are lacking. As a result, the effects of climate change on primary tree growth remain insufficiently understood. This work aims at reconstructing native deciduous trees in 3D as a basis for measuring and monitoring shoot elongation over entire tree canopies. Here we explored the use of low-cost UAV photogrammetry and of a multi-camera CraneCam system under real-world conditions. Data were collected in two study areas over an entire growing season. We present sensor evaluations, photogrammetric data acquisition and processing strategies. A special focus is placed on the analysis of the resulting photogrammetric 3D point clouds in terms of accuracy, resolution and completeness. Results demonstrate 3D point accuracies of 5-6 mm for entire trees using consumer-grade UAVs weighing less than 250 g and a 3D reconstruction completeness between 92% and 98% depending on the UAV type. The paper introduces a novel 3Dprinted ground-truth branch to evaluate the capability to reconstructing fine-detail structures such as thin tree shoots. Finally, we discuss operational challenges and initial experiments towards a skeletonization of entire trees based on photogrammetric point clouds.
Why it matches plant phenotyping methodsUAVおよびマルチカメラによる3D再構成を開発・評価し、樹冠全体のシュート伸長という植物形質の測定に用いる方法が中心である。
abstractThis work aims at reconstructing native deciduous trees in 3D as a basis for measuring and monitoring shoot elongation over entire tree canopies.
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:222-243Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
ABSTRACT High-throughput phenotyping using unmanned aerial vehicles (UAVs) and spectral vegetation indices has been proposed to overcome the cost and logistical constraints of manual measurements in multi-environment breeding trials. However, the reliability of models trained on spectral data to predict structural traits across genotypes and environments remains unclear. This study aimed to develop an approach for predicting soybean plant height (PH) and first pod insertion height (FPIH) using UAV-based vegetation indices acquired at the flowering stage, as well as to compare extreme gradient boosting (XGBoost), multilayer perceptron (MLP), random forest (RF), and multiple linear regression (MLR) models under realistic cross-validation scenarios. Trials were conducted across multiple seasons using UAV multispectral imagery, with PH and FPIH manually measured. The models were evaluated under five phenotyping scenarios: baseline calibration using all data; prediction in a completely unmeasured future season; estimation of missing genotypes within a partially sampled season; calibration using a small fraction of data from a new season; and prediction under absence of field records for specific genotypes across environments. When all data were used for calibration, non-linear models showed a high apparent accuracy. However, prediction in unseen seasons failed for all models, reflecting strong genotype × environment interactions. Under reduced phenotyping within the same environment network, the models maintained a robust accuracy for PH, whereas FPIH predictions declined to moderate levels. UAV-based models are reliable for interpolation, but limited for extrapolation without local calibration, enabling reductions of up to 80 % in manual measurements for PH and 20-30 % for FPIH.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と機械学習によるダイズ形質推定を開発・比較検証し、異なる環境での予測性能と手測定削減を評価しているため、フェノタイピング手法が中心である。
abstractThis study aimed to develop an approach for predicting soybean plant height (PH) and first pod insertion height (FPIH) using UAV-based vegetation indices acquired at the flowering stage, as well as to compare extreme gradient boosting (XGBoost), multilayer perceptron (MLP), random forest (RF), and multiple linear regression (MLR) models under realistic cross-validation scenarios.
Precision horticulture demands intelligent monitoring systems for automated crop management. While traditional biomass estimation relies on destructive sampling, modern ICT-driven approaches offer transformative solutions. This study presents an AI-powered methodology integrating YOLOv11 for autonomous biomass estimation and stress monitoring in tomato crops. High-resolution RGB imagery was captured across multiple phenological stages under two irrigation regimes. YOLOv11’s computer vision capabilities enabled automated canopy detection, segmentation, and digital biomass quantification, eliminating destructive sampling. Novel AI-driven metrics were introduced: convex hull area for stress-induced canopy alterations and compactness (digital biomass to convex hull ratio) for automated canopy assessment. This integrated approach achieved robust accuracy in stress detection and biomass estimation (R² =0.821), enabling real-time monitoring for precision horticulture. The model demonstrated exceptional performance with segmentation precision of 0.950, recall of 0.979, and mean average precision of 0.975 at IoU 0.5, with mAP50-95 of 0.826. The rapid inference time of 2.3ms per image enables high-throughput phenotyping and decision support for site-specific management. YOLOv11-derived digital biomass correlated strongly with fresh biomass (R² = 0.821). Image-derived features effectively differentiated control and stress conditions. Genotype analysis revealed variation in biomass accumulation: Arka Abhed and Arka Rakshak performed better under optimal irrigation, while Arka Vikas showed greater stress resilience. These results validate YOLOv11 as a scalable solution for intelligent crop monitoring and precision input application in next-generation digital horticulture.
Why it matches plant phenotyping methods植物の画像からバイオマスと水ストレスを推定するコンピュータビジョン手法を開発・検証しており、表現型取得が研究の中心である。
abstractThis study presents an AI-powered methodology integrating YOLOv11 for autonomous biomass estimation and stress monitoring in tomato crops.
The accurate three-dimensional (3D) distribution of plant area density (PAD) within forests is crucial for understanding canopy structure and provides essential scene inputs for 3D Radiative Transfer Models (RTMs) to facilitate remote sensing interpretation. However, current lidar-based voxelization methods that estimate detailed PAD distributions often cover limited areas, constraining their applications in conducting broad forest studies and interpreting Earth Observation Satellite (EOS) data of various scales and resolutions. To address this, we developed the Large-Scale Path Volume Leaf Area Density (LS-PVlad), a novel forest 3D reconstruction workflow capable of producing extensive high-resolution 3D voxelized forest scenes (up to 100 km² with ≤2 m voxel size) from worldwide open-access airborne lidar scanning (ALS) data. By applying LS-PVlad to the ALS data acquired during the extensive NASA Goddard's LiDAR, Hyperspectral & Thermal Imager (G-LiHT) campaigns, we developed the first release of FoScenes—a high-fidelity PAD product comprising 40 seamless scenes from 28 diverse forest sites, with individual area ranging from ∼50 to ∼11,000 ha. The leaf area estimates of LS-PVlad have been validated by two-year field-measured leaf area index (LAI) from litter collection (best RMSE = 0.35 m²/m²) and digital hemispherical photography (DHP) images (RMSE = 0.46 m²/m²) across multiple plots at a deciduous forest site. Additionally, a broad comparison between FoScenes and MODIS plant/leaf area index product demonstrates high consistency (R² = 0.70, RMSE = 0.86 m²/m²). By providing multi-dimensional forest characterizations, FoScenes enables temporal insights into structure dynamics. Its integration with the discrete anisotropic radiative transfer (DART) model underscores the potential of FoScenes for extensive 3D RTM applications at various scales.
Why it matches plant phenotyping methods森林の植物面積密度を推定する3D再構成ワークフローを開発し、実測LAI等で検証した大規模フェノタイピング製品・データセットであり、植物形質取得が中心である。
abstractwe developed the Large-Scale Path Volume Leaf Area Density (LS-PVlad), a novel forest 3D reconstruction workflow
This research aims to overcome key limitations of traditional pea breeding, namely the lengthy variety development cycle and the subjectivity of manual phenotyping, by developing automated image analysis methods. The study compares three computer vision methods applied to peas: YOLO-based detection, semantic segmentation for recognizing plant elements in dry and green samples (using a proprietary digital phenotyping setup), and an original algorithm for detecting stem nodes by analyzing stem width. The detection method demonstrated low accuracy for plant parts. Semantic segmentation achieved 65 % accuracy for dry and 76 % for green plants. The node detection algorithm demonstrated 100 % accuracy. The developed software package enables objective assessment of key pea phenotypic traits. Further development of the system is aimed at integration with neural networks for determining leaf surface area and the number of productive nodes, which creates the basis for accelerated pea breeding.
Why it matches plant phenotyping methodsエンドウの表現型を抽出するコンピュータビジョン手法とソフトウェアを開発・評価しており、方法開発が研究の中心である。
abstractThis research aims to overcome key limitations of traditional pea breeding, namely the lengthy variety development cycle and the subjectivity of manual phenotyping, by developing automated image analysis methods.
In recent years, the development of automated methods for phenotypic assessments of observable plant traits is gaining interest because they can provide advantages over standard ones. This study presents a novel methodology to estimate the canopy volume of grapevine plants during their growth cycle using a low-cost red, green, blue and depth (RGB-D) sensor mounted on a ground-based platform. The aim is to address the need for cost-effective and easy-to-use systems that can operate under variable environmental conditions without requiring highly specific data acquisition constraints and advanced technical skills. Using a Microsoft Azure Kinect RGB-D camera, detailed 3-D images of the plants are captured. The iterative closest point (ICP) algorithm is then applied to reconstruct a full view of the plants useful to estimate the canopy volumes. The effectiveness of this approach is validated by comparing the volumetric estimates with the leaf area index (LAI) measures obtained with traditional agronomic techniques during significant phenological periods of the plants’ lifecycle. The results demonstrate a correlation between the two approaches, indicating the reliability of the proposed method and highlighting advantages in terms of precision. These outcomes demonstrate the potential of automated vineyard monitoring systems, providing reliable data to assess plant growth conditions.
Why it matches plant phenotyping methodsRGB-D画像とICP再構成によりブドウ樹の樹冠体積を推定する手法を開発・検証しており、植物形質の取得が研究の中心です。
abstractThis study presents a novel methodology to estimate the canopy volume of grapevine plants during their growth cycle using a low-cost red, green, blue and depth (RGB-D) sensor mounted on a ground-based platform.
Sieve elements in the phloem transport carbon and small molecules, such as RNA and phytohormones, throughout the plant body. Understanding the physical dimensions of sieve elements and phloem tissue is thus crucial for predicting how much carbon can be moved at any given time. Quantification of sieve element diameters and areas has previously been performed using transmission electron microscopy, scanning electron microscopy, and light microscopy, but sieve element identification is difficult because the phloem is a heterogeneous tissue. The recently identified LM26 antibody labels a pectin in the sieve element cell wall, allowing the identification of sieve elements and the measurement of their properties, such as diameter, relatively quickly and the quantification of their number in cross sections using image analysis software. Here, we describe methods for immunolabelling sieve elements in fresh or fixed tissue embedded in polyethylene glycol or methacrylate. The protocol is broadly adaptable to various fixation and sectioning methods, provided they do not alter the structure of pectins in the cell wall.
Why it matches plant phenotyping methods師部篩要素の同定、画像解析による直径・面積・数の定量を可能にする免疫標識プロトコルが中心で、植物形態形質の取得手法を提供している。
abstractallowing the identification of sieve elements and the measurement of their properties, such as diameter, relatively quickly and the quantification of their number in cross sections using image analysis software.
Accurate prediction of complex agronomic traits such as grain yield and yield components remains a central challenge in winter wheat breeding because these traits are controlled by many genes and are strongly influenced by environmental variation. The integration of high-throughput phenotyping (HTP), genomics, and advanced machine learning approaches offers new opportunities to improve predictive accuracy and accelerate genetic gain in plant breeding. This study evaluates the use of an unmanned aerial vehicle (UAV)-based multispectral phenomics combined with genomic information, and deep learning approaches to enhance the prediction of grain yield (GY), test weight (TW), grain protein content (GPC), and tiller density (TD) in a winter wheat breeding program. The research was conducted during the 2022 to 2024 growing seasons at three locations in South Dakota: Brookings, Dakota Lakes, and Winner, across multiple breeding nurseries, including the Elite, Advanced, and Preliminary yield trials. UAV-derived spectral indices collected across multiple developmental stages were first integrated into deep neural network (DNN)-based phenomic prediction models and multi-trait genomic selection (MT-GS) frameworks. Significant associations were observed between UAV-based spectral indices and key agronomic traits. Phenomic prediction using DNN achieved strong accuracy for single-location trials (R² = 0.71, 0.62, and 0.49 for GY, TW, and GPC, respectively), with further improvement when models were trained on multi-location datasets (R² = 0.76, 0.64, and 0.75). Prediction accuracy for GY was highest at the Feekes 11 stage. Forward prediction of preliminary breeding lines using models trained on multi-location advanced lines improved accuracy by 32% relative to single-location training. Incorporating UAV-derived spectral indices as covariates in MT-GS models further improved predictive ability for GY (0.40) compared to single-trait genomic selection models (0.23), demonstrating the value of integrating phenomic information into genomic prediction frameworks. To address challenges associated with model transferability across environments, a deep transfer learning (DTL) strategy based on a one-dimensional convolutional neural network (1D-CNN) was implemented. Baseline cross-year and cross-location predictions showed poor performance (R² as low as -15.3); however, partial fine-tuning with 20-40% of target data substantially improved accuracy, achieving R² values up to 0.83 in cross-year and 0.29-0.69 in cross-location scenarios. Growth stage-specific modeling further revealed predictive performance highest at Feekes 10.5 and 11 (R² = 0.78-0.82), highlighting the importance of developmental timing in UAVbased trait prediction. Beyond predicting primary agronomic traits, UAV-based phenomics also provides opportunities to estimate important yield components that are difficult to measure at scale in breeding programs. Finally, UAV multispectral imagery was used to estimate early-season tiller density (TD). Among evaluated models, an attention-based convolutional neural network achieved the highest predictive performance (R² = 0.82; RMSE% = 15.20), outperforming conventional machine learning and standard deep learning approaches. Collectively, these findings demonstrate that integrating UAVbased HTP, genomic information, and deep learning approaches substantially improves the accuracy, generalizability, and scalability of complex trait prediction in winter wheat breeding. By enabling earlier and more reliable estimation of key agronomic traits across environments, these data-driven phenomic and genomic prediction frameworks accelerate breeding decisions and support the development of high-yielding and climate-resilient wheat cultivars.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像による作物形質推定と、深層学習・転移学習モデルの開発および検証が研究の中心であり、再利用可能なHTPワークフローとして実質的な方法論的貢献がある。
abstractThis study evaluates the use of an unmanned aerial vehicle (UAV)-based multispectral phenomics combined with genomic information, and deep learning approaches to enhance the prediction of grain yield (GY), test weight (TW), grain protein content (GPC), and tiller density (TD)
Banana plant canopies exhibit pronounced three-dimensional heterogeneity due to their large, sparse, and overlapping leaves. Under dense planting and severe occlusion, conventional single-modality approaches commonly suffer from insufficient information and limited recognition accuracy, highlighting the necessity of cross-modal complementarity and collaborative modeling. This study proposes a 3D-2D dual-modal collaborative framework based on low-cost UAV oblique imagery to enable end-to-end estimation of canopy volume and porosity in banana plantations. The framework integrates a task-oriented YOLO-SPES model to improve the detection of irregular and overlapping canopies and combines it with the SoftGroup instance segmentation model for three-dimensional structural extraction. At the data level, a cross-modal coordinate interaction strategy (PCI-LLCM) is introduced to achieve precise alignment between point clouds and orthomosaics. At the structural level, a consistent indexing scheme between 3D instances and 2D detection boxes is established, upon which a 3D-2D collaborative modeling algorithm (SGP-YS DMCA) and a multi-scale volume differencing algorithm (MSVDA) are developed for canopy volume and porosity estimation. At the decision level, an adaptive canopy volume completion module (CVC-AOML) leverages 2D detection information to correct and supplement errors and omissions in 3D segmentation, thereby ensuring the accuracy and completeness of large-scale automated measurements. In addition, the coupling performance of multiple geometric algorithms and collaborative models is systematically evaluated. Experimental results demonstrate that the proposed dual-modal collaborative framework achieves a coefficient of determination (R$^{2}$) of 0.885 for canopy volume estimation, representing an average improvement of 0.15 over single-modality baseline methods, with a corresponding mean absolute percentage error (MAPE) of 6.4%. For canopy porosity estimation, an R$^{2}$of 0.65 is obtained with a MAPE of 1.05%. These results not only overcome the limitations of single-modality approaches in phenotypic analysis of complex banana canopies but also provide a low-cost and scalable solution for large-scale agricultural monitoring and precision management of tropical fruit crops.
Why it matches plant phenotyping methodsUAV画像・点群を用いてバナナの樹冠体積と多孔性という明示的な植物形質を自動推定する3D–2D手法を開発し、ベースライン比較と精度評価を行っているため、フェノタイピング手法が中心である。
abstractThis study proposes a 3D-2D dual-modal collaborative framework based on low-cost UAV oblique imagery to enable end-to-end estimation of canopy volume and porosity in banana plantations.
Understanding the relatedness of angiosperms and the evolution of their inflorescence remains challenging, as these structures are highly modified and prone to convergent evolution. The current research describing inflorescence architecture, whether genetically or morphologically focused, typically relies on text based explanations, 2D images, or 3D based models created with CAD modeling software. This creates a disparity in the reader's understanding of these models since descriptions rely heavily on the author's interpretation of the inflorescence. The goal of our study is to bridge this disconnection by producing anatomically and color-correct 3D inflorescence models using Solanaceae flowers that readers can explore directly, which will allow readers to view and rotate the inflorescence structure in real time. The Solanaceae clade serves as an excellent platform to demonstrate this concept, as it is an active area of floral research and exhibits high inflorescence diversity within the clade. Its ancestral scorpioid cyme-like morphology is presently thought to have been the result of convergent evolution from a currently unknown driving force. Developing 3D models of extant Solanaceae species could provide valuable insights into these evolutionary patterns and help clarify the mechanisms underlying inflorescence diversification. Here, we use a novel photogrammetry approach to create 3D renderings of the inflorescences of Juanulloa sp., in the Solanaceae clade. This process will involve taking high-resolution 360° photos at various angles using a camera. Photos will be processed with Agisoft Metashape software, which generates 3D models using photographs. It is expected that the rendered 3D images will accurately reflect specimen dimensions with precise color. This will serve as a way to study and provide a larger 3D inflorescence library that can bridge the gap between authors and readers within the literature.
Why it matches plant phenotyping methodsSolanaceaeの花序形態を対象に、フォトグラメトリと3D再構成による植物形質の取得・可視化手法を開発しており、方法が研究の中心である。
abstractHere, we use a novel photogrammetry approach to create 3D renderings of the inflorescences of Juanulloa sp., in the Solanaceae clade.