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.
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. 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 3Dßprinted 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という植物形質を取得するための3D再構成手法を開発・評価しており、精度・解像度・完全性の検証も中心的に扱っている。
abstractThis work aims at reconstructing native deciduous trees in 3D as a basis for measuring and monitoring shoot elongation over entire tree canopies.
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.
The endodermis plays a critical role in root function by regulating the movement of water and nutrients. Because endodermal function emerges from coordinated interactions among neighboring cells, the three-dimensional (3D) organization of cellular networks may influence how transport pathways are spatially arranged within root tissues. However, the 3D cellular network topology of the endodermis and its potential functional significance in woody plants remain poorly understood. Here, we combined light-sheet fluorescence microscopy (LSFM), 3D reconstruction, and network topology analysis to compare the endodermal cellular networks of two tree species, balsa (Ochroma pyramidale) and Eucalyptus robusta. We found that the balsa endodermis exhibits a distinct network topology characterized by higher local connectivity, lower closeness centrality, and lower edge betweenness centrality than that of Eucalyptus. Confocal Raman spectroscopy revealed broadly similar lignin and suberin signatures in the Casparian strip of the two species. Physiological measurements further showed that balsa roots exhibited significantly higher K + influx than Eucalyptus roots. Together, these observations indicate an association between variation in endodermal network organization and differences in root ion transport characteristics. This study highlights the value of integrating three-dimensional cellular reconstruction with network analysis to investigate structure-function relationships in plant tissues.
Why it matches plant phenotyping methodsLSFMによる3D細胞再構築とネットワーク解析が、根内皮の形態・構造特性を定量化する中心的手法として用いられているため、植物フェノタイピング手法の実質的応用に該当する。
abstractHere, we combined light-sheet fluorescence microscopy (LSFM), 3D reconstruction, and network topology analysis to compare the endodermal cellular networks of two tree species, balsa (Ochroma pyramidale) and Eucalyptus robusta.
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.
Commercial greenhouse cucumber production is graded by fruit length, which drives harvest scheduling, labour allocation, and logistics. Manual measurement with thread or caliper is accurate but infeasible at commercial scale. This paper presents CucumberVision, a non-contact length estimation framework using an Intel RealSense D435 RGB-D camera. A YOLO26n instance segmentation model locates cucumbers, and SAM (ViT-B backbone) refines each detection to a pixel-precise mask. Five methods are evaluated under matched conditions: (M1) a dominant-axis skeleton scan-line baseline; (M2) PCA on the bounding-box depth point cloud; (M3) SAM mask with medial-axis skeletonisation; (M4) a hybrid keypoint-guided approach using a YOLO26-pose model predicting five anatomical landmarks (KP0--KP4) with piecewise 3D arc-length; and (M5) a novel medial arc spline method fitting a cubic spline through the 3D medial axis of the SAM mask and computing arc length by trapezoidal integration -- the first such application to elongated vegetable measurement. All methods share five-frame burst depth averaging, colour-stream intrinsic alignment, and adaptive method selection with cascading fallbacks ensuring 100% coverage. A benchmark of 48 captures across seven cucumbers in three size categories (small ~8 cm, medium ~13 cm, large ~25 cm) with thread-based ground truth establishes a significant accuracy hierarchy: M1 (MAPE 9.68%) > M2 (5.31%) > M4 (5.51%) > M3 (5.82%) > M5 (4.13%). M5 significantly outperforms all competitors at Bonferroni-corrected alpha=0.0125. A secondary contribution is identifying a 12--18% length underestimation caused by using depth-stream rather than colour-stream intrinsics after rs.align(rs.stream.color) -- an under-reported error source. The complete system is released open source and runs in real time on a single consumer-grade GPU.
Why it matches plant phenotyping methodsRGB-D画像からキュウリ果実長を推定する手法を開発し、複数手法との比較検証、実測値によるベンチマーク、誤差要因分析まで行っており、植物形質取得が研究の中心です。
abstractThis paper presents CucumberVision, a non-contact length estimation framework using an Intel RealSense D435 RGB-D camera.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
MaizeGrowth chamberRootMorphology / geometry measurementSegmentationSkeletonization / topologyRoot system architecture
monitoring capabilities, and existing models have limited accuracy in root segmentation. To address these issues, we developed a crop root phenotyping system integrating crop cultivation and data collection. We also proposed a DB-UNet model for hydroponic maize root segmentation. DB-UNet builds a CNN-ViT dual-branch parallel structure during encoder downsampling level. The lightweight ViT branch uses sequential downsampling to achieve global topological dependency modeling while reducing computational costs. An attention fusion module dynamically calibrate dual-branch features weights, achieving complementary fusion of local root edge details and global context information. we constructed a mixed loss function combining Dice loss, Focal loss, and structural consistency KL loss to solve class imbalance, hard sample segmentation, and semantic divergence of dual-branch features. On our custom hydroponic maize root dataset, DB-UNet achieved an mIoU of 91.02%, an FG IoU of 82.78%, and a Centerline-Dice of 97.72%.Compared to classic UNet, mIoU, FG IoU, and Centerline-Dice increased by 0.92%, 1.84%, and 1.99%, respectively. Plant-level five-fold cross-validation further showed that DB-UNet maintained stable segmentation performance across different plant-level partitions. Based on DB-UNet segmentation results, we propose a custom skeleton-based algorithm for multi-trait root phenotyping, enabling the extraction of total root length and root branch points. Root area is calculated from binary mask pixel statistics. Compared to the traditional Zhang-Suen algorithm, the average relative error of root length measurement is reduced to 3.14%, which is 8.42 percentage points lower than the traditional method. Furthermore, we analyzed relationships between segmentation accuracy metrics and phenotypic relative errors. Higher segmentation quality generally led to lower phenotypic relative errors and more reliable trait measurements. In particular, Centerline-Dice was closely associated with root length estimation, whereas pixel-level segmentation consistency was more closely related to root area measurement. Pearson and Spearman correlation analyses showed a strong positive correlation between maize plant height and total root length, with coefficients of 0.8466 and 0.8634, respectively.
Why it matches plant phenotyping methods画像ベースの根セグメンテーションと骨格解析を開発・検証し、根長や分枝点などの形質を抽出するシステムが研究の中心であるため。
abstractwe developed a crop root phenotyping system integrating crop cultivation and data collection.
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.
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 · UnverifiedCrossref · checked 15 Sept 2026
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.
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 · UnverifiedCrossref · checked 14 Sept 2026
BarleyRootMorphology / geometry measurementSegmentationSkeletonization / topologyRoot system architecture
• A fully automated pipeline for barley root extraction and characterization. • DeepRoot-3H : multi-head network for segmenting roots, tips, and sources. • Post-processing stage handling overlaps and dense root clusters. • Graph-based path analysis for RSML generation and trait extraction • High accuracy and robustness on challenging barley root image datasets Understanding plant root architecture under diverse environmental conditions is crucial for improving crop resilience and ensuring global food security. We present a fully automated method for segmenting barley root systems from high-resolution images and detecting keypoints such as tips and sources with high precision. At the core of our approach is DeepRoot-3H , a novel multi-head deep network built upon the DeepLabv3+ backbone, designed to jointly handle root segmentation and keypoint detection within a unified architecture. This integrated design enhances both the consistency and robustness of the outputs. A dedicated post-processing stage further refines keypoint localization, effectively handling challenges such as dense root clusters and variability in image quality. The resulting predictions are then structured into a graph representation, on which a path-walking algorithm identifies biologically meaningful connections between tips and sources. This enables the generation of RSML files and the extraction of critical morphological traits. To evaluate the system, we employ IoU and Dice scores for segmentation quality, alongside Euclidean and weighted distance metrics for tip and source detection. We also assess the biological consistency of the extracted traits—such as total root length, tortuosity, covered area, and outer angles—through correlation and discrepancy measures. Experimental results on a challenging benchmark dataset demonstrate significant improvements over existing techniques, confirming the effectiveness and reliability of our method for high-fidelity root system analysis.
Why it matches plant phenotyping methods根系画像から分割・キーポイント検出・形態形質抽出を行う自動フェノタイピング手法の開発と評価が中心である。
abstractA fully automated pipeline for barley root extraction and characterization.
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.
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.
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 15 Sept 2026
Published1 Jan 2026IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingCited by 0 · OpenAlex ↗
Leaf-wood separation is crucial for single-tree aboveground biomass estimation and 3D reconstruction. Although the non-destructive and efficient acquisition of fine-grained, high-density point cloud data can be performed using terrestrial laser scanning (TLS) technology, existing methods suffer from various drawbacks, including insufficient detection of fine branches, limited robustness to point cloud subsampling, and weak adaptability across different tree species and crown structures. A core issue lies in the over-reliance on prior values for key algorithm parameters. This study proposes an adaptive shortest path tracking for robust leaf–wood separation (ASPTS) in individual trees. First, a graph is constructed, and the shortest path backtracking is employed to extract skeleton points. Second, an improved k-nearest neighbor (KNN) algorithm is proposed to adaptively optimize the number of neighboring points based on the shortest path, thereby obtaining initial wood points. Third, the feature descriptor construction for characterizing trunk and branch structures is optimized using principal component analysis (PCA) by implementing an enhanced adaptive neighborhood radius selection strategy. Finally, final wood points are extracted using a region-growing approach guided by a stepwise feature thresholding scheme. Twenty-two individual trees, which represent different species, heights, and crown structures, are selected as test subjects. The results demonstrate the capability of ASPTS to make a good balance between type I and type II errors. ASPTS consistently exhibits strong fine-branch detection capability and robust performance under varying conditions, including different tree species, crown structures, and point cloud densities. ASPTS demonstrates superior performance compared to four state-of-the-art methods.
Why it matches plant phenotyping methodsTLS点群から個体樹木の葉・木部を分離し、枝構造やバイオマス推定・3D再構成に用いる新規アルゴリズムを開発・比較検証しており、植物形態の取得・抽出が中心である。
abstractASPTS consistently exhibits strong fine-branch detection capability and robust performance under varying conditions, including different tree species, crown structures, and point cloud densities.
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.
Leaf vein segmentation is a critical task in plant phenotyping and species classification, yet it remains challenging due to the hierarchical, curvilinear nature of veins and interference from complex backgrounds. Existing methods face three key limitations. First, they lack directional context modeling, leading to blurred vein boundaries and the omission of fine venation. Second, they fail to effectively capture global dependencies, limiting semantic coherence across spatial regions. Third, they do not incorporate explicit mechanisms for detecting vein discontinuities, which is essential for complete topological understanding. To address these challenges, we propose MultiTaskVenationNet (MTV-Net), a multi-task deep segmentation framework that integrates four complementary modules. The Strip Pooling Module (SPM) captures orientation-specific long-range context by performing directional pooling along horizontal and vertical axes, enhancing the visibility of delicate vein structures. The Global Context Block (GCBlock) aggregates long-range dependencies through channel attention at the bottleneck stage, improving the semantic consistency of encoded features. A dual-branch decoder explicitly separates the learning objectives for vein segmentation and breakpoint detection. At the same time, a hybrid upsampling strategy combines bilinear interpolation and transposed convolution to accurately reconstruct vein boundaries without introducing artifacts. Extensive experiments on the LVD2021 benchmark dataset demonstrate that MTV-Net outperforms state-of-the-art models such as U-Net, GCNet, CE-Net, and HRNet, achieving an IoU of 76.46 ± 0.27 and a Dice coefficient of 86.61 ± 0.18. The model also exhibits strong generalization across diverse leaf morphologies, vein densities, and lighting conditions, validating its effectiveness and robustness for high-precision leaf vein analysis.
Why it matches plant phenotyping methods葉脈という植物形態を画像から抽出する深層学習セグメンテーション手法を開発し、ベンチマークで性能・頑健性を検証しており、植物フェノタイピング手法が中心である。
abstractLeaf vein segmentation is a critical task in plant phenotyping and species classification
SoybeanLiDAR / point cloudRoot2D/3D reconstructionSegmentationSkeletonization / topologyRoot system architecture
Characterizing root system architecture (RSA) is essential for understanding plant acclimatization and guiding breeding strategies to enhance stress tolerance and optimize resource uptake. Although 3D root analysis provides significantly more detailed and structurally informative insights than conventional 2D methods, the development of robust and quantitative tools for 3D root phenotyping has been hindered by challenges such as data complexity, noise, and root overlap. In this study, we present a biologically inspired skeletonization framework that segments root architectures by tracing root growth trajectories. The primary objective is to enable anatomically accurate extraction of RSA traits from 3D point clouds. Our method begins by segmenting the primary root through shortest-path extraction and tangent-plane-based clustering. Lateral root initiation points are then detected, and candidate paths are grown using a bionic pathfinding strategy with adaptive parameters; an optimal, non-overlapping skeleton is selected through clustering and combination sorting, and finally refined via an inward back-tracing procedure to improve junction connectivity. To support downstream phenotyping, we compute root length and angle from the segmented skeletons, and reconstruct anatomically faithful tubular meshes for each lateral root to analytically estimate surface area and volume. Our method achieved high accuracy across multiple traits, including an F1 score of 0.88 for lateral root numeration, R2 values of 0.992 and 0.987 for primary and lateral root length estimation, respectively, and strong agreement in surface area (R2=0.953) and volume (R2=0.912) validation against reference methods. Overall, our method offers a robust and biologically meaningful solution for 3D root phenotyping. The extracted traits provide plant breeders with critical insights for genotype selection and offer plant scientists a powerful tool to evaluate the effects of agronomic treatments and environmental interventions.
Why it matches plant phenotyping methods3D根系骨架化と形態形質抽出法の開発・検証が研究の中心であり、根長・角度・表面積・体積などの表現型を定量化している。
abstractwe present a biologically inspired skeletonization framework that segments root architectures by tracing root growth trajectories
NeRF / 3D Gaussian SplattingLiDAR / point cloudStem / branch2D/3D reconstructionSegmentationSkeletonization / topologyArchitecture / morphology / geometry
The 3D reconstruction and physical simulation of plants in natural scenes are of significant research and practical value in fields such as agronomy, forestry, ecology, and remote sensing. However, mainstream 3D reconstruction methods generally focus on geometric detail recovery but lack integration with physics-driven approaches, making it challenging to accurately and efficiently simulate the dynamic changes in plant structures. Although the latest physical Gaussian methods can simulate a variety of nonrigid deformations, there is limited consideration of plant-specific structural features, which affects the accuracy of reconstruction and simulation. To address this challenge, an end-to-end woody-plant Gaussian is proposed, which is a framework of high-precision 3D reconstruction and physical simulation for woody plants. This framework begins by fine-tuning a pre-trained plant instance segmentation model tailored for this purpose to reduce environmental noise interference and improve the accuracy of skeleton extraction from point cloud data. It leverages the extracted topology to guide fine-grained hierarchical classification of branches. By segmenting hierarchical radii and cross-sectional proportions, the Gaussian point distribution is constrained, enabling the Gaussian ellipsoids to better align with branch surfaces, thereby enhancing reconstruction details. In the physical simulation stage, the framework incorporates material property variation rules. Using topological guidance, Gaussian ellipsoids are mapped to branch hierarchies, and a cantilever beam physical model predicts Gaussian distributions and covariance matrix parameters. This approach not only improves rendering quality but also enhances the realism of branch-bending simulations. Finally, we evaluate our framework on the photos of real woody plants we took (3D deformable wood plant) and a public dataset (NeRF-synthetic). Compared to existing plant reconstruction methods, woody-plant Gaussian achieves state-of-the-art performance and significantly improves the visual quality of plant physical simulations.
Why it matches plant phenotyping methods木本植物の3D形状・骨格・枝半径を画像から再構成する計算手法を中心に開発しており、植物の構造形質の取得に直接関係する。物理シミュレーションも含む技術評価が行われている。
abstracta framework of high-precision 3D reconstruction and physical simulation for woody plants
Introduction: Leaf phenotypes are key indicators of plant growth status. Existing deep learning-based leaf skeletonization typically requires extensive manual labeling, long training, and predefined keypoints, which limits scalability. We developed a training-free and label-free approach that connects spontaneously detected keypoints to generate leaf skeletons for leafy plants. Methods: The method comprises random seed-point generation and adaptive keypoint connection. For plants with random leaf morphology, we determine a threshold for the angle difference among any three consecutive adjacent points and iteratively identify keypoints within circular search neighborhoods to trace leaf skeletons. For plants with regular leaf morphology, we fit the skeleton trajectory by minimizing curvature. We validated the approach on vertical and front-view images of orchids (covering random and regular morphological cases) and extracted five phenotypic parameters from the resulting skeletons. Generalization was further assessed on a maize image dataset. Results: On orchid images, the proposed approach achieved an average curvature fitting error of 0.12 and an average leaf recall of 92%. Five orchid phenotypic parameters were accurately derived from the skeletons. The method also showed effective skeletonization on maize, indicating cross-species applicability. Discussion: By eliminating manual labels and training, this approach reduces annotation effort and computational overhead while enabling precise geometric phenotype calculation from skeleton-based keypoints. Its effectiveness on both randomly distributed and regularly shaped leafy plants suggests suitability for high-throughput plant phenotyping workflows.
Why it matches plant phenotyping methods葉の骨格化とフェノタイプ抽出のための画像解析手法を開発し、複数植物種で検証しているため、植物フェノタイピング手法が中心的である。
abstractWe developed a training-free and label-free approach that connects spontaneously detected keypoints to generate leaf skeletons for leafy plants.
Reproduction assets foundThe paper uses a publicly available single-plant maize image dataset (hosted on datasetninja) as its generalization-test input; the orchid images and the authors' algorithm code have no stated public deposit in the supplied blocks.Dataset · publicUsing the publicly available single-plant maize dataset (Dataset URL: https://datasetninja.com/maize-whole-plant-image-dataset ), which captured images of a single maize plant over 113 days—with 1 vertical view and 12 front view images taken each day—the spontaneous keypoints connection algorithm was applied to both a front view ( Figure 9 ) and a vertical view image ( Figure 9 ) from 10 different daysOpen asset ↗datasetninja · maize-whole-plant-image-datasetlines:429-438Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Published19 Oct 20252025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)Cited by 0 · OpenAlex ↗
RootObject detection2D/3D reconstructionSkeletonization / topologyRoot system architecture
Plant roots typically exhibit a highly complex and dense architecture, incorporating numerous slender lateral roots and branches, which significantly hinders the precise capture and modeling of the entire root system. Additionally, roots often lack sufficient texture and color information, making it difficult to identify and track root traits using visual methods. Previous research on roots has been largely confined to 2D studies; however, exploring the 3D architecture of roots is crucial in botany. Since roots grow in real 3D space, 3D phenotypic information is more critical for studying genetic traits and their impact on root development. We have introduced a 3D root skeleton extraction method that efficiently derives the 3D architecture of plant roots from a few images. This method includes the detection and matching of lateral roots, triangulation to extract the skeletal structure of lateral roots, and the integration of lateral and primary roots. We developed a highly complex root dataset and tested our method on it. The extracted 3D root skeletons showed considerable similarity to the ground truth, validating the effectiveness of the model. This method can play a significant role in automated breeding robots. Through precise 3D root structure analysis, breeding robots can better identify plant phenotypic traits, especially root structure and growth patterns, helping practitioners select seeds with superior root systems. This automated approach not only improves breeding efficiency but also reduces manual intervention, making the breeding process more intelligent and efficient, thus advancing modern agriculture.
Why it matches plant phenotyping methods3D画像から植物根系の骨格・構造を抽出する手法を開発し、データセット上で正解値と比較検証しており、根形態フェノタイピングが研究の中心である。
abstractWe have introduced a 3D root skeleton extraction method that efficiently derives the 3D architecture of plant roots from a few images.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
Aiming to address the issues of low efficiency and large errors in the manual measurement process of phenotypic parameters in Schima Superba seedlings, an automated non-destructive method for acquiring phenotypic parameters based on three-dimensional point clouds is proposed, which includes the main steps of alignment, skeleton extraction, and automatic phenotypic calculation. Aiming to overcome the technical challenges of stem and leaf separation in Schima Superba , a density-weighted voxel centroid method is proposed to extract skeleton points, combined with minimum spanning tree (MST) and principal component analysis (PCA) techniques to accurately identify the stem skeleton point cloud, effectively addressing the problem of stem-leaf separation. The separation process encounters difficulties at the stem-leaf junction, resulting in suboptimal separation accuracy. An improved K-means++ algorithm is proposed to initially estimate the number of adhering leaves based on coarse segmentation, followed by fine segmentation to achieve higher precision in leaf segmentation, effectively improving the accuracy and efficiency of the segmentation process. Following the completion of stem and leaf segmentation, a fully automated phenotypic characterization method based on the segmented point cloud is proposed for the first time. The method automatically outputs relevant phenotypic parameters, including plant height, stem length, stem diameter, and leaf area. The predicted correlation coefficients for the experimental phenotypes were 0.994, 0.992, 0.938, and 0.873, meeting the requirements for on-site measurement of phenotypic parameters in Schima Superba and providing strong technical support for plantation management and cultivar improvement.
Why it matches plant phenotyping methods3D点群による茎葉分離、骨格抽出、形質自動計算を開発・検証しており、植物表現型取得手法が研究の中心である。
abstractan automated non-destructive method for acquiring phenotypic parameters based on three-dimensional point clouds is proposed
This study proposes a novel system for estimating the 3D structure of grapevines as part of a robotic pruning system. The system aims to accurately and efficiently estimate grapevine structures. Utilizing two RGB-D cameras based on Time-of-Flight (ToF) technology, depth images were captured from a wide field of view. This paper employs a minimum spanning tree (MST) to estimate the grapevine skeleton using a cost function that considers node distance, gravitropism, and connection smoothness. Notably, we developed a new component classification method that accurately classifies structural parts—cordons, shoots, and buds—using only skeletal information. The results demonstrated that the system could effectively distinguish between different parts of the grapevine using just the 3D skeletal structure. The system was evaluated on 10 grapevines in real-world vineyard environments. The proposed method achieved high alignment accuracy with manually constructed ground-truth skeletons, even under occlusion. For bud estimation, statistical analysis based on field data facilitated effective estimation. The proposed method outperformed existing approaches in processing speed, with an average processing time of 619 ms per grapevine. These results indicate the potential for real-time application in robotic pruning, enabling efficient structure estimation with high accuracy. Future work will focus on integration with other subsystems integrated within a robotic pruning system, which is expected to produce synergistic effects and enhance overall system performance.
Why it matches plant phenotyping methodsRGB-D画像からブドウ樹の3D構造、器官分類、芽数を推定する手法を開発・評価しており、ロボット剪定への応用を超えて植物形態フェノタイピングが中心である。
abstractThis study proposes a novel system for estimating the 3D structure of grapevines as part of a robotic pruning system.
RootObject detection2D/3D reconstructionSkeletonization / topologyRoot system architecture
Plant roots typically exhibit a highly complex and dense architecture, incorporating numerous slender lateral roots and branches, which significantly hinders the precise capture and modeling of the entire root system. Additionally, roots often lack sufficient texture and color information, making it difficult to identify and track root traits using visual methods. Previous research on roots has been largely confined to 2D studies; however, exploring the 3D architecture of roots is crucial in botany. Since roots grow in real 3D space, 3D phenotypic information is more critical for studying genetic traits and their impact on root development. We have introduced a 3D root skeleton extraction method that efficiently derives the 3D architecture of plant roots from a few images. This method includes the detection and matching of lateral roots, triangulation to extract the skeletal structure of lateral roots, and the integration of lateral and primary roots. We developed a highly complex root dataset and tested our method on it. The extracted 3D root skeletons showed considerable similarity to the ground truth, validating the effectiveness of the model. This method can play a significant role in automated breeding robots. Through precise 3D root structure analysis, breeding robots can better identify plant phenotypic traits, especially root structure and growth patterns, helping practitioners select seeds with superior root systems. This automated approach not only improves breeding efficiency but also reduces manual intervention, making the breeding process more intelligent and efficient, thus advancing modern agriculture.
Why it matches plant phenotyping methods植物根系の3D骨格・構造という表現型を画像から抽出する手法を開発し、データセット上で検証しており、方法が研究の中心である。
abstractWe have introduced a 3D root skeleton extraction method that efficiently derives the 3D architecture of plant roots from a few images.
Plant phenotyping involves the measurements of plant traits to gain more insight into the interaction between the genotype (G), environment (E) and crop management strategies (M). To improve plant phenotyping, accurate measurements are crucial. Manual measurements are biased, time-intensive, and therefore limited to only a few plants. Especially measurements of 3D phenotypic traits, such as plant architecture, internode length, and leaf area are difficult to extract manually. To enhance the speed and accuracy of phenotyping, there is a need for automatic digital plant phenotyping solutions. The presented dataset contains 3D point clouds of tomato plants, which will enable researchers to develop novel methods to extract 3D phenotypic traits. Converting 3D point clouds to plant traits is also known as 3D plant phenotyping. This process can be subdivided into three steps: point cloud segmentation, skeletonisation to extract plant architecture, and plant-traits extraction. Those three steps need to be analysed properly to indicate bottlenecks and improve 3D phenotyping algorithms. Currently, the development of 3D phenotyping algorithms is inhibited by the availability of comprehensive datasets and algorithms to analyse all steps. To our best knowledge only five annotated datasets exist for testing and validating 3D phenotyping algorithms. However, these datasets mainly focus on the segmentation step. Skeletonisation and manual measured plant traits are frequently not included. To improve 3D plant phenotyping, a novel dataset, TomatoWUR, is presented. This comprehensive dataset consists of 44 point clouds of single tomato plants imaged by fifteen cameras to create a point cloud using the shape-from-silhouette methodology. The dataset includes annotated point clouds, skeletons, and manual reference measurements. In addition, the dataset includes software for comprehensive evaluation and comparison of phenotyping methods, which is expected to benefit the development of 3D phenotyping algorithms. The related software can be found our GIT: https://github.com/WUR-ABE/TomatoWUR.
Why it matches plant phenotyping methods3D植物フェノタイピング用の注釈付きデータセットと評価ソフトウェアを提示し、セグメンテーション、骨格化、形質抽出アルゴリズムの開発・検証を直接支援するため、方法論が中心である。
abstractThe presented dataset contains 3D point clouds of tomato plants, which will enable researchers to develop novel methods to extract 3D phenotypic traits.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicIn addition, the dataset includes software for comprehensive evaluation and comparison of phenotyping methods, which is expected to benefit the development of 3D phenotyping algorithms. The related software can be found our GIT: https://github.com/WUR-ABE/TomatoWUROpen asset ↗WUR-ABE/TomatoWURlines:1-45Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Plant phenotyping involves the measurements of plant traits to gain more insight into the interaction between the genotype (G), environment (E) and crop management strategies (M). To improve plant phenotyping, accurate measurements are crucial. Manual measurements are biased, time-intensive, and therefore limited to only a few plants. Especially measurements of 3D phenotypic traits, such as plant architecture, internode length, and leaf area are difficult to extract manually. To enhance the speed and accuracy of phenotyping, there is a need for automatic digital plant phenotyping solutions. The presented dataset contains 3D point clouds of tomato plants, which will enable researchers to develop novel methods to extract 3D phenotypic traits. Converting 3D point clouds to plant traits is also known as 3D plant phenotyping. This process can be subdivided into three steps: point cloud segmentation, skeletonisation to extract plant architecture, and plant-traits extraction. Those three steps need to be analysed properly to indicate bottlenecks and improve 3D phenotyping algorithms. Currently, the development of 3D phenotyping algorithms is inhibited by the availability of comprehensive datasets and algorithms to analyse all steps. To our best knowledge only five annotated datasets exist for testing and validating 3D phenotyping algorithms. However, these datasets mainly focus on the segmentation step. Skeletonisation and manual measured plant traits are frequently not included. To improve 3D plant phenotyping, a novel dataset, TomatoWUR, is presented. This comprehensive dataset consists of 44 point clouds of single tomato plants imaged by fifteen cameras to create a point cloud using the shape-from-silhouette methodology. The dataset includes annotated point clouds, skeletons, and manual reference measurements. In addition, the dataset includes software for comprehensive evaluation and comparison of phenotyping methods, which is expected to benefit the development of 3D phenotyping algorithms. The related software can be found our GIT: https://github.com/WUR-ABE/TomatoWUR.
Why it matches plant phenotyping methods3D植物フェノタイピング用の注釈付きデータセットと評価ソフトウェアを提供し、セグメンテーション、骨格化、形質抽出アルゴリズムの開発・検証を中心に扱っている。
abstractThe presented dataset contains 3D point clouds of tomato plants, which will enable researchers to develop novel methods to extract 3D phenotypic traits.
Accurately reconstructing tree skeletons from multi-view images is challenging. While most existing works use skeletonization from 3D point clouds, thin branches with low-texture contrast often involve multi-view stereo (MVS) to produce noisy and fragmented point clouds, which break branch connectivity. Leveraging the recent development in accurate mask extraction from images, we introduce a mask-guided graph optimization framework that estimates a 3D skeleton directly from multi-view segmentation masks, bypassing the reliance on point cloud quality. In our method, a skeleton is modeled as a graph whose nodes store positions and radii while its adjacency matrix encodes branch connectivity. We use 3D Gaussian splatting (3DGS) to render silhouettes of the graph and directly optimize the nodes and the adjacency matrix to fit given multi-view silhouettes in a differentiable manner. Furthermore, we use a minimum spanning tree (MST) algorithm during the optimization loop to regularize the graph to a tree structure. Experiments on synthetic and real-world plants show consistent improvements in completeness and structural accuracy over existing point-cloud-based and heuristic baseline methods.
Why it matches plant phenotyping methods植物のマルチビュー画像から樹木の3D骨格・枝構造を推定する計算手法を開発し、実植物で既存法と比較検証しているため、植物形態フェノタイピング手法が中心である。
abstractwe introduce a mask-guided graph optimization framework that estimates a 3D skeleton directly from multi-view segmentation masks
Reproduction assets foundThe paper's authors explicitly state their implementation is publicly available on GitHub, which is the paper-specific computational analysis code for the mask-guided tree skeleton extraction method.Code · publicOur implementation is available in the public GitHub repository ( https://github.com/huntorochi/Masks-to-Skeleton , accessed on 18 May 2025).Open asset ↗huntorochi/Masks-to-Skeletonlines:27-41Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Today, leaf trait estimation remains a labor-intensive process. The effort to obtain ground truth measurements limits how accurately this task can be performed automatically. Traditionally, plant scientists manually measure the traits of harvested leaves and associate them with sensor data, which is key for training machine learning approaches and to automate the processes. In this paper, we propose a neural network-based method to generate synthetic 3D point clouds of leaves with their associated traits to support approaches for phenotyping. We use real-world leaf point clouds to learn how to generate realistic leaves from a leaf skeleton, which is automatically extracted. We use the generated leaves to fine-tune different leaf trait estimation methods. We evaluate our generated data using different trait estimation methods and compare the results to using real-world data or other synthetic datasets from agricultural simulation software. Experiments show that our approach generates leaf point clouds with high similarity to real-world leaves. Tuning trait estimation methods on our generated data improves their performance in the estimation of real-world leaves' traits, making our data crucial for developing and testing data-driven trait estimation methods. Accurate trait estimation is key to understanding crop growth, productivity, and pest resistance, as leaf size directly influences photosynthesis, yield potential, and vulnerability to insects and fungal growth.
Why it matches plant phenotyping methods葉の形質推定を支援するため、形質付き合成3D点群を生成するニューラルネットワーク手法とデータセットを開発・評価しており、フェノタイピング手法が中心である。
abstractwe propose a neural network-based method to generate synthetic 3D point clouds of leaves with their associated traits to support approaches for phenotyping.
Reproduction assets foundThe paper uses two public 3D plant point-cloud datasets (Pheno4D and BonnBeetClouds3D) as real-world inputs for training/evaluating its leaf trait estimation and generation pipeline; both have explicit public URLs. The authors' code is only promised ('We plan to make our code publicly available'), so it is not yet an aDataset · publicWe use two publicly available datasets. Pheno4D [43] is available at the url: https://www.ipb.uni-bonn.de/data/pheno4d/index.html. It contains maize and tomato plants measured daily, over 12 and 20 days respectively.Open asset ↗Pheno4Dhtml-lines:401-424Plant phenotyping relevance match · UnverifiedOpenAlex · arXiv · checked 15 Sept 2026
Plants frequently contain numerous organs, organized in 3D branching systems defining the plant's architecture. Reconstructing the architecture of plants from unstructured observations is challenging because of self-occlusion and spatial proximity between organs, which are often thin structures. To achieve the challenging task, we propose an approach that allows to infer a parameterized representation of the plant's architecture from a given 3D scan of a plant. In addition to the plant's branching structure, this representation contains parametric information for each plant organ, and can therefore be used directly in a variety of tasks. In this data-driven approach, we train a recursive neural network with virtual plants generated using a procedural model. After training, the network allows to infer a parametric tree-like representation based on an input 3D point cloud. Our method is applicable to any plant that can be represented as binary axial tree. We quantitatively evaluate our approach on Chenopodium Album plants on reconstruction, segmentation and skeletonization, which are important problems in plant phenotyping. In addition to carrying out several tasks at once, our method achieves results on-par with strong baselines for each task. We apply our method, trained exclusively on synthetic data, to 3D scans and show that it generalizes well.
Why it matches plant phenotyping methods3Dスキャンから植物の分枝構造・器官パラメータを推定する手法を開発し、植物フェノタイピングに重要な再構成・セグメンテーション・骨格化を定量評価しているため。
abstractwe propose an approach that allows to infer a parameterized representation of the plant's architecture from a given 3D scan of a plant.
Persistent homology (PH) is a well-established mathematical approach that has been increasingly used to measure plant morphology. Stemming from topological data analysis, PH was developed as a mathematical framework to characterize topological relationships between data points. Structures are found by tracking topological features that persist across scales, making it resistant to noise and invariant to orientation and size. The great advantage of PH lies in its ability to integrate several morphological features into a single metric value. In this way, it captures multiple and comprehensive measurements better than uni- or multivariate systems. Consequently, PH enables an accurate quantification of phenological variations, quantifying the complete morphology of a plant, including growing branching structures. Most studies that use PH for plant morphology quantification use 2D images for the task. This is despite the fact that plants are essentially three-dimensional objects and should be analysed in that space. Studies that have explored PH in 3D focus on classification of man-made objects (e.g., toys or furniture). However, point clouds of trees that were acquired in their natural environment present a bigger challenge. There, the collected data is unevenly distributed, includes occlusions and highly depends on the season (leafing stage). All of these can vastly influence the topological analysis, and lead to incorrect structures. Not only that, but also the platform used to acquire the data might greatly affect the quantification. This is due to the point of view (i.e., from the air or terrestrially), which documents different parts of the tree. In this work, we test the applicability of PH for tree morphology characterization. We show how such an analysis enables us to describe various branching topologies. We use PH on individual trees that were acquired by different laser scanning platforms (i.e., UAV-borne and terrestrial), with and without leaves. This enables us to evaluate the potential of PH for 3D tree morphology characterization, test its limits, and explore its application in tree species classification.
Why it matches plant phenotyping methods3D点群に永続ホモロジーを適用して樹木の形態・分枝構造を定量化する手法を開発・評価しており、植物フェノタイピング手法が研究の中心である。
abstractIn this work, we test the applicability of PH for tree morphology characterization.
BarleyLaboratory / benchtopRoot2D/3D reconstructionSegmentationSkeletonization / topologyRoot system architecture
Plant phenotyping plays a crucial role in agricultural research, especially in identifying resilient traits essential for global food security. Quantitative analysis of root growth has become increasingly vital in evaluating a plant’s resilience to abiotic stresses and its efficiency in nutrient and water absorption. However, extracting features from root images presents substantial challenges due to the complexity of root structures, variations in size, background noise, occlusions, clutter, and inconsistent lighting conditions. In this study, we introduce “RootEx”, a comprehensive automated approach for extracting barley plant root systems from high-resolution images acquired from 2D root phenotyping systems set up in transparent growing mediums. Our method involves several stages, beginning with preprocessing to identify the Region of Interest (ROI). Subsequent stages utilize deep neural network-based segmentation, skeleton construction, and graph generation to produce detailed representations of root systems stored in RSML format. Notably, our dataset exclusively comprises primary roots without secondary roots or bifurcations, allowing for a focused examination of primary root characteristics and environmental adaptability. Evaluation against established methods, RootNav 1.8 and 2.0, reveals significant improvements in root system reconstruction accuracy across various performance indicators. Although RootEx may exhibit slightly lower performance due to the absence of neural network-based tip detection, its advantages include minimal losses in missing root lengths and independence from dedicated training datasets. Our approach effectively mitigates detection errors, providing a reliable tool for precise barley root analysis in agricultural research.
Why it matches plant phenotyping methods根画像からオオムギ根系の形質を自動抽出する手法を開発し、既存手法と精度比較しているため、植物フェノタイピング手法が研究の中心です。
abstractIn this study, we introduce “RootEx”, a comprehensive automated approach for extracting barley plant root systems from high-resolution images acquired from 2D root phenotyping systems set up in transparent growing mediums.
BarleyLaboratory / benchtopRoot2D/3D reconstructionSegmentationSkeletonization / topologyRoot system architecture
Plant phenotyping plays a crucial role in agricultural research, especially in identifying resilient traits essential for global food security. Quantitative analysis of root growth has become increasingly vital in evaluating a plant’s resilience to abiotic stresses and its efficiency in nutrient and water absorption. However, extracting features from root images presents substantial challenges due to the complexity of root structures, variations in size, background noise, occlusions, clutter, and inconsistent lighting conditions. In this study, we introduce “RootEx”, a comprehensive automated approach for extracting barley plant root systems from high-resolution images acquired from 2D root phenotyping systems set up in transparent growing mediums. Our method involves several stages, beginning with preprocessing to identify the Region of Interest (ROI). Subsequent stages utilize deep neural network-based segmentation, skeleton construction, and graph generation to produce detailed representations of root systems stored in RSML format. Notably, our dataset exclusively comprises primary roots without secondary roots or bifurcations, allowing for a focused examination of primary root characteristics and environmental adaptability. Evaluation against established methods, RootNav 1.8 and 2.0, reveals significant improvements in root system reconstruction accuracy across various performance indicators. Although RootEx may exhibit slightly lower performance due to the absence of neural network-based tip detection, its advantages include minimal losses in missing root lengths and independence from dedicated training datasets. Our approach effectively mitigates detection errors, providing a reliable tool for precise barley root analysis in agricultural research. • RootEx: automated extraction of barley root systems from high-res images. • Improved precision in root analysis through deep learning-based segmentation. • Focus on primary roots, without the complexity of secondary root systems. • Significant accuracy improvement w.r.t. RootNav 1.8 and 2.0. • RootEx minimizes detection errors, ensuring reliabile root analysis.
Why it matches plant phenotyping methods根系画像から植物形質を抽出する自動手法を開発し、既存手法と精度比較しているため、植物フェノタイピング手法が中心である。
abstractwe introduce “RootEx”, a comprehensive automated approach for extracting barley plant root systems from high-resolution images acquired from 2D root phenotyping systems
RootMorphology / geometry measurement2D/3D reconstructionSkeletonization / topologyRoot system architecture
The phenotypic parameters of root systems are vital in reflecting the influence of genes and the environment on plants, and three-dimensional (3D) reconstruction is an important method for obtaining phenotypic parameters. Based on the characteristics of root systems, being featureless, thin structures, this study proposed a skeleton-based 3D reconstruction and phenotypic parameter measurement method for root systems using multi-view images. An image acquisition system was designed to collect multi-view images for root system. The input images were binarized by the proposed OTSU-based adaptive threshold segmentation method. Vid2Curve was adopted to realize the 3D reconstruction of root systems and calibration objects, which was divided into four steps: skeleton curve extraction, initialization, skeleton curve estimation, and surface reconstruction. Then, to extract phenotypic parameters, a scale alignment method based on the skeleton was realized using DBSCAN and RANSAC. Furthermore, a small-sized root system point completion algorithm was proposed to achieve more complete root system 3D models. Based on the above-mentioned methods, a total of 30 root samples of three species were tested. The results showed that the proposed method achieved a skeleton projection error of 0.570 pixels and a surface projection error of 0.468 pixels. Root number measurement achieved a precision of 0.97 and a recall of 0.96, and root length measurement achieved an MAE of 1.06 cm, an MAPE of 2.37%, an RMSE of 1.35 cm, and an R2 of 0.99. The whole process of reconstruction in the experiment was very fast, taking a maximum of 4.07 min. With high accuracy and high speed, the proposed methods make it possible to obtain the root phenotypic parameters quickly and accurately and promote the study of root phenotyping.
Why it matches plant phenotyping methods根系の3D再構成と表現型パラメータ抽出法を開発し、精度・再現性を定量評価した研究であり、植物フェノタイピング手法が中心である。
abstractthis study proposed a skeleton-based 3D reconstruction and phenotypic parameter measurement method for root systems using multi-view images.
Trees have a strong effect on the local wind climate. To better understand their impact, an accurate and detailed reconstruction of botanical trees into digital twins from terrestrial LiDAR scan point clouds is important. However, capturing the complex, multi-scale nature of tree structures poses significant challenges. Issues such as gaps in the model due to occlusion in the point cloud data and inaccuracies in branch thickness estimations — especially for smaller branches — are prevalent limitations. Most advanced reconstruction methods today, such as TreeQSM (Raumonen et al., 2013), have been primarily designed for forestry applications, such as volume and biomass estimation. However, numerical flow simulations pose additional requirements including the need for a closed and continuous surface.This study introduces a different approach, building upon the work of Bærenzten et al., 2023, using tools from the field of computer graphics. The proposed method initially creates a graph from the point cloud by connecting nearby points. Subsequently, a highly detailed skeleton of the tree is generated using the so-called local separators approach (Bærenzten et al., 2021). Local separators are defined as collections of vertices that are contained within a sub-graph of the original graph. The removal of a local separator splits the sub-graph into multiple smaller sub-graphs. The branch diameters are subsequently determined using a hybrid method that blends data-driven estimates derived from the point cloud data with the Da Vinci rule for trees, which defines a relationship between the diameters of a mother branch and its daughter branches. Additionally, species-specific data obtained from direct diameter measurements is incorporated in the estimation process. The tree’s surface is then reconstructed by first generating an implicit representation from which a closed mesh is extracted as an iso-surface.Through a parameter study, the two main parameters for the generation of the skeleton, as well as the two main parameters influencing the branch thickness estimation, were studied in detail. The algorithm effectively handles occlusion in the point cloud, producing fully connected branching structures. The combined approach notably enhances the branch thickness estimation compared to using only one approach. We demonstrate the robustness of the method by applying it to three trees of very different dimensions, complexities, and point cloud characteristics and outline how the finally reconstructed tree will be used in atmospheric flow simulations. ReferencesRaumonen, P., Kaasalainen, M., Åkerblom, M., Kaasalainen, S., Kaartinen, H., Vastaranta, M., Holopainen, M., Disney, M., & Lewis, P. (2013). Fast Automatic Precision Tree Models from Terrestrial Laser Scanner Data. Remote Sensing, 5, 491-520. https://doi.org/10.3390/rs5020491Bærentzen, J. A., Villesen, I. B., & Dellwik, E. (2023). Reconstruction of a Botanical Tree from a 3D Point Cloud. In E. Christiani, M. Falcone, & S. Tozza (Eds.), Mathematical Methods for Objects Reconstruction: From 3D Vision to 3D Printing (Vol. 54, pp. 103-120). Springer. https://doi.org/10.1007/978-981-99-0776-2\_4Bærentzen, A., & Rotenberg, E. (2021). Skeletonization via Local Separators. ACM Transactions on Graphics, 40(5), Article 187. https://doi.org/10.1145/3459233
Why it matches plant phenotyping methodsLiDAR点群から樹木の骨格、枝径、閉曲面を再構成する手法を開発・パラメータ評価しており、植物の形態・構造形質の取得が中心である。
abstractThis study introduces a different approach, building upon the work of Bærenzten et al., 2023, using tools from the field of computer graphics.
Today, leaf trait estimation remains a labor-intensive process. The effort to obtain ground truth measurements limits how accurately this task can be performed automatically. Traditionally, plant scientists manually measure the traits of harvested leaves and associate them with sensor data, which is key for training machine learning approaches and to automate the processes. In this paper, we propose a neural network-based method to generate synthetic 3D point clouds of leaves with their associated traits to support approaches for phenotyping. We use real-world leaf point clouds to learn how to generate realistic leaves from a leaf skeleton, which is automatically extracted. We use the generated leaves to fine-tune different leaf trait estimation methods. We evaluate our generated data using different trait estimation methods and compare the results to using real-world data or other synthetic datasets from agricultural simulation software. Experiments show that our approach generates leaf point clouds with high similarity to real-world leaves. Tuning trait estimation methods on our generated data improves their performance in the estimation of real-world leaves’ traits, making our data crucial for developing and testing data-driven trait estimation methods. Accurate trait estimation is key to understanding crop growth, productivity, and pest resistance, as leaf size directly influences photosynthesis, yield potential, and vulnerability to insects and fungal growth.
Why it matches plant phenotyping methods葉の形質推定を支援するため、形質付き合成3D点群を生成するニューラルネットワーク手法とデータ評価を開発しており、植物フェノタイピング手法が中心である。
abstractwe propose a neural network-based method to generate synthetic 3D point clouds of leaves with their associated traits to support approaches for phenotyping.
Highlights A tree skeleton extraction method based on the fusion of geometric and multiscale features. DBSCAN clustering resolves skeleton omissions and merging in graph algorithms for incomplete point clouds and adjacent branches. Unique breakpoint selection strategy adaptable to various breakage scenarios. Combining local and global features to enhance the accuracy of skeleton breakpoint connections. ABSTRACT. Tree topology reconstruction is essential for applications in precision agriculture, such as canopy structure analysis and yield prediction. However, 3D reconstruction based on RGB images is often affected by noise and data loss, exacerbating the problem of missing branches during skeleton extraction. To address this issue, this paper proposes a tree skeleton extraction method that integrates local and global geometric features. First, DBSCAN is introduced into the graph-based clustering process to segment the incomplete point cloud into multiple clusters, facilitating skeleton extraction in discontinuous regions and enhancing the distinction of spatially adjacent branches. Then, potential connections between skeleton segments are identified based on local branch distance and angular features. Incorrect connections are removed through closed-loop structure recognition and filtering, ensuring accurate skeleton completion. Furthermore, the global growth direction of the tree is incorporated to refine the skeleton structure, followed by Laplacian smoothing to enhance skeleton quality. Experimental validation on point clouds from 13 real plum trees and 200 simulated trees demonstrates the effectiveness of the proposed method, achieving an accuracy of 85.96% for real trees and 88.80% for simulated trees. The results significantly improve the completeness and accuracy of tree structure reconstruction, providing a reliable approach for subsequent tree topology analysis and precision agriculture applications. Keywords: Local-global features, Missing skeleton branches, Skeleton extraction, Topological structure, Tree point clouds.
Why it matches plant phenotyping methodsRGB画像由来の樹木点群から枝・樹冠構造を抽出する計算手法の開発と実データ・シミュレーションによる検証が中心であり、植物形態形質の取得に直接関係する。
abstractthis paper proposes a tree skeleton extraction method that integrates local and global geometric features.
The three-dimensional reconstruction of fruit trees plays a crucial role in assessing their growth status, analyzing agronomic traits, and categorizing their organs. This is vital for implementing intelligent orchard management. This study aims to develop a cost-effective and efficient method for the three-dimensional reconstruction and skeleton extraction of fruit trees. The proposed method leverages the 3D geometric structure captured by Time-of-Flight (TOF) sensors and addresses common issues such as occlusion and perspective ambiguity. Firstly, the TOF sensor and its supporting components are used to build an acquisition platform to collect the full range point cloud of fruit trees in the key growth period. The noise information is filtered through the point cloud preprocessing operation to obtain the complete target point cloud and extract its structural invariant features. The IWOA-RANSAC-NDT algorithm is introduced for 3D model registration. Secondly, the Delaunay triangulation algorithm and Dijkstra shortest path algorithm are used to calculate the Minimum Spanning Tree. Branch segmentation is expedited using the Kd-tree data structure. The Levenberg Marquardt algorithm and the cylindrical fitting method are used to obtain the full fruit tree skeleton model. Finally, taking walnut tree as the experimental object, a high-precision fruit tree point cloud model is constructed, and the actual verification is carried out based on the measured data. Findings indicate that the proposed methodology can accurately construct both 3D point cloud and skeleton models of fruit trees with accuracy deviations from the measured data remaining within 7 %. The proposed method offers valuable data and technical support for the future development of highly autonomous, practical, and user-oriented fruit tree pruning systems.
Why it matches plant phenotyping methods果樹の3D形態・骨格を取得および抽出するTOFセンサベースの手法と取得プラットフォームを開発し、実測データで検証しているため、植物フェノタイピング手法が中心である。
abstractThis study aims to develop a cost-effective and efficient method for the three-dimensional reconstruction and skeleton extraction of fruit trees.
Aiming at the problems of low efficiency and high cost in determining the phenotypic parameters of Cymbidium seedlings by artificial approaches, this study proposed a fully automated measurement scheme for some phenotypic parameters based on point cloud. The key point or difficulty is to design a segmentation method for individual tillers according to the morphology-specific structure. After determining the branch points, two rounds of segmentation schemes were designed. The non-overlapping part of each tiller and the overlapping parts of each ramet are separated in the first round based on the edge point cloud-based segmentation, while in the second round, the overlapping part was sliced along the horizontal direction according to the weight ratio of the tillers above, to obtain the complete point cloud of all tillers. The core superiority of the algorithm is that the segmentation fits the tiller growth direction well, and the extracted skeleton points of tillers are close to the actual growth direction, significantly improving the prediction accuracy of the subsequent phenotypic parameters. Five phenotypic parameters, plant height, leaf number, leaf length, leaf width and leaf area, were automatically calculated. Through experiments, the accuracy of the five parameters reached 98.6%, 100%, 92.2%, 89.1%, and 82.3%, respectively, which reach the needs of various phenotypic applications.
Why it matches plant phenotyping methods3D点群による植物形態計測法を開発し、分蘖のセグメンテーションと5種類の表現型パラメータ抽出を精度検証しており、フェノタイピング手法が研究の中心である。
titleAn automated phenotyping method for Chinese Cymbidium seedlings based on 3D point cloud.
Characterizing the architecture of tree root systems is essential to advance the development of root-inspired anchorage in engineered systems. This study explores the structural root architectures of orchard trees to understand the interplays between the mechanical behavior of roots and the root architecture. Full three-dimensional (3D) models of natural tree root systems, Lovell, Marianna, and Myrobalan, that were extracted from the ground by vertical pullout are reconstructed through photogrammetry and later skeletonized as nodes and root branch segments. Combined analyses of the full 3D models and skeletonized models enable a detailed examination of basic bulk properties and quantification of architectural parameters. While the root segments are divided into three categories, trunk root, main lateral root, and remaining roots, the patterns in branching and diameter distributions show significant differences between the trunk and main laterals versus the remaining lateral roots. In general, the branching angle decreases over the sequence of bifurcations. The main lateral roots near the trunk show significant spreading while the lateral roots near the ends grow roughly parallel to the parent root. For branch length, the roots bifurcate more frequently near the trunk and later they grow longer. Local thickness analysis confirms that the root diameter decays at a higher rate near the trunk than in the remaining lateral roots, while the total cross-sectional area across a bifurcation node remains mostly conserved. The histograms of branching angle, and branch length and thickness gradient can be described using lognormal and exponential distributions, respectively. This unique study presents data to characterize mechanically important structural roots, which may help link root architecture to the mechanical behaviors of root structures.
Why it matches plant phenotyping methods根系の3D形状をフォトグラメトリで再構築し、骨格化して分枝角・長さ・厚さなどの形態形質を定量化する手法が研究の中心である。
abstractFull three-dimensional (3D) models of natural tree root systems, Lovell, Marianna, and Myrobalan, that were extracted from the ground by vertical pullout are reconstructed through photogrammetry and later skeletonized as nodes and root branch segments.
Rice panicle traits serve as critical indicators of both yield potential and germplasm resource quality. However, traditional manual measurements of these traits, which typically involve threshing, are not only laborious and time-consuming but also prone to introducing measurement errors. This study introduces a high-throughput and nondestructive method, termed extraction of panicle traits (EOPT), along with the software Panicle Analyzer, which is designed to assess unshaped intact rice panicle traits, including the panicle grain number, grain length, grain width, and panicle length. To address the challenge of grain occlusion within an intact panicle, we define a panicle morphology index to quantify the occlusion levels among the rice grains within the panicle. By calibrating the grain number obtained directly from rice panicle images based on the panicle morphology index, we substantially improve the grain number detection accuracy. For measuring grain length and width, the EOPT selects rice grains using an intersection over union threshold of 0.8 and a confidence threshold of 0.7 during the grain detection process. The mean values of these grains were calculated to represent all the panicle grain lengths and widths. In addition, EOPT extracted the main path of the skeleton of the rice panicle using the Astar algorithm to determine panicle lengths. Validation on a dataset of 1,554 panicle images demonstrated the effectiveness of the proposed method, achieving 93.57% accuracy in panicle grain counting with a mean absolute percentage error of 6.62%. High accuracy rates were also recorded for grain length (96.83%) and panicle length (97.13%). Moreover, the utility of EOPT was confirmed across different years and scenes, both indoors and outdoors. A genome-wide association study was conducted, leveraging the phenotypic traits obtained via EOPT and genotypic data. This study identified single-nucleotide polymorphisms associated with grain length, width, number per panicle, and panicle length, further emphasizing the utility and potential of this method in advancing rice breeding.
Why it matches plant phenotyping methods画像からイネ穂の粒数・粒長・粒幅・穂長を高スループットかつ非破壊で抽出する手法とソフトウェアを開発し、データセットで精度検証しているため、植物表現型取得法が研究の中心である。
abstractThis study introduces a high-throughput and nondestructive method, termed extraction of panicle traits (EOPT), along with the software Panicle Analyzer, which is designed to assess unshaped intact rice panicle traits, including the panicle grain number, grain length, grain width, and panicle length.
Background and aims Lidar is a promising tool for fast and accurate measurements of trees. There are several approaches to estimate above-ground woody biomass using lidar point clouds. One of the most widely used methods involves fitting geometric primitives (e.g. cylinders) to the point cloud, thereby reconstructing both the geometry and topology of the tree. However, current algorithms are not suited for accurate estimation of the volume of finer branches, because of the unreliable point dispersions from, for example, beam footprint compared to the structure diameter. Method We propose a new method that couples point cloud-based skeletonization and multi-linear statistical modelling based on structural data to make a model (structural model) that accurately estimates the above-ground woody biomass of trees from high-quality lidar point clouds, including finer branches. The structural model was tested at segment, axis and branch level, and compared to a cylinder fitting algorithm and to the pipe model theory. Key results The model accurately predicted the biomass with 1.6 % normalized root mean square error (nRMSE) at the segment scale from a k-fold cross-validation. It also gave satisfactory results when scaled up to the branch level with a significantly lower error (13 % nRMSE) and bias (-5 %) compared to conventional cylinder fitting to the point cloud (nRMSE: 92 %, bias: 82 %), or using the pipe model theory (nRMSE: 31 %, bias: -27 %). The model was then applied to the whole-tree scale and showed that the sampled trees had more than 1.7 km of structures on average and that 96 % of that length was coming from the twigs (i.e. Conclusions The structural model approach is an effective method that allows a more accurate estimation of the volumes of smaller branches from lidar point clouds. This method is versatile but requires manual measurements on branches for calibration. Nevertheless, once the model is calibrated, it can provide unbiased and large-scale estimations of tree structure volumes, making it an excellent choice for accurate 3D reconstruction of trees and estimating standing biomass.
Why it matches plant phenotyping methodsLiDAR点群から樹木の細枝を含む構造・地上部木質バイオマスを推定する手法を開発し、既存手法と比較検証しており、植物形質取得が中心である。
abstractWe propose a new method that couples point cloud-based skeletonization and multi-linear statistical modelling based on structural data to make a model (structural model) that accurately estimates the above-ground woody biomass of trees from high-quality lidar point clouds, including finer branches.
Tissue morphogenesis remains poorly understood. In plants, a central problem is how the 3D cellular architecture of a developing organ contributes to its final shape. We address this question through a comparative analysis of ovule morphogenesis, taking advantage of the diversity in ovule shape across angiosperms. Here, we provide a 3D digital atlas of Cardamine hirsuta ovule development at single cell resolution and compare it with an equivalent atlas of Arabidopsis thaliana. We introduce nerve-based topological analysis as a tool for unbiased detection of differences in cellular architectures and corroborate identified topological differences between two homologous tissues by comparative morphometrics and visual inspection. We find that differences in topology, cell volume variation and tissue growth patterns in the sheet-like integuments and the bulbous chalaza are associated with differences in ovule curvature. In contrast, the radialized conical ovule primordia and nucelli exhibit similar shapes, despite differences in internal cellular topology and tissue growth patterns. Our results support the notion that the structural organization of a tissue is associated with its susceptibility to shape changes during evolutionary shifts in 3D cellular architecture.
Why it matches plant phenotyping methods3Dデジタルアトラスと神経ベースのトポロジー解析、形態計測を用いて植物器官の細胞構造と形状を定量化しており、表現型取得・解析手法が研究の中心である。
abstractHere, we provide a 3D digital atlas of Cardamine hirsuta ovule development at single cell resolution and compare it with an equivalent atlas of Arabidopsis thaliana.
Reproduction assets foundThe paper's topological analysis and statistical evaluation code is publicly available on GitHub (NADO repository), with explicit availability language. The paper-specific 3D digital ovule dataset (S-BIAD957) is deposited in BioStudies, but no matching allowed URL exists for it, so it cannot be listed as an actionable,Code · publicThe source code and the Dockerfiles can be obtained from the Github repository at https://github.com/fabian-roll/NADO .Open asset ↗https://github.com/fabian-roll/NADO · NADOlines:109-124Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Published18 May 2024International Journal of Applied Earth Observation and GeoinformationCited by 16 · OpenAlex ↗
This paper presents a novel fully automated approach for generating structured 3D synthetic tree models, addressing the limitations of existing datasets used in applications like digital twin construction, carbon stock calculation, and environmental assessments. The method allows for the automated creation of a large-scale dataset containing 13,000 tree models of ten common species, each featuring a detailed 3D point cloud with hierarchical structures, precise parameters, and separate branch and leaf information. The dataset includes both original and noise-added point clouds to enhance method testing and evaluation. It stands out by providing comprehensive structural data, including branch numbering and detailed tree skeleton information with node hierarchies and radius data. Furthermore, this study introduces randomly distributed batches of tree models within specific terrains. It provides results from airborne laser scanning simulations, which facilitate the individualized segmentation of these tree models. This first-of-its-kind, extensive synthetic dataset is designed for accurate algorithm evaluation in tasks such as branch-leaf separation, 3D reconstruction, individual tree segmentation and carbon stock estimation. The paper validates the dataset’s utility by applying state-of-the-art algorithms to demonstrate its effectiveness in various applications, marking a significant advancement in 3D tree modeling research. The datasets are publicly available, accessible via the ‘‘TreeNet3D Dataset’’ link.
Why it matches plant phenotyping methods樹木の3D構造・枝葉分離・個体セグメンテーションを対象とする大規模合成データセットで、植物形態の推定手法の評価・ベンチマークが中心である。
abstractThis first-of-its-kind, extensive synthetic dataset is designed for accurate algorithm evaluation in tasks such as branch-leaf separation, 3D reconstruction, individual tree segmentation and carbon stock estimation.
Published10 May 2024The 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 technology for 3D reconstruction of tree models based on point clouds has been extensively researched, necessitating effective datasets for the study of branch and leaf separation, skeleton point extraction, and tree parameter extraction methods. However, existing datasets for 3D tree models face several challenges, including insufficient data volume for deep learning network training, low accuracy of model ground truth impeding effective method precision evaluation, and a lack of dataset richness to satisfy the needs of multi-type method assessments. In response to these challenges, This paper introduces, for the first time, a fully automated method for generating structured three-dimensional synthetic tree models, and constructs a large-scale 3D synthetic tree dataset enriched with comprehensive structural information. This method facilitates automated computation across several processes, including the mass generation of simulated trees, separation of branches and leaves, noise generation, extraction of skeleton points, and volume calculation. To validate the usability of this dataset across various applications, this paper employs state-of-the-art (SoTA) algorithms to verify the accuracy of methods in 3D tree model reconstruction and carbon stock calculation, thereby thoroughly demonstrating the dataset’s effectiveness.
Why it matches plant phenotyping methods3D樹木モデルの自動生成法と構造情報付き大規模データセットを開発し、樹木再構成および炭素蓄積量推定手法の検証に用いており、植物形質抽出のための方法・ベンチマークが中心である。
abstractThis paper introduces, for the first time, a fully automated method for generating structured three-dimensional synthetic tree models, and constructs a large-scale 3D synthetic tree dataset enriched with comprehensive structural information.
Primary branch length is an important morphological trait of individual apple tree phenotypes. This study presents a novel method for estimating the primary branch lengths of individual apple trees during the deciduous period by distinguishing their instances, i.e., merging those belonging to the same primary branch based on part segmentation outputs of PointNet++. Firstly, colored and colorless 3D-datasets were prepared for training PointNet++ models. The model with higher overall accuracy (OA), class average accuracy (CAA), and mean intersection-over-union (mIoU) was employed to segment the point cloud of a tree into primary branches (PB), trunk (TK), and end-points of primary branches (EPB) of individual apple trees. Skeletonization was applied to the outputs of the three parts of individual apple trees. Subsequently, each primary branch instance was distinguished by determining its corresponding path and retaining the longest path of the same primary branch only. Finally, the primary branch length was estimated by calculating the sum of Euclidean distances between adjacent points on the corresponding path. Results indicated that adding color to point clouds did not improve segmentation accuracy of PointNet++ on segmenting PB, TK, and EPB with similar color features. The PointNet++ model that was trained without color achieved an OA, CAA, and mIoU of 0.84, 0.83, and 0.70, respectively. The proportion of estimated and ground-truth values of the number of primary branches was 93.64 %. The mean absolute percentage error of estimating primary branch lengths was 12.00 %. These findings demonstrate that the proposed method is promising for high-throughput phenotyping of apple trees.
Why it matches plant phenotyping methodsリンゴ樹の3D点群を分割・骨格化し、一次枝長という形態形質を推定する手法を開発・評価しており、植物フェノタイピング手法が研究の中心である。
abstractThis study presents a novel method for estimating the primary branch lengths of individual apple trees during the deciduous period by distinguishing their instances
Stem diameter is a critical phenotypic parameter for maize, integral to yield prediction and lodging resistance assessment. Traditionally, the quantification of this parameter through manual measurement has been the norm, notwithstanding its tedious and laborious nature. To address these challenges, this study introduces a non-invasive field-based system utilizing depth information from RGB-D cameras to measure maize stem diameter. This technology offers a practical solution for conducting rapid and non-destructive phenotyping. Firstly, RGB images, depth images, and 3D point clouds of maize stems were captured using an RGB-D camera, and precise alignment between the RGB and depth images was achieved. Subsequently, the contours of maize stems were delineated using 2D image processing techniques, followed by the extraction of the stem's skeletal structure employing a thinning-based skeletonization algorithm. Furthermore, within the areas of interest on the maize stems, horizontal lines were constructed using points on the skeletal structure, resulting in 2D pixel coordinates at the intersections of these horizontal lines with the maize stem contours. Subsequently, a back-projection transformation from 2D pixel coordinates to 3D world coordinates was achieved by combining the depth data with the camera's intrinsic parameters. The 3D world coordinates were then precisely mapped onto the 3D point cloud using rigid transformation techniques. Finally, the maize stem diameter was sensed and determined by calculating the Euclidean distance between pairs of 3D world coordinate points. The method demonstrated a Mean Absolute Percentage Error ( MAPE ) of 3.01%, a Mean Absolute Error ( MAE ) of 0.75 mm, a Root Mean Square Error ( RMSE ) of 1.07 mm, and a coefficient of determination ( R ²) of 0.96, ensuring accurate measurement of maize stem diameter. This research not only provides a new method of precise and efficient crop phenotypic analysis but also offers theoretical knowledge for the advancement of precision agriculture.
Why it matches plant phenotyping methodsRGB-Dカメラと画像・3D処理によりトウモロコシ茎径を非破壊測定する手法を開発し、誤差指標で精度検証しており、フェノタイピング手法が中心である。
abstractthis study introduces a non-invasive field-based system utilizing depth information from RGB-D cameras to measure maize stem diameter
Reproduction assets foundThe paper's data availability statement points to a public Figshare deposit (DOI 10.6084/m9.figshare.25450039) containing the study's datasets (RGB/depth imagery and stem diameter measurements used for the maize stem diameter phenotyping analysis). No author analysis code or trained models are explicitly deposited.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: http://dx.doi.org/10.6084/m9.figshare.25450039 .Open asset ↗figshare · 10.6084/m9.figshare.25450039lines:909-917Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 7 Sept 2026
Traditional phenotyping relies on experts visually examining plants for physical traits like size, color, or disease presence. Measurements are taken manually using rulers, scales, or color charts, with all data recorded by hand. This labor-intensive and time-consuming process poses a significant obstacle to the efficient breeding of new cultivars. Recent innovations in computer vision and machine learning offer potential solutions for accelerating the development of robust and highly effective plant phenotyping. This study introduces an efficient plant recognition framework that leverages the power of the Segment Anything Model (SAM) guided by Explainable Contrastive Language–Image Pretraining (ECLIP). This approach can be applied to a variety of plant types, eliminating the need for labor-intensive manual phenotyping. To enhance the accuracy of plant phenotype measurements, a B-spline curve is incorporated during the plant component skeleton extraction process. The effectiveness of our approach is demonstrated through experimental results, which show that the proposed framework achieves a mean absolute error (MAE) of less than 0.05 for the majority of test samples. Remarkably, this performance is achieved without the need for model training or labeled data, highlighting the practicality and efficiency of the framework.
Why it matches plant phenotyping methods植物の認識・セグメンテーションと表現型パラメータ測定のための画像解析フレームワークを開発しており、方法自体が研究の中心です。
abstractThis study introduces an efficient plant recognition framework that leverages the power of the Segment Anything Model (SAM) guided by Explainable Contrastive Language–Image Pretraining (ECLIP).
Automated phenotyping of plants for breeding and plant studies promises to provide quantitative metrics on plant traits at a previously unattainable observation frequency. Developers of tools for performing high-throughput phenotyping are, however, constrained by the availability of relevant datasets on which to perform validation. To this end, we present a spatio-temporal dataset of 3D point clouds of strawberry plants for two varieties, totalling 84 individual point clouds. We focus on the end use of such tools - the extraction of biologically relevant phenotypes - and demonstrate a phenotyping pipeline on the dataset. This comprises of the steps, including; segmentation, skeletonisation and tracking, and we detail how each stage facilitates the extraction of different phenotypes or provision of data insights. We particularly note that assessment is focused on the validation of phenotypes, extracted from the representations acquired at each step of the pipeline, rather than singularly focusing on assessing the representation itself. Therefore, where possible, we provide \textit{in silico} ground truth baselines for the phenotypes extracted at each step and introduce methodology for the quantitative assessment of skeletonisation and the length trait extracted thereof. This dataset contributes to the corpus of freely available agricultural/horticultural spatio-temporal data for the development of next-generation phenotyping tools, increasing the number of plant varieties available for research in this field and providing a basis for genuine comparison of new phenotyping methodology.
Why it matches plant phenotyping methods植物の3D点群データセットを提供し、セグメンテーション・骨格化・追跡による表現型抽出パイプラインと、その定量的検証手法を中心に扱っているため。
abstractThis comprises of the steps, including; segmentation, skeletonisation and tracking, and we detail how each stage facilitates the extraction of different phenotypes or provision of data insights.
Reproduction assets foundThe paper's LAST-Straw dataset (84 strawberry plant point clouds with semantic/instance annotations and ground-truth stem skeletons) and supplementary graph-matching code are both publicly available via author-provided URLs in the data availability statement.Code · publicSupplementary code
for graph matching can be accessed via https://github.com/LCAS/GraphMatching3D.Open asset ↗LCAS/GraphMatching3Dpdf-page:31 lines:1-39Plant phenotyping relevance match · UnverifiedCrossref · checked 7 Sept 2026
To address the current problem of the difficulty of extracting the phenotypic parameters of tomato plants in a non-destructive and accurate way, we proposed a method of stem and leaf segmentation and phenotypic extraction of tomato plants based on skeleton extraction and supervoxel clustering. To carry out growth and cultivation experiments on tomato plants in a solar greenhouse, we obtained multi-view image sequences of the tomato plants to construct three-dimensional models of the plant. We used Laplace’s skeleton extraction algorithm to extract the skeleton of the point cloud after removing the noise points using a multi-filtering algorithm, and, based on the plant skeleton, searched for the highest point path, height constraints, and radius constraints to separate the stem from the leaf. At the same time, a supervoxel segmentation method based on Euclidean distance was used to segment each leaf. We extracted a total of six phenotypic parameters of the plant: height, stem diameter, leaf angle, leaf length, leaf width and leaf area, using the segmented organs, which are important for the phenotype. The results showed that the average accuracy, average recall and average F1 scores of the stem and leaf segmentation were 0.88, 0.80 and 0.84, and the segmentation indexes were better than the other four segmentation algorithms; the coefficients of determination between the measurement values of the phenotypic parameters and the real values were 0.97, 0.84, 0.88, 0.94, 0.92 and 0.93; and the root-mean-square errors were 2.17 cm, 0.346 cm, 5.65°, 3.18 cm, 2.99 cm and 8.79 cm2. The measurement values of the proposed method had a strong correlation with the actual values, which could satisfy the requirements of daily production and provide technical support for the extraction of high-throughput phenotypic parameters of tomato plants in solar greenhouses.
Why it matches plant phenotyping methodsトマトの3次元画像から茎・葉を分割し、複数の表現型形質を抽出する手法の開発と精度検証が研究の中心であるため。
abstractwe proposed a method of stem and leaf segmentation and phenotypic extraction of tomato plants based on skeleton extraction and supervoxel clustering.
The plant endoplasmic reticulum forms a network of tubules connected by three-way junctions or sheet-like cisternae. Although the network is three-dimensional, in many plant cells, it is constrained to thin volume sandwiched between the vacuole and plasma membrane, effectively restricting it to a 2-D planar network. The structure of the network, and the morphology of the tubules and cisternae can be automatically extracted following intensity-independent edge-enhancement and various segmentation techniques to give an initial pixel-based skeleton, which is then converted to a graph representation. ER dynamics can be determined using optical flow techniques from computer vision or persistency analysis. Collectively, this approach yields a wealth of quantitative metrics for ER structure and can be used to describe the effects of pharmacological treatments or genetic manipulation. The software is publicly available.
Why it matches plant phenotyping methods植物細胞内ERの形態・動態を画像から抽出し、定量指標を算出する解析手法と公開ソフトウェアが中心であるため。
abstractThe structure of the network, and the morphology of the tubules and cisternae can be automatically extracted following intensity-independent edge-enhancement and various segmentation techniques to give an initial pixel-based skeleton, which is then converted to a graph representation.
The cultivation of meadow orchards provides an ecological benefit for biodiversity, which is significantly higher than in intensively cultivated orchards. However, the maintenance of meadow orchards is not economically profitable. The use of automation for pruning would reduce labour costs and avoid accidents. The goal of this research was, using photogrammetric point clouds, to automatically calculate tree models, without additional human input, as basis to estimate pruning points for meadow orchard trees. Pruning estimates require a knowledge of the major tree structure, containing the branch position, the growth direction and their topological connection. Therefore, nine apple trees were captured photogrammetrically as 3D point clouds using an RGB camera. To extract the tree models, the point clouds got filtered with a random forest algorithm, the trunk was extracted and the resulting point clouds were divided into numerous K-means clusters. The cluster centres were used to create skeleton models using methods of graph theory. For evaluation, the nodes and edges of the calculated and the manually created reference tree models were compared. The calculated models achieved a producer’s accuracy of 73.67% and a user's accuracy of 74.30% of the compared edges. These models now contain the geometric and topological structure of the trees and an assignment of their point clouds, from which further information, such as branch thickness, can be derived on a branch-specific basis. This is necessary information for the calculation of pruning areas and for the actual pruning planning, needed for the automation of tree pruning.
Why it matches plant phenotyping methods3D点群から果樹の枝構造・トポロジーを自動抽出し、手動モデルと精度比較しており、植物形態表現型の取得手法が中心です。
abstractThe goal of this research was, using photogrammetric point clouds, to automatically calculate tree models, without additional human input, as basis to estimate pruning points for meadow orchard trees.
Automatic acquisition of tomato canopies’ phenotypic traits is essential for tomato varieties’ selection and scientific cultivation. Due to the infinite growth characteristics of tomato, its organ development is stochastic and the canopies' internal structure of is also complex. These make it challenging to obtain organs’ detailed phenotypic traits. Thus, this work proposed a method for detecting tomato canopies’ phenotypic traits based on improved skeleton extraction algorithm (ISEA). Firstly, after collecting tomato canopies' point cloud data from multiple perspectives, this work reconstructed its three-dimensional (3D) model accurately. Secondly, the least squares method was used to simplify the spatial contraction model of the Laplace Skeleton Extraction algorithm to obtain the tomatoes’ skeleton point set. On this basic, Greedy algorithm was used to optimise the Edge Collapse algorithm to extract more accurate and reliable skeleton structure. Then, in conjunction with the canopy growth characteristics of the crop and the Intrinsic Shape Signatures (ISS) principle, the simplified skeleton structure was subjected to local principal component analysis (PCA), which achieved the separation of tomatoes’ main stem and leaves. Finally, a modelling algorithm based on Delaunay triangulation was applied to construct the separated organs’ model to calculate phenotypic traits such as stem diameter, leaf area index and average leaf inclination. The calculated results were also compared with the measured values at different growth stages for performance evaluation. The average precision, average recall, average accuracy and micro F1 score of ISEA were 0.9144, 0.7751, 0.7243 and 0.8306, respectively. The overall R2 between calculated values and measured values for stem diameter, leaf area index and average leaf inclination were 0.9638, 0.9067, and 0.9428, and the root mean square errors (RMSE) were 0.3922, 0.0029, and 0.0186, respectively. As a result, the proposed method can extract a simple skeleton from the complex tomato canopy and calculate phenotypic traits accurately. It also provided solid technical support for studying the interaction about “genotype-phenotype-envirotype” in tomato.
Why it matches plant phenotyping methodsトマト群落の3D点群から骨格を抽出し、器官分離と茎径・葉面積指数・葉傾斜角などの表現型形質を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。
abstractFinally, a modelling algorithm based on Delaunay triangulation was applied to construct the separated organs’ model to calculate phenotypic traits such as stem diameter, leaf area index and average leaf inclination.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Abstract Background : The use of 3D imaging techniques, such as X-ray CT, in root phenotyping has become more widespread in recent years. However, due to the complexity of root structure, analyzing the resulting 3D volumes to obtain detailed architectural traits of the root system remains a challenging computational problem. Two types of root features that are notably missing from existing computational image-based phenotyping methods are the whorls of a nodal root system and soil line in an excavated root crown. Knowledge of these features would give biologists deeper insights into the structure of nodal roots and the below- and above-ground root properties. Results : We developed TopoRoot+, a computational pipeline that computes architectural traits from 3D X-ray CT volumes of excavated maize root crowns. TopoRoot+ builds upon the TopoRoot software [1], which computes a skeleton representation of the root system and produces a suite of fine-grained traits including the number, geometry, connectivity, and hierarchy level of individual roots. TopoRoot+ adds new algorithms on top of TopoRoot to detect whorls, their associated nodal roots, and the soil line location. These algorithms offer a new set of traits related to whorls and soil lines, such as internode distances, root traits at every hierarchy level associated with a whorl, and aggregate root traits above or below the ground. TopoRoot+ is validated on a diverse collection of field-grown maize root crowns consisting of nine genotypes and spanning across three years, and it exhibits reasonable accuracy against manual measurements for both whorl and soil line detection. TopoRoot+ runs in minutes for a typical downsampled volume size of 400 3 on a desktop workstation. Our software and test dataset are freely distributed on Github. Conclusions : TopoRoot+ advances the state-of-the-art in image-based root phenotyping by offering more detailed architectural traits related to whorls and soil lines. The efficiency of TopoRoot+ makes it well-suited for high-throughput image-based root phenotyping.
Why it matches plant phenotyping methodsCT画像からトウモロコシ根系の形態形質を抽出する計算パイプラインを開発し、手動測定および多様な圃場試料で検証した、中心的な画像ベース植物フェノタイピング研究。
abstractWe developed TopoRoot+, a computational pipeline that computes architectural traits from 3D X-ray CT volumes of excavated maize root crowns.
Eucalyptus plantation forests in southern China provide not only the economic value of producing timber, but also the ecological value service of absorbing carbon dioxide and releasing oxygen. Based on the theory of spatial colonial modeling, this paper proposes a new method for 3D reconstruction of tree terrestrial LiDAR point clouds for determining the aboveground carbon stock of eucalyptus monocotyledons, which consists of the main steps of tree branch and trunk separation, skeleton extraction and optimization, 3D reconstruction, and carbon stock calculation. The main trunk and branches of the tree point clouds are separated using a layer-by-layer judgment and clustering method, which avoids errors in judgment caused by sagging branches. By optimizing and adjusting the skeleton to remove small redundant branches, the near-parallel branches belonging to the same tree branch are fused. The missing parts of the skeleton point clouds were complemented using the cardinal curve interpolation algorithm, and finally a real 3D structural model was generated based on the complemented and smoothed tree skeleton expansion. The bidirectional Hausdoff distance, average Hausdoff distance, and F distance were used as evaluation indexes, which were reduced by 0.7453 m, 0.0028 m, and 0.0011 m, respectively, and the improved spatial colonization algorithm enhanced the accuracy of the reconstructed tree 3D structural model. To verify the accuracy of our method to determine the carbon stock and its related parameters, we cut down 41 eucalyptus trees and destructively sampled the measurement data as reference values. The R2 of the linear fit between the reconstructed single-tree aboveground carbon stock estimates and the reference values was 0.96 with a CV(RMSE) of 16.23%, the R2 of the linear fit between the trunk volume estimates and the reference values was 0.94 with a CV(RMSE) of 19.00%, and the R2 of the linear fit between the branch volume estimates and the reference values was 0.95 with a CV(RMSE) of 38.84%. In this paper, a new method for reconstructing eucalyptus carbon stocks based on TLS point clouds is proposed, which can provide decision support for forest management and administration, forest carbon sink trading, and emission reduction policy formulation.
Why it matches plant phenotyping methodsTLS点群から樹木構造を再構成し、地上部炭素蓄積量や幹・枝体積を推定する手法を開発し、伐倒調査で精度検証しているため、植物表現型の取得・推定が中心です。
abstractthis paper proposes a new method for 3D reconstruction of tree terrestrial LiDAR point clouds for determining the aboveground carbon stock of eucalyptus
For high-value precision agriculture, monitoring plant growth trends, pest and disease control, and automation processes will standardize operations and increase yield while reducing losses as much as possible. In greenhouse scenarios, one method for monitoring plant growth requires localization, modeling, and post-processing of the plants. However, recognizing and extracting the root position of the plant is difficult for a robotic arm. To overcome this challenge, this paper uses a marker-based localization method to provide the root position directly. After acquiring and iteratively registering the point cloud, the main stem of the plant is extracted for future plant organ segmentation and clustering. Nevertheless, extracting the main stem is a complex task, and although there are studies on skeleton extraction for ordinary trees or wheat, there are few solutions for high-wire plant stem extraction. Therefore, an optimized geometric-based stem extraction algorithm (SEA) can extract the stem point cloud with a high success rate under conditions no matter whether the cloud is intact or the main stem region is occluded.
Why it matches plant phenotyping methods植物の点群から主茎を抽出する手法を開発しており、将来の器官分割に利用可能な植物形態特徴の取得が研究の中心であるため。
abstractAfter acquiring and iteratively registering the point cloud, the main stem of the plant is extracted for future plant organ segmentation and clustering.
Pinus massoniana (Lamb.) is an important plantation species in southern China. Accurate measurement of P. massoniana seedling morphological indicators is crucial for accelerating seedling quality assessment. Machine vision, with its objectivity and stability, can replace human eyes in performing these measurements. In this paper, a measurement method for seedling morphological indicators based on Euclidean distance, Laplacian contraction, PointNet++, and 3D reconstruction is proposed. Firstly, multi-angle sequence images of 30 one-year-old P. massoniana seedlings were collected, distorted, and corrected to generate a sparse point cloud through the Structure-from-Motion (SFM) and dense point cloud through the Patch-Based Multiple View Stereo (PMVS). Secondly, a Dense Weighted Semantic Segmentation Model based on PointNet++ was designed, achieving effective segmentation of the P. massoniana seedling point clouds. Finally, a multi-iteration plane method based on Laplacian contraction was proposed. The new skeleton points were refined by minimizing the Euclidean distance, iteratively generating the optimal morphological skeleton, thus facilitating the extraction of morphological indicators. The experimental results demonstrated a good correlation between the machine vision-extracted morphological indicators (including plant height, ground diameter, and height-to-diameter ratio) and manually measured data. The improved PointNet++ model achieved an accuracy of 0.9448 on the training set. The accuracy and Mean Intersection over Union (MIoU) of the test set reached 0.9430 and 0.7872, respectively. These findings can provide reliable technical references for the accurate assessment of P. massoniana seedling quality and the promotion of digital forestry construction.
Why it matches plant phenotyping methods3D再構成、点群分割、骨格化を組み合わせ、苗木の形態形質を抽出する手法が研究の中心であり、手測定との相関による検証も行っている。
abstracta measurement method for seedling morphological indicators based on Euclidean distance, Laplacian contraction, PointNet++, and 3D reconstruction is proposed
Dense three-dimensional point clouds provide opportunities to retrieve detailed characteristics of plant organ-level phenotypic traits, which are helpful to better understand plant architecture leading to its improvements via new plant breeding approaches. In this study, a high-resolution terrestrial LiDAR was used to acquire point clouds of plants under field conditions, and a data processing pipeline was developed to detect plant main stalks and nodes, and then to extract two phenotypic traits including node number and main stalk length. The proposed method mainly consisted of three steps: first, extract skeletons from original point clouds using a Laplacian-based contraction algorithm; second, identify the main stalk by converting a plant skeleton point cloud to a graph; and third, detect nodes by finding the intersection between the main stalk and branches. Main stalk length was calculated by accumulating the distance between two adjacent points from the lowest to the highest point of the main stalk. Experimental results based on 26 plants showed that the proposed method could accurately measure plant main stalk length and detect nodes; the average R² and mean absolute percentage error were 0.94 and 4.3% for the main stalk length measurements and 0.7 and 5.1% for node counting, respectively, for point numbers between 80,000 and 150,000 for each plant. Three-dimensional point cloud-based high throughput phenotyping may expedite breeding technologies to improve crop production.
Why it matches plant phenotyping methods高解像度LiDARによる植物点群取得と、茎長・節数の抽出パイプライン開発および精度評価が研究の中心であり、明確な植物フェノタイピング手法である。
abstracta data processing pipeline was developed to detect plant main stalks and nodes, and then to extract two phenotypic traits including node number and main stalk length.
The application of 3D point cloud data in maize research is increasingly extensive. Currently, there are many approaches to acquiring three-dimensional (3D) point clouds of maize plants. However, automatic stem-leaf segmentation of maize shoots from 3D point clouds remains challenging, especially for new emerging leaves that are wrapped very closely together during the seedling stage. To address this issue, we propose an automatic segmentation method consisting of three steps: skeleton extraction, coarse segmentation based on the skeleton, and fine segmentation based on stem-leaf classification. The segmentation method was tested on 75 maize seedlings and compared with the manually obtained ground truth. The mean precision, mean recall, mean micro F1 score, and mean overall accuracy of our segmentation algorithm were 0.944, 0.956, 0.950 and 0.953, respectively. Using the segmentation results, two applications were also developed in this study, namely, phenotypic trait extraction and skeleton optimization. Six phenotypic parameters, namely, plant height, crown diameter, stem height and diameter, leaf width, and length, can be accurately and automatically measured. Furthermore, the values of R² for the six phenotypic traits were all above 0.92. We also propose a skeleton optimization method that can extract the skeletons of the upper leaves completely and clearly. The results indicate that the proposed algorithm can automatically and precisely segment not only the fully expanded leaves but also the new leaves wrapped closely together. The proposed approach can play an important role in further maize research and applications, such as genotype-to-phenotype study, geometric reconstruction, and dynamic growth animation. We released the source code and test data at the web site https://github.com/syau-miao/seg4maize.git.
Why it matches plant phenotyping methods3D点群からトウモロコシの茎葉を自動分割し、複数の表現型形質を抽出する手法を開発・検証しており、表現型取得が研究の中心です。
abstractwe propose an automatic segmentation method consisting of three steps: skeleton extraction, coarse segmentation based on the skeleton, and fine segmentation based on stem-leaf classification.
The phenotyping of Pinus massoniana seedlings is essential for breeding, vegetation protection, resource investigation, and so on. Few reports regarding estimating phenotypic parameters accurately in the seeding stage of Pinus massoniana plants using 3D point clouds exist. In this study, seedlings with heights of approximately 15-30 cm were taken as the research object, and an improved approach was proposed to automatically calculate five key parameters. The key procedure of our proposed method includes point cloud preprocessing, stem and leaf segmentation, and morphological trait extraction steps. In the skeletonization step, the cloud points were sliced in vertical and horizontal directions, gray value clustering was performed, the centroid of the slice was regarded as the skeleton point, and the alternative skeleton point of the main stem was determined by the DAG single source shortest path algorithm. Then, the skeleton points of the canopy in the alternative skeleton point were removed, and the skeleton point of the main stem was obtained. Last, the main stem skeleton point after linear interpolation was restored, while stem and leaf segmentation was achieved. Because of the leaf morphological characteristics of Pinus massoniana, its leaves are large and dense. Even using a high-precision industrial digital readout, it is impossible to obtain a 3D model of Pinus massoniana leaves. In this study, an improved algorithm based on density and projection is proposed to estimate the relevant parameters of Pinus massoniana leaves. Finally, five important phenotypic parameters, namely plant height, stem diameter, main stem length, regional leaf length, and total leaf number, are obtained from the skeleton and the point cloud after separation and reconstruction. The experimental results showed that there was a high correlation between the actual value from manual measurement and the predicted value from the algorithm output. The accuracies of the main stem diameter, main stem length, and leaf length were 93.5%, 95.7%, and 83.8%, respectively, which meet the requirements of real applications.
Why it matches plant phenotyping methods3D点群を用いてマツ苗の形態形質を自動抽出する手法を開発し、手動測定との相関で精度検証しており、フェノタイピング手法が中心である。
abstractan improved approach was proposed to automatically calculate five key parameters
Sudden changes in the morphological characteristics of trees are closely related to plant health, and automated phenotypic measurements can help improve the efficiency of plant health monitoring, and thus aid in the conservation of old and valuable tress. The irregular distribution of branches and the influence of the natural environment make it very difficult to monitor the status of branches in the field. In order to solve the problem of branch phenotype monitoring of tall and valuable plants in the field environment, this paper proposes an improved UNet model to achieve accurate extraction of trunk and branches. This paper also proposes an algorithm that can measure the branch length and inclination angle by using the main trunk and branches separated in the previous stage, finding the skeleton line of a single branch via digital image morphological processing and the Zhang-Suen thinning algorithm, obtaining the number of pixel points as the branch length, and then using Euclidean distance to fit a straight line to calculate the inclination angle of each branch. These were carried out in order to monitor the change in branch length and inclination angle and to determine whether plant branch breakage or external stress events had occurred. We evaluated the method on video images of Abies beshanzuensis , and the experimental results showed that the present algorithm has more excellent performance at 94.30% MIoU as compared with other target segmentation algorithms. The coefficient of determination (R 2 ) is higher than 0.89 for the calculation of the branch length and inclination angle. In summary, the algorithm proposed in this paper can effectively segment the branches of tall plants and measure their length and inclination angle in a field environment, thus providing an effective method to monitor the health of valuable plants.
Why it matches plant phenotyping methods改良UNetと骨格細線化により樹木の枝長・傾斜角を画像から抽出する手法を開発・評価しており、植物表現型取得が研究の中心である。
abstractthis paper proposes an improved UNet model to achieve accurate extraction of trunk and branches.
Field / plotRGB-D / ToFStem / branch2D/3D reconstructionSegmentationSkeletonization / topology
The shortage of agricultural labourers worldwide has left many groups unharvested and wasted, which motivates researchers worldwide to research fruit harvesting robots extensively. One of the major problems in fruit harvesting is to selectively avoid hard obstacles such as tree branches so that more optimal picking positions can be found and more fruits throughout the tree can be harvested. However, tree branches are often obscured in unstructured natural orchards and thus necessary branch reconstruction and recovery are required. The current branch reconstruction and recovery methods for harvesting robots focus on planar reconstruction with few occlusions while the existing 3D tree modelling methods are not optimised for harvesting purposes that require low computational cost and high localisation accuracy. This work presented a novel framework that reconstructs and recovers 3D obscured branches from planar images and depth maps captured by an RGB-D camera. The framework comprises three parts: branch segmentation using Unet++, branch reconstruction using Point2Skeleton and branch recovery using a novel obscured branch recovery (OBR) algorithm. Branch segmentation using Unet++ with InceptionV3 encoder shows the best overall result with IoU and F1-score of 0.6249 and 0.7692 respectively. OBR recovery algorithm achieves average reconstruction accuracy of 0.72. The mean error of the reconstructed total surface and obscured surface using OBR is 18.68 mm and 38.11 mm with a standard deviation of 14.3 mm and 32.64 mm. The result shows that this framework can effectively reconstruct spatial information of visible and obscured branches from a single view image which can potentially be utilised in harvesting robots.
Why it matches plant phenotyping methodsRGB-D画像から枝の分割・3D再構成・遮蔽部復元を行う技術が中心で、単なる収穫対象の位置検出を超えて植物器官の空間構造を定量化しているため、植物表現型計測法として含める。
abstractThis work presented a novel framework that reconstructs and recovers 3D obscured branches from planar images and depth maps captured by an RGB-D camera.
In plant phenotyping, accurate trait extraction from 3D point clouds of trees is still an open problem. For automatic modeling and trait extraction of tree organs such as blossoms and fruits, the semantically segmented point cloud of a tree and the tree skeleton are necessary. Therefore, we present CherryPicker, an automatic pipeline that reconstructs photo-metric point clouds of trees, performs semantic segmentation and extracts their topological structure in form of a skeleton. Our system combines several state-of-the-art algorithms to enable automatic processing for further usage in 3D-plant phenotyping applications. Within this pipeline, we present a method to automatically estimate the scale factor of a monocular reconstruction to overcome scale ambiguity and obtain metrically correct point clouds. Furthermore, we propose a semantic skeletonization algorithm build up on Laplacian-based contraction. We also show by weighting different tree organs semantically, our approach can effectively remove artifacts induced by occlusion and structural size variations. CherryPicker obtains high-quality topology reconstructions of cherry trees with precise details.
Why it matches plant phenotyping methodsサクランボ樹木の3D点群から器官の形態・トポロジーを抽出する自動フェノタイピング手法の開発が中心である。
abstractIn plant phenotyping, accurate trait extraction from 3D point clouds of trees is still an open problem.
Silique morphology is an important trait that determines the yield output of oilseed rape ( Brassica napus L .). Segmenting siliques and quantifying traits are challenging because of the complicated structure of an oilseed rape plant at the reproductive stage. This study aims to develop an accurate method in which a skeletonization algorithm was combined with the hierarchical segmentation (SHS) algorithm to separate siliques from the whole plant using 3-dimensional (3D) point clouds. We combined the L1-median skeleton with the random sample consensus for iteratively extracting skeleton points and optimized the skeleton based on information such as distance, angle, and direction from neighborhood points. Density-based spatial clustering of applications with noise and weighted unidirectional graph were used to achieve hierarchical segmentation of siliques. Using the SHS, we quantified the silique number (SN), silique length (SL), and silique volume (SV) automatically based on the geometric rules. The proposed method was tested with the oilseed rape plants at the mature stage grown in a greenhouse and field. We found that our method showed good performance in silique segmentation and phenotypic extraction with R 2 values of 0.922 and 0.934 for SN and total SL, respectively. Additionally, SN, total SL, and total SV had the statistical significance of correlations with the yield of a plant, with R values of 0.935, 0.916, and 0.897, respectively. Overall, the SHS algorithm is accurate, efficient, and robust for the segmentation of siliques and extraction of silique morphological parameters, which is promising for high-throughput silique phenotyping in oilseed rape breeding.
Why it matches plant phenotyping methods3D点群からシリクを分離し、形態形質を自動抽出する手法の開発と性能評価が研究の中心であるため。
abstractThis study aims to develop an accurate method in which a skeletonization algorithm was combined with the hierarchical segmentation (SHS) algorithm to separate siliques from the whole plant using 3-dimensional (3D) point clouds.
Leaf numbers are vital in estimating the yield of crops. Traditional manual leaf-counting is tedious, costly, and an enormous job. Recent convolutional neural network-based approaches achieve promising results for rosette plants. However, there is a lack of effective solutions to tackle leaf counting for monocot plants, such as sorghum and maize. The existing approaches often require substantial training datasets and annotations, thus incurring significant overheads for labeling. Moreover, these approaches can easily fail when leaf structures are occluded in images. To address these issues, we present a new deep neural network-based method that does not require any effort to label leaf structures explicitly and achieves superior performance even with severe leaf occlusions in images. Our method extracts leaf skeletons to gain more topological information and applies augmentation to enhance structural variety in the original images. Then, we feed the combination of original images, derived skeletons, and augmentations into a regression model, transferred from Inception-Resnet-V2, for leaf-counting. We find that leaf tips are important in our regression model through an input modification method and a Grad-CAM method. The superiority of the proposed method is validated via comparison with the existing approaches conducted on a similar dataset. The results show that our method does not only improve the accuracy of leaf-counting, with overlaps and occlusions, but also lower the training cost, with fewer annotations compared to the previous state-of-the-art approaches.The robustness of the proposed method against the noise effect is also verified by removing the environmental noises during the image preprocessing and reducing the effect of the noises introduced by skeletonization, with satisfactory outcomes.
Why it matches plant phenotyping methods単子葉植物の葉数という形態形質を画像から推定する深層学習手法を開発し、既存手法との比較検証と頑健性評価を行っており、表現型取得が研究の中心です。
abstractwe present a new deep neural network-based method
The bottleneck in plant breeding programs is to have cost-effective high-throughput phenotyping methodologies to efficiently describe the new lines and hybrids developed. In this paper, we propose a fully automatic approach to overcome not only the individual maize extraction but also the trait quantification challenge of structural components from unmanned aerial system (UAS) imagery. The experimental setup was carried out at the Indiana Corn and Soybean Innovation Center at the Agronomy Center for Research and Education (ACRE) in West Lafayette (IN, USA). On 27 July and 3 August 2021, two flights were performed over maize trials using a custom-designed UAS platform with a Sony Alpha ILCE-7R photogrammetric sensor onboard. RGB images were processed using a standard photogrammetric pipeline based on structure from motion (SfM) to obtain a final scaled 3D point cloud of the study field. Individual plants were extracted by, first, semantically segmenting the point cloud into ground and maize using 3D deep learning. Secondly, we employed a connected component algorithm to the maize end-members. Finally, once individual plants were accurately extracted, we robustly applied a Laplacian-based contraction skeleton algorithm to compute several structural component traits from each plant. The results from phenotypic traits such as height and number of leaves show a determination coefficient (R2) with on-field and digital measurements, respectively, better than 90%. Our test trial reveals the viability of extracting several phenotypic traits of individual maize using a skeletonization approach on the basis of a UAS imagery-based point cloud. As a limitation of the methodology proposed, we highlight that the lack of plant occlusions in the UAS images obtains a more complete point cloud of the plant, giving more accuracy in the extracted traits.
Why it matches plant phenotyping methodsUAS-SfM点群、3Dセグメンテーション、骨格化を統合し、個体トウモロコシの構造形質を自動抽出・検証する方法開発が中心である。
abstractwe propose a fully automatic approach to overcome not only the individual maize extraction but also the trait quantification challenge of structural components from unmanned aerial system (UAS) imagery.
The dormant pruning of jujube is a labor-intensive and time-consuming activity in the production and management of jujube orchards, which mainly depends on manual operation. Automatic pruning using robots could be a better way to solve the shortage of skilled labor and improve efficiency. In order to realize automatic pruning of jujube trees, a method of pruning point identification based on skeleton information is presented. This study used an RGB-D camera to collect multi-view information on jujube trees and built a complete point cloud information model of jujube trees. The space colonization algorithm acts on the global point cloud to generate the skeleton of jujube trees. The iterative relationship between skeleton points was represented by constructing a directed graph. The proposed skeleton analysis algorithm marked the skeleton as the trunk, the primary branches, and the lateral branches and identified the pruning points under the guidance of pruning rules. Finally, the visual model of the pruned jujube tree was established through the skeleton information. The results showed that the registration errors of individual jujube trees were less than 0.91 cm, and the average registration error was 0.66 cm, which provided a favorable database for skeleton extraction. The skeleton structure extracted by the space colonization algorithm had a high degree of coincidence with jujube trees, and the identified pruning points were all located on the primary branches of jujube trees. The study provides a method to identify the pruning points of jujube trees and successfully verifies the validity of the pruning points, which can provide a reference for the location of the pruning points and visual research basis for automatic pruning.
Why it matches plant phenotyping methodsRGB-D点群からナツメ樹の樹幹・一次枝・側枝の骨格を抽出し、剪定点を推定する画像・計算手法が研究の中心であり、植物形態・樹体構造の表現型取得として妥当。
abstractThis study used an RGB-D camera to collect multi-view information on jujube trees and built a complete point cloud information model of jujube trees.
Calculating the complex 3D traits of trees such as branch structure using drones/unmanned aerial vehicles (UAVs) with onboard RGB cameras is challenging because extracting branch skeletons from such image-generated sparse point clouds remains difficult. This paper proposes a skeleton extraction algorithm for the sparse point cloud generated by UAV RGB images with photogrammetry. We conducted a comparison experiment by flying a UAV from two altitudes (50 m and 20 m) above a university orchard with several fruit tree species and developed three metrics, namely the F1-score of bifurcation point (FBP), the F1-score of end point (FEP), and the Hausdorff distance (HD) to evaluate the performance of the proposed algorithm. The results show that the average values of FBP, FEP, and HD for the point cloud of fruit tree branches collected at 50 m altitude were 64.15%, 69.94%, and 0.0699, respectively, and those at 20 m were 83.24%, 84.66%, and 0.0474, respectively. This paper provides a branch skeleton extraction method for low-cost 3D digital management of orchards, which can effectively extract the main skeleton from the sparse fruit tree branch point cloud, can assist in analyzing the growth state of different types of fruit trees, and has certain practical application value in the management of orchards.
Why it matches plant phenotyping methods樹木の枝構造という植物形態形質を、UAV画像由来の点群から抽出するアルゴリズムを開発し、複数高度で性能評価しており、フェノタイピング手法が中心である。
abstractThis paper proposes a skeleton extraction algorithm for the sparse point cloud generated by UAV RGB images with photogrammetry.
The microtubule (MT) network is a highly dynamic subcellular structure playing an important role in the growth and development of plants, and it is able to respond to biotic and abiotic environmental signals. Recent literature shows that microtubules play a key role in the tolerance of plants to salt stress. For example, salt stress induces microtubules to undergo a process of depolymerization-repolymerization, which is necessary for Arabidopsis seedlings to survive under these conditions. However, the potential cellular and molecular mechanisms still need to be further studied. Here, we describe the protocol for salt treatment of Arabidopsis seedlings and imaging the MT array by confocal laser scanning microscopy. We also introduce the AnalyzeSkeleton (2D/3D) plugin for quantitative analysis of the microtubule array after salt stress. The application of such an image processing method can rapidly develop an appreciation of the role of microtubules in the salt stress response of plants.
Why it matches plant phenotyping methods植物の塩ストレス下における微小管配列を共焦点画像で取得し、画像解析プラグインで定量化するプロトコルが中心であり、植物の細胞状態を測定するフェノタイピング手法に該当する。
abstractHere, we describe the protocol for salt treatment of Arabidopsis seedlings and imaging the MT array by confocal laser scanning microscopy.
Generating accurate digital tree models from scanned environments is invaluable for forestry, agriculture, and other outdoor industries in tasks such as identifying fall hazards, estimating trees’ biomass and calculating traversability. Existing methods for tree reconstruction rely on sparse feature identification to segment a forest into individual trees and generate a branch structure graph, limiting their application to easily separable trees and uniform forests. However, the natural world is a messy place in which trees present with significant heterogeneity and are frequently encroached upon by the surrounding environment. We present a general method for extracting the branch structure of trees from point cloud data, which estimates the structure of trees by adapting the methods of structural topology optimisation to find the optimal material distribution to interpolate the input data. We present the results of this optimisation over a wide variety of scans, and discuss the benefits and drawbacks of this novel approach to tree structure reconstruction. Our method generates detailed and accurate tree structures, with a mean Surface Error (SE) of 15 cm over 13 diverse tree datasets.
Why it matches plant phenotyping methods点群データから樹木の枝構造を再構成する計算手法を開発し、多様なデータセットで精度評価しており、植物形態の取得が研究の中心です。
abstractWe present a general method for extracting the branch structure of trees from point cloud data
Current methods of root sampling typically only obtain small or incomplete sections of root systems and do not capture their true complexity. To facilitate the visualization and analysis of full-sized plant root systems in 3-dimensions, we developed customized mesocosm growth containers. While highly scalable, the design presented here uses an internal volume of 45 ft 3 (1.27 m 3 ), suitable for large crop and bioenergy grass root systems to grow largely unconstrained. Furthermore, they allow for the excavation and preservation of 3-dimensional root system architecture (RSA), and facilitate the collection of time-resolved subterranean environmental data. Sensor arrays monitoring matric potential, temperature and CO 2 levels are buried in a grid formation at various depths to assess environmental fluxes at regular intervals. Methods of 3D data visualization of fluxes were developed to allow for comparison with root system architectural traits. Following harvest, the recovered root system can be digitally reconstructed in 3D through photogrammetry, which is an inexpensive method requiring only an appropriate studio space and a digital camera. We developed a pipeline to extract features from the 3D point clouds, or from derived skeletons that include point cloud voxel number as a proxy for biomass, total root system length, volume, depth, convex hull volume and solidity as a function of depth. Ground-truthing these features with biomass measurements from manually dissected root systems showed a high correlation. We evaluated switchgrass, maize, and sorghum root systems to highlight the capability for species wide comparisons. We focused on two switchgrass ecotypes, upland (VS16) and lowland (WBC3), in identical environments to demonstrate widely different root system architectures that may be indicative of core differences in their rhizoeconomic foraging strategies. Finally, we imposed a strong physiological water stress and manipulated the growth medium to demonstrate whole root system plasticity in response to environmental stimuli. Hence, these new "3D Root Mesocosms" and accompanying computational analysis provides a new paradigm for study of mature crop systems and the environmental fluxes that shape them.
Why it matches plant phenotyping methods3Dルートメソコスム、フォトグラメトリ、点群解析による根系形態形質の取得・検証が研究の中心であり、植物フェノタイピング手法に該当する。
abstractTo facilitate the visualization and analysis of full-sized plant root systems in 3-dimensions, we developed customized mesocosm growth containers.
Reproduction assets foundThe paper's supplementary videos on figshare are photogrammetry-generated 3D point clouds of the paper's own root system phenotyping measurements (sorghum, maize, and switchgrass root systems, including stress-conditioned and sensor-flux coaligned visualizations), publicly downloadable. The OpenCV link is a generic, unDataset · publice, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2022.1041404/full#supplementary-material . Videos can be found for viewing and download at https://doi.org/10.6084/m9.figshare.21335898.v1 .
Supplementary Figure 1
Interpolation of 3-dimensional environmental sensor data.
Click here for additional data file.
Supplementary Figure 2
Time course of shoot morphological responses of switchgrass in different growth media.
Click here for additional data file.
Supplementary Figure 3
Manual post-process cleaning of Open asset ↗figshare · 10.6084/m9.figshare.21335898.v1lines:327-356Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 8 Sept 2026
BACKGROUND: High-throughput phenotyping is crucial for the genetic and molecular understanding of adaptive root system development. In recent years, imaging automata have been developed to acquire the root system architecture of many genotypes grown in Petri dishes to explore the Genetic x Environment (GxE) interaction. There is now an increasing interest in understanding the dynamics of the adaptive responses, such as the organ apparition or the growth rate. However, due to the increasing complexity of root architectures in development, the accurate description of the topology, geometry, and dynamics of a growing root system remains a challenge. RESULTS: We designed a high-throughput phenotyping method, combining an imaging device and an automatic analysis pipeline based on registration and topological tracking, capable of accurately describing the topology and geometry of observed root systems in 2D + t. The method was tested on a challenging Arabidopsis seedling dataset, including numerous root occlusions and crossovers. Static phenes are estimated with high accuracy ([Formula: see text] and [Formula: see text] for primary and second-order roots length, respectively). These performances are similar to state-of-the-art results obtained on root systems of equal or lower complexity. In addition, our pipeline estimates dynamic phenes accurately between two successive observations ([Formula: see text] for lateral root growth). CONCLUSIONS: We designed a novel method of root tracking that accurately and automatically measures both static and dynamic parameters of the root system architecture from a novel high-throughput root phenotyping platform. It has been used to characterise developing patterns of root systems grown under various environmental conditions. It provides a solid basis to explore the GxE interaction controlling the dynamics of root system architecture adaptive responses. In future work, our approach will be adapted to a wider range of imaging configurations and species.
Why it matches plant phenotyping methods根系の静的・動的形質を画像から自動抽出する高スループット手法と解析パイプラインを開発・検証しており、表現型取得法が研究の中心である。
abstractWe designed a high-throughput phenotyping method, combining an imaging device and an automatic analysis pipeline based on registration and topological tracking
Reproduction assets foundThe paper's root reconstruction/phenotyping pipeline (RootSystemTracker) is released as open-source code on GitHub with an ImageJ plugin documentation page; an example time-lapse movie of the reconstruction is also available on YouTube. No public dataset of the 1000 time-lapse images or RSML outputs is stated in thesupCode · publicThe architecture reconstruction pipeline is supplied as an ImageJ plugin with online documentation (Plugin page: https://imagej.net/plugins/rootsystemtracker [ 9 ]) and as open-source code on GitHub ( https://github.com/Rocsg/RootSystemTracker ).Open asset ↗Rocsg/RootSystemTrackerlines:208-277Plant phenotyping relevance match · UnverifiedOpenAlex · checked 13 Sept 2026
The reconstruction of tree branching structures is a longstanding problem in Computer Graphics which has been studied over several data sources, from photogrammetry point clouds to Terrestrial and Aerial Laser Imaging Detection and Ranging technology. However, most data sources present acquisition errors that make the reconstruction more challenging. Among them, the main challenge is the partial or complete occlusion of branch segments, thus leading to disconnected components whether the reconstruction is resolved using graph-based approaches. In this work, we propose a hybrid method based on radius-based search and Minimum Spanning Tree for the tree branching reconstruction by handling occlusion and disconnected branches. Furthermore, we simplify previous work evaluating the similarity between ground-truth and reconstructed skeletons. Using this approach, our method is proved to be more effective than the baseline methods, regarding reconstruction results and response time. Our method yields better results on the complete explored radii interval, though the improvement is especially significant on the Ground Sampling Distance In terms of latency, an outstanding performance is achieved in comparison with the baseline method.
Why it matches plant phenotyping methodsUAV-LiDARから樹木の枝分かれ構造を再構成する計算手法を開発し、ベースライン比較で性能評価しており、植物形態の取得が中心である。
abstractIn this work, we propose a hybrid method based on radius-based search and Minimum Spanning Tree for the tree branching reconstruction by handling occlusion and disconnected branches.
RootSkeletonization / topologyRoot system architecture
Branching patterns in plant roots are associated with complex traits such as stress-tolerance, yield, and the ability for carbon sequestration. The capability of the root system to branch allows the plant to search the soil for water and nutrients. For example, a reduction of higher order roots may determine how well a crop plant tolerates drought, whereas the ability to develop more higher order roots determines how well a crop plant tolerates a nutrient deficient soil. Measurements of traits such as rooting depth, root width or specific root length, however, often fail to capture the complex morphological arrangement of the root system. Therefore, a more rigorous analysis of root branching patterns is highly relevant as they are linked to the ability of plants to respond to abiotic stresses, such as drought and nutrient deficiency. Despite the need, it remains a challenge to extract information about branching patterns due to intersecting and overlapping roots in 2D and 3D imaging data. Such occlusion problems add ambiguity and outliers to root trait measurements. We present an algorithm to resolve such intersections in a globally optimal way based on simple heuristics such as straightness of roots - thus being dimension independent. This will enable quantitative analysis of how root branching patterns change in response to abiotic stress using shape descriptors. The possibility to computationally measure very dense branching structures with thousands of intersections will support the breeding of plants that withstand increasing areas of drought and nutrient deficiencies in the world.
Why it matches plant phenotyping methods根系画像における交差・重複を解消し、根分枝形態を定量化するアルゴリズムを開発しており、植物表現型の抽出が研究の中心です。
abstractWe present an algorithm to resolve such intersections in a globally optimal way based on simple heuristics such as straightness of roots - thus being dimension independent.
Root phenotype detection is key to cultivating seeds with excellent traits, and requires a complete root image. However, soil occlusion, uneven lighting, and other factors cause broken points and segments in the root image. To solve this problem, an anti-gravity stem-seeking (AGSS) root image restoration algorithm is proposed in this paper to repair root images and extract root phenotype information for different resistant maize seeds. First, the obtained root image was processed using uniform illumination, grayscale, binarization, and morphological filtering to separate it from the background. Subsequently, a root skeleton map was generated using a thinning algorithm for pixel-level image processing, and the Taproot junction G was obtained. Subsequently, root pixel coordinates were obtained by traversing the root skeleton map.All root-segment endpoint coordinates were obtained using the endpoint judgment rule and stored in the endpoint list.The lateral and primary root endpoints were separated based on the lateral root judgment rule, and stored in the side root and primary root endpoint lists, respectively. Subsequently, the primary root endpoints were processed and fitted in the order short to long using arbitrary-two-endpoint spacing until all breakpoints of the primary root were found.The coordinates of the top endpoints of each principal root were obtained. Finally, the top endpoint was connected to point G based on the Bezier curve-fitting method to achieve complete root repair. The proposed AGSS root image restoration algorithm was applied to detect the root systems of maize with different resistances and wheat to evaluate its performance against the standard dataset.The results indicated a detection accuracy of greater than 90% for root taproot length and diameter.It was also found that maize drought resistance was positively correlated with root length and diameter, but negatively with the lateral root number.In contrast, the waterlogging and salt resistance traits of maize were positively correlated with the number of lateral roots. In conclusion, the proposed AGSS root image restoration algorithm can quickly and effectively repair root images, is suitable for different resistance evaluations of maize seeds, and is conducive to the detection of root phenotypes. Compared with deep learning methods, this algorithm displays advantages of fast repair, low hardware platform requirements, and less requirement of training images. The algorithm is highly suitable for deploying in small embedded systems, with broad application prospects.
Why it matches plant phenotyping methodsトウモロコシ根画像の欠損修復と根形質抽出アルゴリズムを開発し、標準データセットで性能評価しているため、植物フェノタイピング手法が中心である。
abstractan anti-gravity stem-seeking (AGSS) root image restoration algorithm is proposed in this paper to repair root images and extract root phenotype information
RootMorphology / geometry measurementSegmentationSkeletonization / topologyRoot system architecture
The structures of roots play an essential role in plant growth, development, and stress responses. Minirhizotron imaging is one of the widely used approaches to capture and analyze root systems. After segmenting minirhizotron images, every individual root is separated from each other and the background. Root traits, like root lengths and diameter distributions, can provide information about the health of the plants. Current methods to analyze minirhizotron images usually rely on manually annotated labels and commercial software tools, which are time and labor-consuming. Unfortunately, these current methods usually generate a statistical analysis of the input image rather than the features of each root. In this work, we propose a pipeline to automatically use deep neural networks to segment roots from the background and then extract root features like lengths and diameter distributions from the individual segmented root. In detail, we first use a pre-trained U-Net to segment the roots in the minirhizotron images. Then, we separate each individual root with the help of connected component analysis. Finally, we extract the features like diameter distribution or root lengths of every individual root with morphological operations, like skeletonization. For evaluation, we conduct experiments on synthetic roots, which are made of strings and threads, and compare results against a benchmark root dataset (PRMI) of real switchgrass roots and compare the estimated results with the existing commercial software.
Why it matches plant phenotyping methodsミニライゾトロン画像から根を分割し、個々の根の長さや直径分布を自動抽出する画像解析パイプラインの開発であり、植物表現型取得が中心です。
abstractIn this work, we propose a pipeline to automatically use deep neural networks to segment roots from the background and then extract root features like lengths and diameter distributions from the individual segmented root.
Abstract Background Statistical analysis of root architectural parameters is necessary for development and exploration of root structure representations and their resulting anchorage properties. Three-dimensional (3D) models of orchard tree root systems, Lovell (from seed, prunus persica ), Marianna (from cutting, prunus cerasifera ), Myrobalan (from cutting, also prunus cerasifera ), that were extracted from the ground by vertical pullout are reconstructed through photogrammetry, and then skeletonized as nodes and root branch segments. Combined analyses of the 3D models and skeletonized models enable detailed examination of basic bulk properties and quantification of architectural parameters divided into simple root segment classifications— trunk root, main lateral root, and remaining roots. Results The patterns in branching and diameter distributions show significant difference between the trunk and main laterals versus the remaining lateral roots. In general, the branching angle decreases with branching order. The main lateral roots near the trunk show significant spreading while the lateral roots near the end tips grow roughly parallel to the parent root. For branch length, the roots branch more frequently near the trunk than further from the trunk. The root diameter decays at a higher rate near the trunk than in the remaining lateral roots, while the total cross-sectional area across a bifurcation node remains mostly conserved. The histograms of branching angle, and branch length and thickness gradient can be described using lognormal and exponential distributions, respectively. Conclusions Statistical measurements of root system architecture upon hierarchy provide a basis for representation and exploration of root system structure. This unique study presents data to characterize mechanically important structural roots, which will help link root architecture to the mechanical behaviors of root structures.
Why it matches plant phenotyping methodsフォトグラメトリによる根系3D再構成とスケルトン化を中心に、根系構造形質を定量化しており、植物フェノタイピング手法が研究の中核である。
abstractCombined analyses of the 3D models and skeletonized models enable detailed examination of basic bulk properties and quantification of architectural parameters
MaizeSorghumMesh / voxelPhotogrammetry / SfM / MVSLiDAR / point cloudRootMorphology / geometry measurement2D/3D reconstructionSkeletonization / topologyVisualization / data management
Current methods of root sampling typically only obtain small or incomplete sections of root systems and do not capture their true complexity. To facilitate the visualization and analysis of full-sized plant root systems in 3-dimensions, we developed customized mesocosm growth containers. While highly scalable, the design presented here uses an internal volume of 45 ft 3 (1.27 m 3 ), suitable for large crop and bioenergy grass root systems to grow largely unconstrained. Furthermore, they allow for the excavation and preservation of 3-dimensional RSA, and facilitate the collection of time-resolved subterranean environmental data. Sensor arrays monitoring matric potential, temperature and CO 2 levels are buried in a grid formation at various depths to assess environmental fluxes at regular intervals. Methods of 3D data visualization of fluxes were developed to allow for comparison with root system architectural traits. Following harvest, the recovered root system can be digitally reconstructed in 3D through photogrammetry, which is an inexpensive method requiring only an appropriate studio space and a digital camera. We developed a pipeline to extract features from the 3D point clouds, or from derived skeletons that include point cloud voxel number as a proxy for biomass, total root system length, volume, depth, convex hull volume and solidity as a function of depth. Ground-truthing these features with biomass measurements from manually dissected root systems showed a high correlation. We evaluated switchgrass, maize, and sorghum root systems to highlight the capability for species wide comparisons. We focused on two switchgrass ecotypes, upland (VS16) and lowland (WBC3), in identical environments to demonstrate widely different root system architectures that may be indicative of core differences in their rhizoeconomic foraging strategies. Finally, we imposed a strong physiological water stress and manipulated the growth medium to demonstrate whole root system plasticity in response to environmental stimuli. Hence, these new “3D Root Mesocosms” and accompanying computational analysis provides a new paradigm for study of mature crop systems and the environmental fluxes that shape them.
Why it matches plant phenotyping methods大型作物の根系を3Dで取得・再構築し、根系形質を抽出するメソッドとメソコスム基盤を開発・検証しており、植物フェノタイピング手法が中心である。
abstractTo facilitate the visualization and analysis of full-sized plant root systems in 3-dimensions, we developed customized mesocosm growth containers.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 8 Sept 2026
ArabidopsisLaboratory / benchtopRootMorphology / geometry measurementImage / point-cloud registrationSkeletonization / topologyGrowth / time-series analysisTrackingGrowth / development / phenologyRoot system architecture
Abstract Background High-throughput phenotyping is crucial for the genetic and molecular understanding of adaptive root system development. In recent years, imaging automata have been developed to acquire the root system architecture of many genotypes grown in Petri dishes to explore the Genetic x Environment (GxE) interaction. There is now an increasing interest in understanding the dynamics of the adaptive responses, such as the organ apparition or the growth rate. However, due to the increasing complexity of root architectures in development, the accurate description of the topology, geometry, and dynamics of a growing root system remains a challenge. Results We designed a high-throughput phenotyping method, combining an imaging device and an automatic analysis pipeline based on registration and topological tracking, capable of accurately describing the topology and geometry of observed root systems in 2D+t. The method was tested on a challenging Arabidopsis seedling dataset, including numerous root occlusions and crossovers. Static phenes are estimated with high accuracy ( R 2 = 0.996 and 0, 923 for primary and second-order roots length, respectively). These performances are similar to state-of-the-art results obtained on root systems of equal or lower complexity. In addition, our pipeline estimates dynamic phenes accurately between two successive observations ( R 2 = 0. 938 for lateral root growth). Conclusions We designed a novel method of root tracking that accurately and automatically measures both static and dynamic RSA parameters from a novel high-throughput root phenotyping platform. It has been used to characterize developing patterns of root systems grown under various environmental conditions. It provides a solid basis to explore the GxE interaction controlling the dynamics of root system architecture adaptive responses. In future work, our approach will be adapted to a wider range of imaging configurations and species.
Why it matches plant phenotyping methods根系の静的・動的形質を画像取得と自動解析で抽出する高スループット手法の設計・精度検証が研究の中心であるため。
abstractWe designed a high-throughput phenotyping method, combining an imaging device and an automatic analysis pipeline based on registration and topological tracking
Laboratory / benchtopMicroscopyX-ray / CTRoot2D/3D reconstructionSegmentationSkeletonization / topologyVisualization / data managementRoot system architecture
Land plants have two types of shoot-supporting systems, root system and rhizoid system, in vascular plants and bryophytes. However, since the evolutionary origin of the systems are different, how much they exploit common systems or distinct systems to architect their structures are largely unknown. To understand the regulatory mechanism how bryophytes architect rhizoid system responding to an environmental factor, such as gravity, and compare it with the root system of vascular plants, we have developed the methodology to visualize and quantitatively analyze the rhizoid system of the moss, Physcomitrium patens in 3D. The rhizoids having the diameter of 21.3 m on the average were visualized by refraction-contrast X-ray micro-CT using coherent X-ray optics available at synchrotron radiation facility SPring-8. Three types of shape (ring-shape, line, black circle) observed in tomographic slices of specimens embedded in paraffin were confirmed to be the rhizoids by optical and electron microscopy. Comprehensive automatic segmentation of the rhizoids which appeared in different three form types in tomograms was tested by a method using Canny edge detector or machine learning. Accuracy of output images was evaluated by comparing with the manually-segmented ground truth images using measures such as F1 score and IoU, revealing that the automatic segmentation using the machine learning was more effective than that using Canny edge detector. Thus, machine learning-based skeletonized 3D model revealed quite dense distribution of rhizoids, which was similar to root system architecture in vascular plants. We successfully visualized the moss rhizoid system in 3D for the first time.
Why it matches plant phenotyping methodsコケの根茎系を3D可視化・定量化するX線マイクロCTと自動セグメンテーション手法を開発し、教師データとの比較で精度検証しているため、植物表現型取得法が中心である。
abstractwe have developed the methodology to visualize and quantitatively analyze the rhizoid system of the moss, Physcomitrium patens in 3D
Physiological dynamics at plant level are essential but also challenging for precision agriculture applications linked to plant phenotyping. In this study, we explore not only the spatial dynamics of corn in field conditions but also their temporal analysis via skeleton reconstruction of individual plants as a shape descriptor. For this purpose, an optimized approach for high-throughput was developed by point cloud data derived from UAS imagery. The curve-skeleton extraction is calculated based on a constrained Laplacian smoothing algorithm. The experimental setup was performed at the Indiana Corn and Soybean Innovation Center at the Agronomy Center for Research and Education (ACRE) in West Lafayette, Indiana, USA. On July 27th and August 3rd of 2021, two flights were performed over a trial with more than 200 maize plants using a custom designed UAS platform with a Sony Alpha ILCE-7R photogrammetric sensor. RGB images were processed by a standard photogrammetric pipeline by Structure from Motion (SfM) to get a scaled 3D point cloud of the individual corn. Filtering techniques and labeling algorithms were joined together to reconstruct a robust and accurate skeleton of individual maize. Therefore, significant traits such as number, length, growth angle and elongation rate of leaves and stem can be easily extracted. Height variations computed from the skeleton at the two dates show a coefficient of correlation with on-field measurements better than 92%. Our experimental outcomes demonstrate the UAS-data’s ability to provide practical information to efficiently select phenotypes in plant breeding programs.
Why it matches plant phenotyping methodsUAS-SfM点群から個体の骨格を再構成し、葉・茎の形態形質を抽出する手法を開発・検証しており、植物表現型取得が研究の中心である。
abstractan optimized approach for high-throughput was developed by point cloud data derived from UAS imagery
Field / plotPhotogrammetry / SfM / MVSLiDAR / point cloudRootStem / branch2D/3D reconstructionSkeletonization / topologyArchitecture / morphology / geometry
Living architecture, changing in structure with annual growth, requires precise, regular characterisation. However, its geometric irregularity and topological complexity make documentation using traditional methods difficult and presents challenges in creating useful models for mechanical and physiological analyses. Two kinds of living architecture are examined: historic living root bridges grown in Meghalaya, India, and contemporary 'Baubotanik' structures designed and grown in Germany. These structures exhibit common features, in particular network-like structures of varying complexity that result from inosculations between shoots or roots. As an answer to this modelling challenge, we present the first extensive documentation of living architecture using photogrammetry and a subsequent skeleton extraction workflow that solves two problems related to the anastomoses and varying nearby elements specific to living architecture. Photogrammetry was used as a low cost method, supplying detailed point clouds of the structures' visible surfaces. A workflow based on voxel-thinning (using deletion templates and adjusted p-simplicity criteria) provides efficient, accurate skeletons. A volume reconstruction method is derived from the thinning process. The workflow is assessed on seven characteristics beneficial in representing living architecture in comparison with alternative skeleton extraction methods. The resulting models are ready for use in analytical tools, necessary for functional, responsible design.
Why it matches plant phenotyping methods植物の生体構造をフォトグラメトリで取得し、点群から骨格・体積を再構成するワークフロー自体が中心的な方法開発であり、植物構造の表現・解析に用いるため。
abstractwe present the first extensive documentation of living architecture using photogrammetry and a subsequent skeleton extraction workflow
Reproduction assets foundThe paper's Data availability statement explicitly provides public access to the authors' skeletonisation source code on GitHub and the photogrammetric point clouds (Freiburg pavilion, Ficus joint, Baubotanik joint) on the TUM media repository. Both are paper-specific, public, and actionable.Code · publicThe source code is available at: https://github.com/QiguanShu/skeleton-abstraction-of-point-cloud-by-voxel-thinningOpen asset ↗QiguanShu/skeleton-abstraction-of-point-cloud-by-voxel-thinninglines:141-214Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 8 Sept 2026
Selection for yield at high planting density has reshaped the leaf canopy of maize, improving photosynthetic productivity in high density settings. Further optimization of canopy architecture may be possible. However, measuring leaf angles, the widely studied component trait of leaf canopy architecture, by hand is a labor and time intensive process. Here, we use multiple, calibrated, 2D images to reconstruct the 3D geometry of individual sorghum plants using a voxel carving based algorithm. Automatic skeletonization and segmentation of these 3D geometries enable quantification of the angle of each leaf for each plant. The resulting measurements are both heritable and correlated with manually collected leaf angles. This automated and scaleable reconstruction approach was employed to measure leaf-by-leaf angles for a population of 366 sorghum plants at multiple time points, resulting in 971 successful reconstructions and 3,376 leaf angle measurements from individual leaves. A genome wide association study conducted using aggregated leaf angle data identified a known large effect leaf angle gene, several previously identified leaf angle QTL from a sorghum NAM population, and novel signals. Genome wide association studies conducted separately for three individual sorghum leaves identified a number of the same signals, a previously unreported signal shared across multiple leaves, and signals near the sorghum orthologs of two maize genes known to influence leaf angle. Automated measurement of individual leaves and mapping variants associated with leaf angle reduce the barriers to engineering ideal canopy architectures in sorghum and other grain crops.
Why it matches plant phenotyping methods3D画像再構成、骨格化、セグメンテーションによりソルガム個葉角度を自動定量する手法が研究の中心であり、手作業測定との検証と大規模適用も行っている。
abstractwe use multiple, calibrated, 2D images to reconstruct the 3D geometry of individual sorghum plants using a voxel carving based algorithm.
Reproduction assets foundThe paper's Data Availability statement provides three public, paper-specific assets: the voxel carving/skeletonization reconstruction code on GitHub, the raw RGB phenotyping images on Zenodo, and the phenotypic data, GWAS result files, and figure code on GitHub.Code · publicThe code for reconstruction and skeletonization is available at GitHub: https://github.com/cropsinsilico/SorghumVoxelCarving .Open asset ↗cropsinsilico/SorghumVoxelCarvinglines:351-493Code · publicThe phenotypic data, GWAS result files and code for main figures are available at GitHub: https://github.com/mtross2/Sorghum-3D-Reconstruction .Open asset ↗mtross2/Sorghum-3D-Reconstructionlines:351-493Plant phenotyping relevance match · UnverifiedCrossref · checked 13 Sept 2026
The vascular bundle is an important structural unit that determines the growth and properties of bamboo. A high-resolution X-ray microtomography (μCT) was used to observe and reconstruct a three-dimensional (3D) morphometry model of the vascular bundle of the Qiongzhuea tumidinoda node due to its advantages of quick, nondestructive, and accurate testing of plant internal structure. The results showed that the morphology of vascular bundles varied significantly in the axial direction. In the cross-section, the number of axial vascular bundles reached a maximum at the lower end of the sheath scar, and the minimum of it was at the middle of the diaphragm. The frequency of axial vascular bundles decreased from the lower end of the node to the nodal ridge, and subsequently increased until the upper end of the bamboo node. The proportion of parenchyma, fibers, and conducting tissue was 65.7%, 30.5%, and 3.8%, respectively. The conducting tissues were intertwined to form a complex 3D network structure, with a connectivity of 94.77%. The conducting tissue with the largest volume accounted for 60.26% of the total volume of the conducting tissue. The 3D-distribution pattern of the conducting tissue of the node and that of the fibers were similar, but their thickness changed in the opposite pattern. This study revealed the 3D morphometry of the conducting tissue and fibers of the bamboo node, the reconstruction of the skeleton made the morphology more intuitive. Quantitative indicators such as the 3D volume, proportion, and connectivity of each type of tissue was obtained, the bamboo node was enlarged mainly caused by the particularly developed fibers. This work laid the foundation for a better understanding of the mechanical properties and water transportation of bamboo and revealed the mystery of bamboo node shedding of Q. tumidinoda.
Why it matches plant phenotyping methods植物ノード内部の維管束・繊維の3D形態をμCTで取得・再構築し、体積・割合・接続性などの定量形質を抽出することが研究の中心であり、単なる routine measurement ではない。
abstractA high-resolution X-ray microtomography (μCT) was used to observe and reconstruct a three-dimensional (3D) morphometry model of the vascular bundle
Background 3D imaging, such as X-ray CT and MRI, has been widely deployed to study plant root structures. Many computational tools exist to extract coarse-grained features from 3D root images, such as total volume, root number and total root length. However, methods that can accurately and efficiently compute fine-grained root traits, such as root number and geometry at each hierarchy level, are still lacking. These traits would allow biologists to gain deeper insights into the root system architecture. Results We present TopoRoot, a high-throughput computational method that computes fine-grained architectural traits from 3D images of maize root crowns or root systems. These traits include the number, length, thickness, angle, tortuosity, and number of children for the roots at each level of the hierarchy. TopoRoot combines state-of-the-art algorithms in computer graphics, such as topological simplification and geometric skeletonization, with customized heuristics for robustly obtaining the branching structure and hierarchical information. TopoRoot is validated on both CT scans of excavated field-grown root crowns and simulated images of root systems, and in both cases, it was shown to improve the accuracy of traits over existing methods. TopoRoot runs within a few minutes on a desktop workstation for images at the resolution range of 400^3, with minimal need for human intervention in the form of setting three intensity thresholds per image. Conclusions TopoRoot improves the state-of-the-art methods in obtaining more accurate and comprehensive fine-grained traits of maize roots from 3D imaging. The automation and efficiency make TopoRoot suitable for batch processing on large numbers of root images. Our method is thus useful for phenomic studies aimed at finding the genetic basis behind root system architecture and the subsequent development of more productive crops.
Why it matches plant phenotyping methods3D画像からトウモロコシ根系の階層別形態形質を抽出する計算手法を開発し、既存法と精度比較・検証しており、植物表現型取得が中心です。
abstractWe present TopoRoot, a high-throughput computational method that computes fine-grained architectural traits from 3D images of maize root crowns or root systems.
Reproduction assets foundThe paper's authors publicly distribute the TopoRoot analysis software (C++ pipeline with GUI) together with the 45 X-ray CT scans of maize root crowns, per-image threshold values, and hand-measured nodal root counts in a GitHub repository. The synthetic OpenSimRoot images and ground-truth traits are only available on.Code · publicto a Euclidean distance field (e.g., using [ 29 ]). Fig. 12
Hierarchies of sorghum roots computed by TopoRoot, showing one tiller ( A ), two tillers ( B ), and four tillers ( C ). Hierarchy levels 0, 1, 2, 3 and 4 are colored dark blue, light blue, green, orange, and red.
Software availability
TopoRoot is available for free at: https://github.com/danzeng8/TopoRoot .
Included in the page are instructions to run the software, and details on the formats of the input and output files. Currently, the accepted inputs are either image slices (suffixed with.png) or.raw files, with a.dat accompanying the.raw file to specify the dimensions. The output consists of a skeleton, a hierarchy annotationOpen asset ↗https://github.com/danzeng8/TopoRootlines:2051-2060Dataset · public\usepackage{amsfonts}
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\begin{document}$$t_{low} ,t_{mid} ,t_{high}$$\end{document} t low , t mid , t high ) and hand measurements of nodal roots for each sample, are available in the TopoRoot Github repository: https://github.com/danzeng8/TopoRoot . The synthetic images of simulated roots and associated ground truth trait measurements are available from the corresponding author upon request.
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing inOpen asset ↗https://github.com/danzeng8/TopoRootlines:2061-2116Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Abstract Background Breakthrough imaging technologies may challenge the plant phenotyping bottleneck regarding marker-assisted breeding and genetic mapping. In this context, X-Ray CT (computed tomography) technology can accurately obtain the digital twin of root system architecture (RSA) but computational methods to quantify RSA traits and analyze their changes over time are limited. RSA traits extremely affect agricultural productivity. We develop a spatial–temporal root architectural modeling method based on 4D data from X-ray CT. This novel approach is optimized for high-throughput phenotyping considering the cost-effective time to process the data and the accuracy and robustness of the results. Significant root architectural traits, including root elongation rate, number, length, growth angle, height, diameter, branching map, and volume of axial and lateral roots are extracted from the model based on the digital twin. Our pipeline is divided into two major steps: (i) first, we compute the curve-skeleton based on a constrained Laplacian smoothing algorithm. This skeletal structure determines the registration of the roots over time; (ii) subsequently, the RSA is robustly modeled by a cylindrical fitting to spatially quantify several traits. The experiment was carried out at the Ag Alumni Seed Phenotyping Facility (AAPF) from Purdue University in West Lafayette (IN, USA). Results Roots from three samples of tomato plants at two different times and three samples of corn plants at three different times were scanned. Regarding the first step, the PCA analysis of the skeleton is able to accurately and robustly register temporal roots. From the second step, several traits were computed. Two of them were accurately validated using the root digital twin as a ground truth against the cylindrical model: number of branches (RRMSE better than 9%) and volume, reaching a coefficient of determination (R2) of 0.84 and a P < 0.001. Conclusions The experimental results support the viability of the developed methodology, being able to provide scalability to a comprehensive analysis in order to perform high throughput root phenotyping.
Why it matches plant phenotyping methodsX線CTの4Dデータから根系構造形質を抽出する計算手法を開発し、精度検証とハイスループット根系フェノタイピングへの適用可能性を示した研究であり、方法が中心的です。
abstractWe develop a spatial–temporal root architectural modeling method based on 4D data from X-ray CT.
Maize seedlings are grown in Hostun sand with two different gradings and two different densities. The root-soil system is imaged daily for the first 8 days of plant growth with X-ray computed tomography. Segmentation, skeletonisation and digital image correlation techniques are used to analyse the evolution of the root system architecture, the displacement fields and the local strain fields due to plant growth in the soil. It is found that root thickness and root length density do not depend on the initial soil configuration. However, the depth of the root tip is strongly influenced by the initial soil density, and the number of laterals is impacted by grain size, which controls pore size, capillary rise and thus root access to water. Consequently, shorter root axes are observed in denser sand and fewer second order roots are observed in coarser sands. In all soil configurations tested, root growth induces shear strain in the soil around the root system, and locally, in the vicinity of the first order roots axis. Root-induced shear is accompanied by dilative volumetric strain close to the root body. Further away, the soil experiences dilation in denser sand and compaction in looser sand. These results suggest that the increase of porosity close to the roots can be caused by a mix of shear strain and steric exclusion.
Why it matches plant phenotyping methodsX線CT画像にセグメンテーション、骨格化、デジタル画像相関を適用し、根系構造や根の形態・成長を定量化する手法が研究の中心であるため。
abstractThe root-soil system is imaged daily for the first 8 days of plant growth with X-ray computed tomography.
Physiological dynamics at plant level are essential but also challenging for precision agriculture applications linked to plant phenotyping. In this study, we explore not only the spatial dynamics of corn in field conditions but also their temporal analysis via skeleton reconstruction of individual plants as a shape descriptor. For this purpose, an optimized approach for high-throughput was developed by point cloud data derived from UAS imagery. The curve-skeleton extraction is calculated based on a constrained Laplacian smoothing algorithm. The experimental setup was performed at the Indiana Corn and Soybean Innovation Center at the Agronomy Center for Research and Education (ACRE) in West Lafayette, Indiana, USA. On July 27th and August 3rd of 2021, two flights were performed over a trial with more than 200 maize plants using a custom designed UAS platform with a Sony Alpha ILCE-7R photogrammetric sensor. RGB images were processed by a standard photogrammetric pipeline by Structure from Motion (SfM) to get a scaled 3D point cloud of the individual corn. Filtering techniques and labeling algorithms were joined together to reconstruct a robust and accurate skeleton of individual maize. Therefore, significant traits such as number, length, growth angle and elongation rate of leaves and stem can be easily extracted. Height variations computed from the skeleton at the two dates show a coefficient of correlation with on-field measurements better than 92%. Our experimental outcomes demonstrate the UAS-data’s ability to provide practical information to efficiently select phenotypes in plant breeding programs.
Why it matches plant phenotyping methodsUAS-SfM点群から個体の3D骨格を再構成し、葉・茎の形態形質を抽出する手法を開発・検証しており、植物フェノタイピング手法が中心である。
abstractan optimized approach for high-throughput was developed by point cloud data derived from UAS imagery
Modern plant phenotyping requires tools that are robust to noise and missing data, while being able to efficiently process large numbers of plants. Here, we studied the skeletonization of plant architectures from 3D point clouds, which is critical for many downstream tasks, including analyses of plant shape, morphology, and branching angles. Specifically, we developed an algorithm to improve skeletonization at branch points (forks) by leveraging the geometric properties of cylinders around branch points. We tested this algorithm on a diverse set of high-resolution 3D point clouds of tomato and tobacco plants, grown in five environments and across multiple developmental timepoints. Compared to existing methods for 3D skeletonization, our method efficiently and more accurately estimated branching angles even in areas with noisy, missing, or non-uniformly sampled data. Our method is also applicable to inorganic datasets, such as scans of industrial pipes or urban scenes containing networks of complex cylindrical shapes.
Why it matches plant phenotyping methods植物の3D点群から分枝構造を骨格化し、分枝角度を推定するアルゴリズムを開発・比較評価しており、植物表現型の抽出手法が研究の中心です。
abstractHere, we studied the skeletonization of plant architectures from 3D point clouds
Reproduction assets foundThe paper's Data Availability Statement explicitly states that data and code executable are publicly available at the authors' GitHub repository iziamtso/P3D, which is an allowed URL. This covers the paper-specific plant point cloud data and skeletonization analysis code.Code · publicData Availability Statement: Data and code executable are available at: https://github.com/iziamtso/P3D.Open asset ↗iziamtso/P3Dpdf-page:14 lines:1-59Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
Background 3D imaging, such as X-ray CT and MRI, has been widely deployed to study plant root structures. Many computational tools exist to extract coarse-grained features from 3D root images, such as total volume, root number and total root length. However, methods that can accurately and efficiently compute fine-grained root traits, such as root number and geometry at each hierarchy level, are still lacking. These traits would allow biologists to gain deeper insights into the root system architecture (RSA). Results We present TopoRoot, a high-throughput computational method that computes fine-grained architectural traits from 3D X-ray CT images of field-excavated maize root crowns. These traits include the number, length, thickness, angle, tortuosity, and number of children for the roots at each level of the hierarchy. TopoRoot combines state-of-the-art algorithms in computer graphics, such as topological simplification and geometric skeletonization, with customized heuristics for robustly obtaining the branching structure and hierarchical information. TopoRoot is validated on both real and simulated root images, and in both cases it was shown to improve the accuracy of traits over existing methods. We also demonstrate TopoRoot in differentiating a maize root mutant from its wild type segregant using fine-grained traits. TopoRoot runs within a few minutes on a desktop workstation for volumes at the resolution range of 400^3, without need for human intervention. Conclusions TopoRoot improves the state-of-the-art methods in obtaining more accurate and comprehensive fine-grained traits of maize roots from 3D CT images. The automation and efficiency makes TopoRoot suitable for batch processing on a large number of root images. Our method is thus useful for phenomic studies aimed at finding the genetic basis behind root system architecture and the subsequent development of more productive crops.
Why it matches plant phenotyping methodsX線CT画像からトウモロコシ根系の階層的形態形質を抽出する計算手法を開発し、実画像・シミュレーション画像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。
abstractWe present TopoRoot, a high-throughput computational method that computes fine-grained architectural traits from 3D X-ray CT images of field-excavated maize root crowns.
Reproduction assets foundThe paper's TopoRoot phenotyping software (C++ pipeline computing root hierarchy and fine-grained traits from X-ray CT volumes) and the datasets generated/analysed in the study (including the test dataset) are publicly released on the authors' GitHub repository.Code · publicduce a
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probability density field (e.g., deep learning). Since TopoRoot requires a gray-scale intensity
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volume with three thresholds (shape, kernel and neighborhood), a binary segmentation will first
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need to be converted into a Euclidean distance field.
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Software availability
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TopoRoot is available for free at: https://github.com/danzeng8/TopoRoot
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CC-BY 4.0 International license
available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprint
this version posted August 28, 2021.
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https://doi.org/10.1101/2021.08.24.457522
doi:
bOpen asset ↗danzeng8/TopoRootpdf-raw-page:37 lines:1-53Dataset · public39
CT: Computed Tomography
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Declarations
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Ethics approval and consent to participate
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Not applicable
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Consent for publication
727
Not applicable
728
Availability of data and materials
729
The datasets generated and analysed during the current study are available in the TopoRoot
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Github repository: https://github.com/danzeng8/TopoRoot
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Competing interests
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The authors declare that they have no competing interests.
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Funding
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This material is based upon work supported by the National Science Foundation under award
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numbers DBI-1759836, DBI-1759807, DBI-1759796, EF-1971728, CCF-1907612, CCF-
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2106672, and IOS-1638507. DZ is funded in part by aOpen asset ↗danzeng8/TopoRootpdf-raw-page:39 lines:1-45Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
RiceX-ray / CTRootMorphology / geometry measurementSkeletonization / topologyRoot system architecture
Background The root distribution in the soil is one of the elements that comprise the root system architecture (RSA). In monocots, RSA comprises radicle and crown roots, each of which can be basically represented by a single curve with lateral root branches or approximated using a polyline. Moreover, RSA vectorization (polyline conversion) is useful for RSA phenotyping. However, a robust software that can enable RSA vectorization while using noisy three-dimensional (3D) volumes is unavailable. Results We developed RSAtrace3D, which is a robust 3D RSA vectorization software for monocot RSA phenotyping. It manages the single root (radicle or crown root) as a polyline (a vector), and the set of the polylines represents the entire RSA. RSAtrace3D vectorizes root segments between the two ends of a single root. By utilizing several base points on the root, RSAtrace3D suits noisy images if it is difficult to vectorize it using only two end nodes of the root. Additionally, by employing a simple tracking algorithm that uses the center of gravity (COG) of the root voxels to determine the tracking direction, RSAtrace3D efficiently vectorizes the roots. Thus, RSAtrace3D represents the single root shape more precisely than straight lines or spline curves. As a case study, rice (Oryza sativa) RSA was vectorized from X-ray computed tomography (CT) images, and RSA traits were calculated. In addition, varietal differences in RSA traits were observed. The vector data were 32,000 times more compact than raw X-ray CT images. Therefore, this makes it easier to share data and perform re-analyses. For example, using data from previously conducted studies. For monocot plants, the vectorization and phenotyping algorithm are extendable and suitable for numerous applications. Conclusions RSAtrace3D is an RSA vectorization software for 3D RSA phenotyping for monocots. Owing to the high expandability of the RSA vectorization and phenotyping algorithm, RSAtrace3D can be applied not only to rice in X-ray CT images but also to other monocots in various 3D images. Since this software is written in Python language, it can be easily modified and will be extensively applied by researchers in this field.
Why it matches plant phenotyping methods3D X線CT画像からイネ科根系構造をベクトル化し、根系形態形質を算出するソフトウェアとアルゴリズムの開発が中心である。
abstractWe developed RSAtrace3D, which is a robust 3D RSA vectorization software for monocot RSA phenotyping.
Selection for yield at high planting density has reshaped the leaf canopy of maize, improving photosynthetic productivity in high density settings. Further optimization of canopy architecture may be possible. However, measuring leaf angles, the widely studied component trait of leaf canopy architecture, by hand is a labor and time intensive process. Here, we use multiple, calibrated, 2D images to reconstruct the 3D geometry of individual sorghum plants using a voxel carving based algorithm. Automatic skeletonization and segmentation of these 3D geometries enable quantification of the angle of each leaf for each plant. The resulting measurements are both heritable and correlated with manually collected leaf angles. This automated and scaleable reconstruction approach was employed to measure leaf-by-leaf angles for a population of 366 sorghum plants at multiple time points, resulting in 971 successful reconstructions and 3,376 leaf angle measurements from individual leaves. A genome wide association study conducted using aggregated leaf angle data identified a known large effect leaf angle gene, several previously identified leaf angle QTL from a sorghum NAM population, and novel signals. Genome wide association studies conducted separately for three individual sorghum leaves identified a number of the same signals, a previously unreported signal shared across multiple leaves, and signals near the sorghum orthologs of two maize genes known to influence leaf angle. Automated measurement of individual leaves and mapping variants associated with leaf angle reduce the barriers to engineering ideal canopy architectures in sorghum and other grain crops.
Why it matches plant phenotyping methods複数の較正2D画像から3D植物形状を再構成し、葉ごとの葉角度を自動抽出する手法が研究の中心であり、遺伝性・手動測定との相関による検証も行っている。
abstractwe use multiple, calibrated, 2D images to reconstruct the 3D geometry of individual sorghum plants using a voxel carving based algorithm.
Reproduction assets foundThe paper's Data and Code availability statement provides three paper-specific public assets: the voxel carving/skeletonization code (GitHub cropsinsilico/SorghumVoxelCarving), the raw sorghum images analyzed (Zenodo deposit 10.5281/zenodo.4426620), and the phenotypic data, GWAS result files, and figure code (GitHub mtCode · publicThe code for reconstruction and skeletonization is hosted on GitHub: https://github.com/cropsinsilico/
SorghumVoxelCarving.Open asset ↗pdf-page:9 lines:1-59Code · publicPhenotypic data, GWAS result files and code for main figures are located on GitHub:
https://github.com/mtross2/Sorghum-3D-ReconstructionOpen asset ↗mtross2/Sorghum-3D-Reconstructionpdf-page:9 lines:1-59Plant phenotyping relevance match · UnverifiedbioRxiv · Europe PMC · Crossref · checked 8 Sept 2026
Root2D/3D reconstructionSegmentationSkeletonization / topologyRoot system architecture
In this work we propose an extension to recent methods for the reconstruction of root architectures in 2-dimensions. Recent methods for the automatic root analysis have proposed deep learned segmentation of root images followed by path finding such as Dijkstras algorithm to reconstruct root topology. These approaches assume that roots are separate, and that a shortest path within the image foreground represents a reliable reconstruction of the underlying root structure. This approach is prone to error where roots grow in close proximity, with path finding algorithms prone to taking "short cuts" and overlapping much of the root material. Here we extend these methods to also consider root angle, allowing a more informed shortest path search that disambiguates roots growing close together. We adapt a CNN architecture to also predict the angle of root material at each foreground position, and utilise this additional information within shortest path searchers to improve root reconstruction. Our results show an improved ability to separate clustered roots.
Why it matches plant phenotyping methods根画像から根系構造を再構成するCNNと経路探索手法を開発しており、植物表現型の取得・抽出が研究の中心です。
abstractwe propose an extension to recent methods for the reconstruction of root architectures in 2-dimensions
Published30 Jun 2021The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciencesCited by 3 · OpenAlex ↗
MaizeTomatoX-ray / CTRootImage / point-cloud registrationSkeletonization / topologyRoot system architecture
Abstract. Breakthrough imaging technologies are a potential solution to the plant phenotyping bottleneck in marker-assisted breeding and genetic mapping. X-Ray CT (computed tomography) technology is able to acquire the digital twin of root system architecture (RSA), however, advances in computational methods to digitally model spatial disposition of root system networks are urgently required.We extracted the root skeleton of the digital twin based on 3D data from X-ray CT, which is optimized for high-throughput and robust results. Significant root architectural traits such as number, length, growth angle, elongation rate and branching map can be easily extracted from the skeleton. The curve-skeleton extraction is computed based on a constrained Laplacian smoothing algorithm. This skeletal structure drives the registration procedure in temporal series. The experiment was carried out at the Ag Alumni Seed Phenotyping Facility (AAPF) at Purdue University in West Lafayette (IN, USA). Three samples of tomato root at 2 different times and three samples of corn root at 3 different times were scanned. The skeleton is able to accurately match the shape of the RSA based on a visual inspection.The results based on a visual inspection confirm the feasibility of the proposed methodology, providing scalability to a comprehensive analysis to high throughput root phenotyping.
Why it matches plant phenotyping methodsX線CT画像から根系骨格を抽出し、根の形態形質を高スループットに推定する計算手法の開発が中心であるため。
abstractWe extracted the root skeleton of the digital twin based on 3D data from X-ray CT, which is optimized for high-throughput and robust results.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 9 Sept 2026
GrapevineRGB / grayscaleRoot2D/3D reconstructionSegmentationSkeletonization / topologyRoot system architecture
The root system plays an essential role in the development and physiology of the plant, as well as in its response to various stresses. However, it is often insufficiently studied, mainly because it is difficult to visualize. For grapevine, a plant of major economic interest, there is a growing need to study the root system, in particular to assess its resistance to biotic and abiotic stresses, understand the decline that may affect it, and identify new ecofriendly production systems. In this context, we have evaluated and compared three distinct growing methods (hydroponics, plane, and cylindric rhizotrons) in order to describe relevant architectural root traits of grapevine cuttings (mode of grapevine propagation), and also two 2D- (hydroponics and rhizotron) and one 3D- (neutron tomography) imaging techniques for visualization and quantification of roots. We observed that hydroponics tubes are a system easy to implement but do not allow the direct quantification of root traits over time, conversely to 2D imaging in rhizotron. We demonstrated that neutron tomography is relevant to quantify the root volume. We have also produced a new automated analysis method of digital photographs, adapted for identifying adventitious roots as a feature of root architecture in rhizotrons. This method integrates image segmentation, skeletonization, detection of adventitious root skeleton, and adventitious root reconstruction. Although this study was targeted to grapevine, most of the results obtained could be extended to other plants propagated by cuttings. Image analysis methods could also be adapted to characterization of the root system from seedlings.
Why it matches plant phenotyping methods根系形態の画像取得・定量化手法を比較評価し、画像解析による自動的な不定根検出・再構成法を開発しており、植物フェノタイピング手法が中心である。
abstractwe have evaluated and compared three distinct growing methods
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
Abstract BackgroundBreakthrough imaging technologies are a potential solution to address the plant phenotyping bottleneck regarding marker-assisted breeding and genetic mapping. X-Ray CT (computed tomography) technology is able to acquire the digital twin of root system architecture (RSA) but computational methods to quantify RSA traits and analyze their changes over time are limited. RSA traits extremely affect agricultural productivity. We develop a spatial-temporal root architectural modeling method based on 4D data from X-ray CT. This novel approach is optimized for high-throughput phenotyping considering the cost-effective time to process the data and the accuracy and robustness of the results. Significant root architectural traits, including root elongation rate, number, length, growth angle, height, diameter, branching map, and volume of axial and lateral roots are extracted from the model based on the digital twin. Our pipeline is divided into two major steps: (i) first, we compute the curve-skeleton based on a constrained Laplacian smoothing algorithm. This skeletal structure determines the registration of the roots over time; (ii) subsequently, the RSA is robustly modeled by a cylindrical fitting. The experiment was carried out at the Ag Alumni Seed Phenotyping Facility (AAPF) from Purdue University in West Lafayette (IN, USA). ResultsRoots from three samples of tomato plants at two different times and three samples of corn plants at three different times were scanned. Regarding the first step, the PCA analysis of the skeleton is able to accurately and robustly register temporal roots. From the second step, the volume from the cylindrical model was compared against the root digital twin, reaching a coefficient of determination (R2) of 0.84 and a P < 0.001. ConclusionsThe results confirm the feasibility of the proposed methodology, providing scalability to a comprehensive analysis to high throughput root phenotyping.
Why it matches plant phenotyping methodsX線CTの4Dデータから根系構造形質を抽出する計算手法を開発・検証しており、植物フェノタイピング手法が研究の中心です。
abstractWe develop a spatial-temporal root architectural modeling method based on 4D data from X-ray CT.
The cultivation of hybrid rice requires lower investment cost and fewer transplanted seedlings per hill to improve the grain yield by increasing the productive tillers. Therefore, for the precise transplanting of hybrid rice, it is crucial to accurately recognize machine-transplanted seedlings and detect seedlings per hill. In this study, an automatic approach based on machine vision technology was proposed for detecting seedlings per hill. For the problem of rice field background interference on machine-transplanted seedling extraction, rice seedling and paddy field pixels were obtained from rice field images to generate two patches. The distribution characteristics of colour component values of these two patches under different colour models (i.e., RGB, YCrCb, HSV, Lab, HLS and LUV) were analysed by adopting exploratory factor analysis. These analysis results showed that the Lab (L-factor of brightness, a-content of red or green, and b-content of yellow or blue) colour model outperformed other models in separating seedlings from the background. The preferred Lab colour model along with Otsu’s method were used to extract rice seedling information, and the skeleton of the seedling hill was extracted using the thinning algorithm to effectively characterize the morphological structure of single seedling hill. An algorithm for detecting endpoints of skeletonized seedling hills was proposed to represent leaf tips of seedlings as endpoints, and the relationship between ground truth counts and automatic counts of endpoints was positive with R² and root mean square error (RMSE) of 0.9105 and 0.7437, respectively. Combining the number of endpoints with the number of skeletons, a mathematical model was developed and used for detecting seedlings per hill of machine-transplanted hybrid rice. The overall detection accuracy of seedlings per hill of machine-transplanted hybrid rice was up to 93.5%. The processing time for detecting single seedling hill image was less than 50 ms. These results show that the proposed approach allowed for the effective, reliable and fast detection of seedlings per hill, which provides technical assistance for further precise adjustments of the machine-transplanted performance of hybrid rice.
Why it matches plant phenotyping methods画像処理と形態解析により、移植稲の1株当たり苗数という植物形質を自動推定する手法を開発・評価しており、フェノタイピング手法が研究の中心である。
abstractIn this study, an automatic approach based on machine vision technology was proposed for detecting seedlings per hill.
Plant leaf detection and segmentation are challenging tasks for in-situ plant image analysis. Here, a novel leaf detection scheme is proposed to detect individual leaves and accurately determine leaf shapes in natural scenes. A leaf skeleton-extraction method was developed by analysing local image features of skeleton pixels. Approximate positions of individual leaves were determined according to the main leaf skeleton. Sub-images containing only single target leaves were extracted from whole plant images according to position and size of the main skeleton. Accurate leaf analysis was conducted on the sub-images of individual leaves. Leaf direction was calculated by examining the structure of the main leaf skeleton. Joint segmentation by combining region and active shape model was presented to accurately elucidate leaf shape. Leaf detection was implemented using deep learning approach, Faster R–CNN. A plant leaf image dataset containing four types of leaf images of different complexity was built to evaluate detection algorithms. Plant leaves with occlusions and complex backgrounds were effectively detected and their shapes accurately determined. Detection accuracy of the proposed method was 81.10%–100%, and 86.75%–100% for Faster R–CNN. The method demonstrated a comparable detection ability to that of Faster R–CNN. Furthermore, the rates of success to determine leaf direction by our method ranged between 89.06% and 100%, while the average measurement difference was 1.29° compared with manual measurement. The accuracy of shape measurement was 75.95%–100% for all types of plant images. Therefore, this method is accurate and stable for precise leaf measurements in agricultural applications.
Why it matches plant phenotyping methods個葉の検出・形状・方向を画像から抽出する手法を開発し、データセット上で精度検証しており、植物表現型取得が中心である。
abstracta novel leaf detection scheme is proposed to detect individual leaves and accurately determine leaf shapes in natural scenes.
The cultivation of orchard meadows provides an ecological benefit for biodiversity, which is significantly higher than in intensively cultivated orchards. The goal of this research is to create a tree model to automatically determine possible pruning points for stand-alone trees within meadows. The algorithm which is presented here is capable of building a skeleton model based on a pre-segmented photogrammetric 3D point cloud. Good results were achieved in assigning the points to their leading branches and building a virtual tree model, reaching an overall accuracy of 95.19 %. This model provided the necessary information about the geometry of the tree for automated pruning.
Why it matches plant phenotyping methods3D点群から枝の骨格・樹体形状を推定する計算手法が中心で、単なる剪定対象の位置検出を超えて植物器官の構造形態を抽出している。
abstractThe algorithm which is presented here is capable of building a skeleton model based on a pre-segmented photogrammetric 3D point cloud.
Plant phenotyping is a central task in crop science and plant breeding. It involves measuring plant traits to describe the anatomy and physiology of plants and is used for deriving traits and evaluating plant performance. Traditional methods for phenotyping are often time-consuming operations involving substantial manual labor. The availability of 3D sensor data of plants obtained from laser scanners or modern depth cameras offers the potential to automate several of these phenotyping tasks. This automation can scale up the phenotyping measurements and evaluations that have to be performed to a larger number of plant samples and at a finer spatial and temporal resolution. In this paper, we investigate the problem of registering 3D point clouds of the plants over time and space. This means that we determine correspondences between point clouds of plants taken at different points in time and register them using a new, non-rigid registration approach. This approach has the potential to form the backbone for phenotyping applications aimed at tracking the traits of plants over time. The registration task involves finding data associations between measurements taken at different times while the plants grow and change their appearance, allowing 3D models taken at different points in time to be compared with each other. Registering plants over time is challenging due to its anisotropic growth, changing topology, and non-rigid motion in between the time of the measurements. Thus, we propose a novel approach that first extracts a compact representation of the plant in the form of a skeleton that encodes both topology and semantic information, and then use this skeletal structure to determine correspondences over time and drive the registration process. Through this approach, we can tackle the data association problem for the time-series point cloud data of plants effectively. We tested our approach on different datasets acquired over time and successfully registered the 3D plant point clouds recorded with a laser scanner. We demonstrate that our method allows for developing systems for automated temporal plant-trait analysis by tracking plant traits at an organ level.
Why it matches plant phenotyping methods植物の時系列3D点群を登録し、骨格表現に基づいて器官レベルの形質追跡を可能にする新規計算手法を開発・検証しており、フェノタイピング手法が中心である。
abstractIn this paper, we investigate the problem of registering 3D point clouds of the plants over time and space.
Reproduction assets foundThe paper's Data Availability Statement explicitly provides both the 4D plant point cloud datasets (maize and tomato laser-scanner time series used for the phenotyping/registration experiments) and the authors' implementation code, each with a public URL.Dataset · publicavailable at https://www.ipb.uni-bonn.de/data/4d- tems for automated temporal plant-trait analysis by tracking plant traits at an organ level.Open asset ↗pdf-page:1 lines:1-63Code · publicThe code for our approach is
available at https://github.com/PRBonn/4d_plant_
registration.Open asset ↗pdf-page:1 lines:1-63Plant phenotyping relevance match · UnverifiedCrossref · checked 13 Sept 2026
Published10 Jan 20212020 25th International Conference on Pattern Recognition (ICPR)Cited by 0 · OpenAlex ↗
MRI / PETRoot2D/3D reconstructionSegmentationSkeletonization / topologyRoot system architecture
Structural reconstruction of plant roots from MRI is challenging, because of low resolution and low signal-to-noise ratio of the 3D measurements which may lead to disconnectivities and wrongly connected roots. We propose a two-stage approach for this task. The first stage is based on semantic root vs. soil segmentation and finds lowest-cost paths from any root voxel to the shoot. The second stage takes the largest fully connected component generated in the first stage and uses 3D skeletonization to extract a graph structure. We evaluate our method on 22 MRI scans and compare to human expert reconstructions.
Why it matches plant phenotyping methodsMRI画像から植物根系を再構成し、セグメンテーションと3D骨格化で根構造を抽出する手法の開発・専門家比較検証が中心であるため。
abstractWe propose a two-stage approach for this task.
Tree skeleton could be useful to agronomy researchers because the skeleton describes the shape and topological structure of a tree. The phenomenon of organs’ mutual occlusion in fruit tree canopy is usually very serious, this should result in a large amount of data missing in directed laser scanning 3D point clouds from a fruit tree. However, traditional approaches can be ineffective and problematic in extracting the tree skeleton correctly when the tree point clouds contain occlusions and missing points. To overcome this limitation, we present a method for accurate and fast extracting the skeleton of fruit tree from laser scanner measured 3D point clouds. The proposed method selects the start point and endpoint of a branch from the point clouds by user’s manual interaction, then a backward searching is used to find a path from the 3D point cloud with a radius parameter as a restriction. The experimental results in several kinds of fruit trees demonstrate that our method can extract the skeleton of a leafy fruit tree with highly accuracy.
Why it matches plant phenotyping methods果樹の3D点群から樹体骨格という形態・構造形質を抽出する手法の開発が中心であり、遮蔽や欠損への対応と精度評価も行っている。
abstractwe present a method for accurate and fast extracting the skeleton of fruit tree from laser scanner measured 3D point clouds.
Skeleton extraction from 3D plant point cloud data is an essential prior for myriads of phenotyping studies. Although skeleton extraction from 3D shapes have been studied extensively in the computer vision and graphics literature, handling the case of plants is still an open problem. Drawbacks of the existing approaches include the zigzag structure of the skeleton, nonuniform density of skeleton points, lack of points in the areas having complex geometry structure, and most importantly the lack of biological relevance. With the aim to improve existing skeleton structures of state-of-the-art, we propose a stochastic framework which is supported by the biological structure of the original plant (we consider plants without any leaves). Initially we estimate the branching structure of the plant by the notion of β-splines to form a curve tree defined as a finite set of curves joined in a tree topology with certain level of smoothness. In the next phase, we force the discrete points in the curve tree to move toward the original point cloud by treating each point in the curve tree as a center of Gaussian, and points in the input cloud data as observations from the Gaussians. The task is to find the correct locations of the Gaussian centroids by maximizing a likelihood. The optimization technique is iterative and is based on the Expectation Maximization (EM) algorithm. The E-step estimates which Gaussian the observed point cloud was sampled from, and the M-step maximizes the negative log-likelihood that the observed points were sampled from the Gaussian Mixture Model (GMM) with respect to the model parameters. We experiment with several real world and synthetic datasets and demonstrate the robustness of the approach over the state-of-the-art.
Why it matches plant phenotyping methods植物3D点群から生物学的に妥当な骨格を抽出する計算手法を開発し、実データおよび合成データで既存手法と比較検証しているため、フェノタイピング手法が中心である。
abstractSkeleton extraction from 3D plant point cloud data is an essential prior for myriads of phenotyping studies.
Motivation Developing methods to efficiently analyze 3D point cloud data of plant architectures remain challenging for many phenotyping applications. Here, we describe a tool that tackles four core phenotyping tasks: classification of cloud points into stem and lamina points, graph skeletonization of the stem points, segmentation of individual lamina and whole leaf labeling. These four tasks are critical for numerous downstream phenotyping goals, such as quantifying plant biomass, performing morphological analyses of plant shapes and uncovering genotype to phenotype relationships. The Plant 3D tool provides an intuitive graphical user interface, a fast 3D rendering engine for visualizing plants with millions of cloud points, and several graph-theoretic and machine-learning algorithms for 3D architecture analyses. Availability and implementation P3D is open-source and implemented in C++. Source code and Windows installer are freely available at https://github.com/iziamtso/P3D/. Contact iziamtso@ucsd.edu or navlakha@cshl.edu. Supplementary information Supplementary data are available at Bioinformatics online.
Why it matches plant phenotyping methods植物3D点群から茎・葉の分類、骨格化、葉分割・ラベリングを行う、植物表現型解析用ツールの開発が中心である。
abstractHere, we describe a tool that tackles four core phenotyping tasks: classification of cloud points into stem and lamina points, graph skeletonization of the stem points, segmentation of individual lamina and whole leaf labeling.
The method proposed in this paper is part of the vision module of a garden robot capable of navigating towards rose bushes and clip them according to a set of pruning rules. The method is responsible for performing the segmentation of the branches and recovering their morphology in 3D. The obtained reconstruction allows the manipulator of the robot to select the candidate branches to be pruned. This method first obtains a stereo pair of images and calculates the disparity image using block matching and the segmentation of the branches using a Fully Convolutional Neuronal Network modified to return a map with the probability at the pixel level of the presence of a branch. A post-processing step combines the segmentation and the disparity in order to improve the results. Then, the skeleton of the plant and the branching structure are calculated, and finally, the 3D reconstruction is obtained. The proposed approach is evaluated with five different datasets, three of them compiled by the authors and two from the state of the art, including indoor and outdoor scenes with uncontrolled environments. The different steps of the proposed pipeline are evaluated and compared with other state-of-the-art methods, showing that the accuracy of the segmentation improves other methods for this task, even with variable lighting, and also that the skeletonization and the reconstruction processes obtain robust results.
Why it matches plant phenotyping methodsバラ植物の枝のセグメンテーション、骨格化、分枝構造および3D形態を抽出する画像解析手法が中心で、複数データセットによる評価・比較も行っている。単なる収穫対象の検出を超え、再利用可能な植物構造形質を推定している。
abstractThe method is responsible for performing the segmentation of the branches and recovering their morphology in 3D.
Skeleton extraction from 3D plant point cloud data is an essential prior for myriads of phenotyping studies. Although skeleton extraction from 3D shapes have been studied extensively in the computer vision and graphics literature, handling the case of plants is still an open problem. Drawbacks of the existing approaches include the zigzag structure of the skeleton, nonuniform density of skeleton points, lack of points in the areas having complex geometry structure, and most importantly the lack of biological relevance. With the aim to improve existing skeleton structures of state-of-the-art, we propose a stochastic framework which is supported by the biological structure of the original plant (we consider plants without any leaves). Initially we estimate the branching structure of the plant by the notion of β-splines to form a curve tree defined as a finite set of curves joined in a tree topology with certain level of smoothness. In the next phase, we force the discrete points in the curve tree to move towards the original point cloud by treating each point in the curve tree as a center of Gaussian, and points in the input cloud data as observations from the Gaussians. The task is to find the correct locations of the Gaussian centroids by maximizing a likelihood. The optimization technique is iterative and is based on the Expectation Maximization (EM) algorithm. The E-step estimates which Gaussian the observed point cloud was sampled from, and the M-step maximizes the negative log-likelihood that the observed points were sampled from the Gaussian Mixture Model (GMM) with respect to the model parameters. We experiment with several real world and synthetic datasets and demonstrate the robustness of the approach over the state-of-the-art.
Why it matches plant phenotyping methods植物3D点群から生物学的に妥当な骨格を抽出する計算手法を開発し、実データと合成データで既存手法と比較検証しているため、植物表現型取得・抽出法が中心である。
abstractSkeleton extraction from 3D plant point cloud data is an essential prior for myriads of phenotyping studies.
Reproduction assets foundThe paper's skeletonization experiments were implemented with the open-source PlantScan3D library, for which the authors provide a public GitHub URL (footnoted in the text and acknowledged as made available for public use). This is the computational tool used to produce the paper's plant point-cloud skeletonization andCode · publicthe open source implementation is available1 . NextOpen asset ↗pdf-page:3 lines:1-74Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Many studies have obtained high-quality plant point clouds but it remains difficult to extract the midrib from a leaf point cloud. In this study, a fast and accurate approach to the extraction of leaf midrib from point clouds was developed. The leaf was converted into its principal component analysis (PCA) coordinates to find the tip and base points of the leaf. A new search algorithm was proposed, which quickly searched for the approximate shortest curve between tip and base points on the leaf point cloud. The curve then was projected and fitted to eliminate the deviation between the curve and the leaf midrib. Two types of point cloud, generated using the structure-from-motion method and using a laser scanner, were obtained to verify our approach. As a result, the extracted curves were both in good agreement with the leaf midrib for the two kinds of point clouds. Compared with manual measurements of the leaf length, the root-mean-square error (RMSE) of the lengths of the two types of the extracted curve were 2.55 mm and 1.38 mm, respectively. The result shows that our method is robust and practical and may assist in the development of plant morphology measurements.
Why it matches plant phenotyping methods葉点群から葉脈を抽出し、葉長を測定する画像ベースの植物形態計測法を開発・検証しており、フェノタイピング手法が中心である。
abstractIn this study, a fast and accurate approach to the extraction of leaf midrib from point clouds was developed.
Developing automated methods to efficiently process large volumes of point cloud data remains a challenge for three-dimensional (3D) plant phenotyping applications. Here, we describe the development of machine learning methods to tackle three primary challenges in plant phenotyping: lamina/stem classification, lamina counting, and stem skeletonization. For classification, we assessed and validated the accuracy of our methods on a dataset of 54 3D shoot architectures, representing multiple growth conditions and developmental time points for two Solanaceous species, tomato ( Solanum lycopersicum cv 75 m82D ) and Nicotiana benthamiana Using deep learning, we classified lamina versus stems with 97.8% accuracy. Critically, we also demonstrated the robustness of our method to growth conditions and species that have not been trained on, which is important in practical applications but is often untested. For lamina counting, we developed an enhanced region-growing algorithm to reduce oversegmentation; this method achieved 86.6% accuracy, outperforming prior methods developed for this problem. Finally, for stem skeletonization, we developed an enhanced tip detection technique, which ran an order of magnitude faster and generated more precise skeleton architectures than prior methods. Overall, our improvements enable higher throughput and accurate extraction of phenotypic properties from 3D point cloud data.
Why it matches plant phenotyping methods3D点群から葉・茎の分類、葉数計測、茎骨格化を行う機械学習・画像解析手法の開発と検証が中心であり、植物形態形質の抽出に直接関わる。
abstractwe describe the development of machine learning methods to tackle three primary challenges in plant phenotyping: lamina/stem classification, lamina counting, and stem skeletonization.
ArabidopsisRapeseed / canolaWheatRootSegmentationSkeletonization / topologyRoot system architecture
We present a new image analysis approach that provides fully-automatic extraction of complex root system architectures from a range of plant species in varied imaging setups. Driven by modern deep-learning approaches, RootNav 2.0 replaces previously manual and semi-automatic feature extraction with an extremely deep multi-task Convolutional Neural Network architecture. The network has been designed to explicitly combine local pixel information with global scene information in order to accurately segment small root features across high-resolution images. In addition, the network simultaneously locates seeds, and first and second order root tips to drive a search algorithm seeking optimal paths throughout the image, extracting accurate architectures without user interaction. The proposed method is evaluated on images of wheat ( Triticum aestivum L.) from a seedling assay. The results are compared with semi-automatic analysis via the original RootNav tool, demonstrating comparable accuracy, with a 10-fold increase in speed. We then demonstrate the ability of the network to adapt to different plant species via transfer learning, offering similar accuracy when transferred to an Arabidopsis thaliana plate assay. We transfer for a final time to images of Brassica napus from a hydroponic assay, and still demonstrate good accuracy despite many fewer training images. The tool outputs root architectures in the widely accepted RSML standard, for which numerous analysis packages exist ( http://rootsystemml.github.io/ ), as well as segmentation masks compatible with other automated measurement tools.
Why it matches plant phenotyping methods植物根系形態を自動抽出する画像解析手法とツールの開発・検証が研究の中心であり、複数作物で精度・速度・転移性能を評価している。
abstractWe present a new image analysis approach that provides fully-automatic extraction of complex root system architectures from a range of plant species in varied imaging setups.
The ability to correlate morphological traits of plants with their genotypes plays an important role in plant phenomics research. However, measuring phenotypes manually is time-consuming, labor intensive, and prone to human errors. The 3D surface model of a plant can potentially provide an efficient and accurate way to digitize plant architecture. This study focused on the extraction of morphological traits at multiple developmental timepoints from sorghum plants grown under controlled conditions. A non-destructive 3D scanning system using a commodity depth camera was implemented to capture sequential images of a plant at different heights. To overcome the challenges of overlapping tillers, an algorithm was developed to first search for the stem in the merged point cloud data, and then the associated leaves. A 3D skeletonization algorithm was created by slicing the point cloud along the vertical direction, and then linking the connected Euclidean clusters between adjacent layers. Based on the structural clues of the sorghum plant, heuristic rules were implemented to separate overlapping tillers. Finally, each individual leaf was automatically segmented, and multiple parameters were obtained from the skeleton and the reconstructed point cloud including: plant height, stem diameter, leaf angle, and leaf surface area. The results showed high correlations between the manual measurements and the estimated values generated by the system. Statistical analyses between biomass and extracted traits revealed that stem volume was a promising predictor of shoot fresh weight and shoot dry weight, and the total leaf area was strongly correlated to shoot biomass at early stages.
Why it matches plant phenotyping methodsソルガムの3D点群から形態形質を自動抽出する手法を開発し、手動測定との相関で検証しており、フェノタイピング手法が中心である。
abstractA non-destructive 3D scanning system using a commodity depth camera was implemented to capture sequential images of a plant at different heights.
Accurate and high-throughput determination of plant morphological traits is essential for phenotyping studies. Nowadays, there are many approaches to acquire high-quality three-dimensional (3D) point clouds of plants. However, it is difficult to estimate phenotyping parameters accurately of the whole growth stages of maize plants using these 3D point clouds. In this paper, an accurate skeleton extraction approach was proposed to bridge the gap between 3D point cloud and phenotyping traits estimation of maize plants. The algorithm first uses point cloud clustering and color difference denoising to reduce the noise of the input point clouds. Next, the Laplacian contraction algorithm is applied to shrink the points. Then the key points representing the skeleton of the plant are selected through adaptive sampling, and neighboring points are connected to form a plant skeleton composed of semantic organs. Finally, deviation skeleton points to the input point cloud are calibrated by building a step forward local coordinate along the tangent direction of the original points. The proposed approach successfully generates accurately extracted skeleton from 3D point cloud and helps to estimate phenotyping parameters with high precision of maize plants. Experimental verification of the skeleton extraction process, tested using three cultivars and different growth stages maize, demonstrates that the extracted matches the input point cloud well. Compared with 3D digitizing data-derived morphological parameters, the NRMSE of leaf length, leaf inclination angle, leaf top length, leaf azimuthal angle, leaf growth height, and plant height, estimated using the extracted plant skeleton, are 5.27, 8.37, 5.12, 4.42, 1.53, and 0.83%, respectively, which could meet the needs of phenotyping analysis. The time required to process a single maize plant is below 100 s. The proposed approach may play an important role in further maize research and applications, such as genotype-to-phenotype study, geometric reconstruction, functional structural maize modeling, and dynamic growth animation.
Why it matches plant phenotyping methods3D点群からトウモロコシの骨格を抽出し、葉長・葉角度・草丈などの表現型形質を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。
abstractIn this paper, an accurate skeleton extraction approach was proposed to bridge the gap between 3D point cloud and phenotyping traits estimation of maize plants.
Maize (Zea mays L.) is one of the most economically important cereal crops. Though time-consuming and labour-intensive, manually measuring phenotypic traits in the field has been the common practice for maize breeding programs. This study presents a system for automated characterisation of several important plant architectural traits of maize plants under field conditions. An algorithm was developed to extract 3D plant skeletons from point cloud data acquired by side-viewing Time-of-Flight cameras. Plants were detected as 3D lines by Hough transform of the skeleton nodes. By analysing the graph structure of the skeletons with respect to the 3D lines, the point cloud was partitioned into plant instances with the stems and the leaves separated. Furthermore, plant height, plant orientation, leaf angle, and stem diameter were extracted for each plant. The image-derived estimates of traits were compared to manual measurements at multiple growth stages. Satisfactory accuracies in terms of mean absolute error (MAE) and coefficient of determination (R2) were achieved for plant height (before flowering: MAE 0.15 m, R2 0.96; after flowering: MAE 0.054 m, R2 0.83), leaf angle (MAE 2.8°, R2 0.83), and plant orientation (MAE 13°), except for stem diameter due to the limitations of the depth sensor. The results showed that the system was robust and accurate when the plants were imaged from only one side despite occlusions caused by leaves, and the method was applicable to maize plants from an early growth stage to full maturity.
Why it matches plant phenotyping methods圃場のToF 3D画像からトウモロコシの骨格を抽出し、複数の建築形質を自動推定する手法を開発・手動測定と比較検証しており、表現型取得が中心です。
abstractThis study presents a system for automated characterisation of several important plant architectural traits of maize plants under field conditions.
Due to the rising cost and decreasing availability of labor, manual picking is becoming an increasing challenge for apple growers. A targeted shake-and-catch apple harvesting technique is being developed at Washington State University to address this challenge. The performance and productivity of such a harvesting technique can be increased greatly if the shaking process is automated. The first step toward automated shaking is the detection and localization of branches in apple tree canopies. A branch detection method was developed in this work for apple trees trained in a formal, fruiting wall architecture using depth features and a Regions-Convolutional Neural Network (R-CNN). Microsoft Kinect v2 was used to acquire RGB images and pseudo-color images, as well as depth images in natural orchard environment. The R-CNN was composed of an improved AlexNet network and was trained to detect apple tree branches using integrated pseudo-color and depth images for improved detection accuracy. The average recall and accuracy from the Pseudo-Color Image and Depth (PCI-D) method were 92% and 86% respectively when the R-CNN confidence level of the pseudo-color image was 50%. For comparison, when using the Pseudo-Color Image (PCI) method (without depth images), these averages were only 86% and 81%, respectively. Furthermore, the average correlation coefficient (r) between the fitting curves for branch skeletons using the PCI-D method and the fitting curves for ground-truth images was 0.91—another indicator that the PCI-D method performs better than the PCI method. In addition, the average accuracy of branch detection increased with both the PCI method and PCI-D method, since the sensor was closer to the canopy. This study demonstrates the great potential for using depth features in branch detection and skeleton estimation to develop effective shake-and-catch apple harvesting machines for use in formally trained apple orchards.
Why it matches plant phenotyping methodsリンゴ樹の枝を深度画像とR-CNNで検出し、枝骨格を推定する手法の開発・比較検証が中心であり、単なる収穫対象の位置特定を超えて植物器官の構造形質を測定している。
abstractA branch detection method was developed in this work for apple trees trained in a formal, fruiting wall architecture using depth features and a Regions-Convolutional Neural Network (R-CNN).
Abstract. For the purpose of extracting productions of some specific branching plants effectively and realizing its 3D reconstruction, Terrestrial LiDAR data was used as extraction source of production, and a 3D reconstruction method based on Terrestrial LiDAR technologies combined with the L-system was proposed in this article. The topology structure of the plant architectures was extracted using the point cloud data of the target plant with space level segmentation mechanism. Subsequently, L-system productions were obtained and the structural parameters and production rules of branches, which fit the given plant, was generated. A three-dimensional simulation model of target plant was established combined with computer visualization algorithm finally. The results suggest that the method can effectively extract a given branching plant topology and describes its production, realizing the extraction of topology structure by the computer algorithm for given branching plant and also simplifying the extraction of branching plant productions which would be complex and time-consuming by L-system. It improves the degree of automation in the L-system extraction of productions of specific branching plants, providing a new way for the extraction of branching plant production rules.
Why it matches plant phenotyping methodsTerrestrial LiDARと計算アルゴリズムを用いて植物の分枝トポロジーと構造パラメータを抽出・3D再構成する手法が研究の中心であり、植物形態のフェノタイピングに該当する。
abstracta 3D reconstruction method based on Terrestrial LiDAR technologies combined with the L-system was proposed in this article
RootMorphology / geometry measurementSkeletonization / topologyRoot system architecture
Quantifying plant morphology is a very challenging task that requires methods able to capture the geometry and topology of plant organs at various spatial scales. Recently, the use of persistent homology as a mathematical framework to quantify plant morphology has been successfully demonstrated for leaves, shoots, and root systems. In this paper, we present a new data analysis pipeline implemented in the R package archiDART to analyse root system architectures using persistent homology. In addition, we also show that both geometric and topological descriptors are necessary to accurately compare root systems and assess their natural complexity.
Why it matches plant phenotyping methods植物根系の形態・トポロジーを定量化する解析パイプラインとRパッケージを開発しており、植物表現型の抽出手法が中心である。
abstractIn this paper, we present a new data analysis pipeline implemented in the R package archiDART to analyse root system architectures using persistent homology.
Reproduction assets foundThe paper's use-case data and R analysis code are publicly deposited on Zenodo (data/R codes for the use cases; archived archiDART 3.0 source; archiShiny app code), with live code on GitHub and a public web application. These directly reproduce the paper's root-system phenotyping and persistent homology analysis.Dataset · publicThe data and R codes used for the use cases presented in this manuscript are available:
https://doi.org/10.5281/zenodo.1117836Open asset ↗Zenodo · 10.5281/zenodo.1117836lines:223-267Code · publicSource code available from:
https://github.com/archidart/archidartOpen asset ↗GitHub · archidart/archidartlines:223-267Code · publicThe data and codes used to make the web application are available:
https://github.com/archidart/archishinyOpen asset ↗GitHub · archidart/archishinylines:223-267Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
ArabidopsisLaboratory / benchtopRootMorphology / geometry measurementSkeletonization / topologyRoot system architecture
Better understanding of root traits such as root angle and root gravitropism will be crucial for development of crops with improved resource use efficiency. This chapter describes a high-throughput, automated image analysis method to trace Arabidopsis (Arabidopsis thaliana) seedling roots grown on agar plates. The method combines a "particle-filtering algorithm with a graph-based method" to trace the center line of a root and can be adopted for the analysis of several root parameters such as length, curvature, and stimulus from original root traces.
Why it matches plant phenotyping methods根の画像から中心線を自動追跡し、長さ・曲率・根角度などの形態形質を高スループットに定量する画像解析法が中心である。
abstractThis chapter describes a high-throughput, automated image analysis method to trace Arabidopsis (Arabidopsis thaliana) seedling roots grown on agar plates.
The plant endoplasmic reticulum forms a network of tubules connected by three-way junctions or sheet-like cisternae. Although the network is three-dimensional, in many plant cells, it is constrained to a thin volume sandwiched between the vacuole and plasma membrane, effectively restricting it to a 2-D planar network. The structure of the network, and the morphology of the tubules and cisternae can be automatically extracted following intensity-independent edge-enhancement and various segmentation techniques to give an initial pixel-based skeleton, which is then converted to a graph representation. Collectively, this approach yields a wealth of quantitative metrics for ER structure and can be used to describe the effects of pharmacological treatments or genetic manipulation. The software is publicly available.
Why it matches plant phenotyping methods植物ERの画像から構造・形態を自動抽出し、定量指標を算出する画像解析手法とソフトウェアが中心であるため。
abstractThe structure of the network, and the morphology of the tubules and cisternae can be automatically extracted following intensity-independent edge-enhancement and various segmentation techniques
X-ray / CTRootMorphology / geometry measurementSkeletonization / topologyRoot system architecture
The root system of a plant is responsible for supplying it with essential nutrients. The plant's ability to explore the surrounding soil is largely determined by its root system architecture (RSA), which varies with both genetic and environmental conditions. X-ray micro computed tomography (µCT) is a powerful tool allowing the non-invasive study of the root system architecture of plants grown in natural soil environments, providing both 3D descriptions of root architecture and the ability to make multiple measurements over a period of time. Once volumetric µCT data is acquired, the root system must first be segmented from the surrounding soil environment and then described. Automated and semi-automated software tools can be used to extract roots from µCT images, but current methods for the recovery of RSA traits from the resulting volumetric descriptions are somewhat limited. This thesis presents a novel tool (RooTh) which, given a segmented µCT image, skeletonises the root system and quantifies global and local root traits with minimal user interaction. The computationally inexpensive method used takes advantage of curve-fitting and active contours to find the optimal skeleton and thus evaluate root traits ... (continues)
Why it matches plant phenotyping methodsµCT画像から根系構造を抽出・定量化するRooThツールの開発が研究の中心であり、植物形質計測法に該当する。
abstractThis thesis presents a novel tool (RooTh) which, given a segmented µCT image, skeletonises the root system and quantifies global and local root traits with minimal user interaction.
Introduction Great areas of the orchards in the world are dedicated to cultivation of the grapevine. Normally grape vineyards are pruned twice a year. Among the operations of grape production, winter pruning of the bushes is the only operation that still has not been fully mechanized while it is known as the most laborious jobs in the farm. Some of the grape producing countries use various mechanical machines to prune the grapevines, but in most cases, these machines do not have a good performance. Therefore intelligent pruning machine seems to be necessary in this regard and this intelligent pruning machines can reduce the labor required to prune the vineyards. It this study in was attempted to develop an algorithm that uses image processing techniques to identify which parts of the grapevine should be cut. Stereo vision technique was used to obtain three dimensional images from the bare bushes whose leaves were fallen in autumn. Stereo vision systems are used to determine the depth from two images taken at the same time but from slightly different viewpoints using two cameras. Each pair of images of a common scene is related by a popular geometry, and corresponding points in the images pairs are constrained to lie on pairs of conjugate popular lines. Materials and Methods Photos were taken from gardens of the Research Center for Agriculture and Natural Resources of Fars province, Iran. At first, the distance between the plants and the cameras should be determined. The distance between the plants and cameras can be obtained by using the stereo vision techniques. Therefore, this method was used in this paper by two pictures taken from each plant with the left and right cameras. The algorithm was written in MATLAB. To facilitate the segmentation of the branches from the rows at the back, a blue plate with dimensions of 2×2 m2 were used at the background. After invoking the images, branches were segmented from the background to produce the binary image. Then, the plant distance from the cameras was calculated by using the stereo vision. In next stage, the main trunk and one year old branches were identified and branches with thicknesses less than 7 mm were removed from the image. To omit these branches consecutive dilation and erosion operations were applied with circular structures having radii of 2 and 4 pixels. Then, based on the branch diameter, one-year-old branches were detected and pruned through considering the pruning parameters. The branches were pruned so that only three buds were left on them. For this aim, the branches should be pruned to have a length of 15 cm. To truncate the branches to 15 cm, the length of the main stem was measured for each of the branches, and branches with length less than 15 cm were omitted from the images. Then the main skeleton of grapevine was determined. Using this skeleton, the attaching points of the branches as well as attachment points to the trunk were identified. Distance between the branches was maintained. At the last step, the cutting points on the branches were determined by labeling the removed branches at each step. Results and Discussion The results indicated that the color components in the texture of the branches could not be used to identify one year old branches and evaluation results of algorithm showed that the proposed algorithm had acceptable performance and in all photos, one year old branches were correctly identified and pruning point of the grapevines were correctly marked. Also among 254 cut off-points extracted from 20 images, just 7 pruning points were misdiagnosed. These results revealed that the accuracy of the algorithm was about 96.8 percent. Conclusions Based on the reasonable achievement of the algorithm it can be concluded that it is possible to use machine vision routines to determine the most suitable cut off points for pruning robots. By an intelligent pruning robot, the one year old branches are diagnosed properly and the cut off points of the plants are determined. This can reduce the required labor to perform winter pruning in vineyards which subsequently reduces the time required and the costs needed for pruning the vineyards.
Why it matches plant phenotyping methodsブドウ樹の画像から枝構造、枝径、枝長、剪定位置を抽出するステレオビジョン・画像処理アルゴリズムを開発し、精度評価も行っており、植物表現型取得法が研究の中心である。
abstractIt this study in was attempted to develop an algorithm that uses image processing techniques to identify which parts of the grapevine should be cut.
Terrestrial Laser Scanning (TLS) processing for biomass mapping involves large data volumes, and often includes relatively slow 3D object fitting steps that increase the processing time. This study aimed to test new features that can speed up the overall processing time. A new type of 3D voxel is used, where the horizontal layers are parallel to the Digital Terrain Model. This voxel type allows procedures to extract tree diameters using just one layer, but still gives direct tree-height estimations. Layer intersection is used to emphasize the trunks as upright standing objects, which are detected in the spatially segmented intersection of the breast-height voxels and then extended upwards and downwards. The diameters were calculated by fitting elliptical cylinders to the laser points in the detected trunk segments. Non-trunk segments, used in sub-tree- structures, were found using the parent-child relationships between successive layers. The branches were reconstructed by skeletonizing each sub-tree branch, and the biomass was distributed statistically amongst the weighted skeletons. The procedure was applied to nine plots within the UK. The average correlation coefficients between reconstructed and directly measured tree diameters, heights and branches were R2 = 0.92, 0.97 and 0.59 compared to 0.91, 0.95, and 0.63 when cylindrical fitting was used. The average time to apply the method reduced from 5hrs:18mins per plot, for the conventional methods, to 2hrs:24mins when the same hardware and software libraries were used with the 3D voxels. These results indicate that this 3D voxel method can produce, much more quickly, results of a similar accuracy that would improve efficiency if applied to projects with large volume TLS datasets.
Why it matches plant phenotyping methodsTLSデータから樹木の直径・高さ・枝・バイオマスを再構成する3Dボクセル処理法を開発し、直接測定値との精度比較と処理時間評価を行っており、植物形質取得法が中心である。
abstractThis study aimed to test new features that can speed up the overall processing time.
In this paper, the method of spatial information extraction of tree branch was studied. The region matching method was used to get the disparity map of stereo image, extracted feature points combining with branch skeleton image by multi-segment approximation method, and calculated the spatial coordinates and the radius of branch feature points by using binocular stereo vision. Real-time model reconstruction for fruit tree has been researched on. Test proposed that each branch module was constructed by 12-prism in the coordinate origin, and then rotated twice and translated once to get correct posture, finally combined with other modules for the fruit tree model. Test has optimized extraction algorithm and matching algorithm of the branch region, improved matching rate, reduced matching errors, avoided matching confusion, accurately extracted branch spatial information and improved the success rate of robot path planning for obstacle avoidance.
Why it matches plant phenotyping methods双眼ステレオ画像から枝の空間座標・半径を抽出し、果樹の枝構造モデルを再構成する手法が中心であり、単なる収穫対象の検出を超えて植物器官の形態・構造を定量化している。
abstractthe method of spatial information extraction of tree branch was studied
Published23 Feb 2017The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciencesCited by 45 · OpenAlex ↗
Abstract. 3D models of tree geometry are important for numerous studies, such as for urban planning or agricultural studies. In climatology, tree models can be necessary for simulating the cooling effect of trees by estimating their evapotranspiration. The literature shows that the more accurate the 3D structure of a tree is, the more accurate microclimate models are. This is the reason why, since 2013, we have been developing an algorithm for the reconstruction of trees from terrestrial laser scanner (TLS) data, which we call TreeArchitecture. Meanwhile, new promising algorithms dedicated to tree reconstruction have emerged in the literature. In this paper, we assess the capacity of our algorithm and of two others -PlantScan3D and SimpleTree- to reconstruct the 3D structure of trees. The aim of this reconstruction is to be able to characterize the geometric complexity of trees, with different heights, sizes and shapes of branches. Based on a specific surveying workflow with a TLS, we have acquired dense point clouds of six different urban trees, with specific architectures, before reconstructing them with each algorithm. Finally, qualitative and quantitative assessments of the models are performed using reference tree reconstructions and field measurements. Based on this assessment, the advantages and the limits of every reconstruction algorithm are highlighted. Anyway, very satisfying results can be reached for 3D reconstructions of tree topology as well as of tree volume.
Why it matches plant phenotyping methodsTLS点群から樹木の3D構造・幾何形状を再構成する手法を開発・比較し、参照モデルと実測値で定量評価しているため、植物形質取得手法が中心である。
abstractwe have been developing an algorithm for the reconstruction of trees from terrestrial laser scanner (TLS) data, which we call TreeArchitecture.
Wheat field seedling density has a significant impact on the yield and quality of grains. Accurate and timely estimates of wheat field seedling density can guide cultivation to ensure high yield. The objective of this study was to develop an image-processing based, automatic counting method for wheat field seedlings, to investigate the principle of automatic counting of wheat emergence in the field, and to validate the newly developed method in various conditions. Digital images of the wheat fields at seedling stages with five cultivars and five seedling densities were acquired directly from above the fields. The wheat seedlings information was extracted from the background using excessive green and Otsu’s method. By analyzing the characteristic parameters of the overlapping regions (Overlapping region is a number of overlapping wheat seedlings in the image) of the fields, a chain code-based skeleton optimization method and corresponding equation were established for automatic counting of wheat seedlings in the overlapping regions. The results showed that the newly developed method can effectively count the number of wheat seedlings, with an average accuracy rate of 89.94 % and a highest accuracy rate of 99.21 %. The results also indicated that the accuracy of counting was not affected by different cultivars. However, the seedling density had significant impact on the counting accuracy (P 92 %) could be obtained. The study demonstrated that the newly developed method is reliable for automatic wheat seedlings counting, and also provides a theoretical perspective for automatic seedling counting in the wheat field.
Why it matches plant phenotyping methodsコムギ幼苗密度という植物形質を画像処理で自動抽出・計数する手法を開発し、複数条件で検証しており、フェノタイピング手法が研究の中心である。
abstractThe objective of this study was to develop an image-processing based, automatic counting method for wheat field seedlings
Published22 Jun 2016The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciencesCited by 6 · OpenAlex ↗
Field / plotLiDAR / point cloudLeafWhole plant / canopy / plot / field2D/3D reconstructionSkeletonization / topologyArchitecture / morphology / geometryWater status / transpiration
Abstract. In the 21st century, urban areas undergo specific climatic conditions like urban heat islands which frequency and intensity increase over the years. Towards the understanding and the monitoring of these conditions, vegetation effects on urban climate are studied. It appears that a natural phenomenon, the evapotranspiration of trees, generates a cooling effect in urban environment. In this work, a 3D microclimate model is used to quantify the evapotranspiration of trees in relation with their architecture, their physiology and the climate. These three characteristics are determined with field measurements and data processing. Based on point clouds acquired with terrestrial laser scanner (TLS), the 3D reconstruction of the tree wood architecture is performed. Then the 3D reconstruction of leaves is carried out from the 3D skeleton of vegetative shoots and allometric statistics. With the aim of extending the simulation on several trees simultaneously, it is necessary to apply the 3D reconstruction process on each tree individually. However, as well for the acquisition as for the processing, the 3D reconstruction approach is time consuming. Mobile laser scanners could provide point clouds in a faster way than static TLS, but this implies a lower point density. Also the processing time could be shortened, but under the assumption that a coarser 3D model is sufficient for the simulation. In this context, the criterion of level of details and accuracy of the tree 3D reconstructed model must be studied. In this paper first tests to assess their impact on the determination of the evapotranspiration are presented.
Why it matches plant phenotyping methodsTLS点群から樹木の3D構造を再構成する手法について、詳細度・精度が蒸散量推定に与える影響を評価しており、植物形態の取得・再構成が中心的な方法論的貢献である。
abstractBased on point clouds acquired with terrestrial laser scanner (TLS), the 3D reconstruction of the tree wood architecture is performed.
Published22 Jun 2016The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciencesCited by 4 · OpenAlex ↗
Field / plotLiDAR / point cloudLeafWhole plant / canopy / plot / field2D/3D reconstructionSkeletonization / topologyArchitecture / morphology / geometryWater status / transpiration
In the 21st century, urban areas undergo specific climatic conditions like urban heat islands which frequency and intensity increase over the years. Towards the understanding and the monitoring of these conditions, vegetation effects on urban climate are studied. It appears that a natural phenomenon, the evapotranspiration of trees, generates a cooling effect in urban environment. In this work, a 3D microclimate model is used to quantify the evapotranspiration of trees in relation with their architecture, their physiology and the climate. These three characteristics are determined with field measurements and data processing. Based on point clouds acquired with terrestrial laser scanner (TLS), the 3D reconstruction of the tree wood architecture is performed. Then the 3D reconstruction of leaves is carried out from the 3D skeleton of vegetative shoots and allometric statistics. With the aim of extending the simulation on several trees simultaneously, it is necessary to apply the 3D reconstruction process on each tree individually. However, as well for the acquisition as for the processing, the 3D reconstruction approach is time consuming. Mobile laser scanners could provide point clouds in a faster way than static TLS, but this implies a lower point density. Also the processing time could be shortened, but under the assumption that a coarser 3D model is sufficient for the simulation. In this context, the criterion of level of details and accuracy of the tree 3D reconstructed model must be studied. In this paper first tests to assess their impact on the determination of the evapotranspiration are presented.
Why it matches plant phenotyping methodsTLS点群による樹木の3D構造再構成について、モデルの詳細度・精度が蒸発散推定に与える影響を評価しており、植物形態の取得・再構成手法の検証が中心である。
abstractBased on point clouds acquired with terrestrial laser scanner (TLS), the 3D reconstruction of the tree wood architecture is performed.