Advanced crop monitoring inside greenhouses is becoming one of the primary objectives of research centers. High-performance sensors, such as LiDAR or stereo cameras, have traditionally been employed for this purpose, though these often have a high cost. This work proposes a Visual-SLAM system using a monocular camera, which is significantly more cost-effective and specifically tailored for agricultural applications, such as mapping tomato crops in a greenhouse. Tests were carried out on a real tomato bunch, located in the Agroconnect experimental greenhouse. A ROS 2 Humble node was developed to run on the robot in order to capture images of these crops, which were then stored for offline processing. To generate a 3D mapped model for the crop in the greenhouse, the GLOMAP mapper, based on Structure-From-Motion, was integrated with the Hierarchical Localization toolbox. This initial mapping is a foundation for future, more advanced algorithms to analyze growth patterns, and optimize agricultural management. The system leverages a hierarchical localization paradigm based on a coarse-to-fine strategy: it first performs global retrieval to generate location hypotheses, then combines local features within the identified candidate regions. The results show a correct identification of the tomato cluster, correctly characterising the tomato that is occluded and inaccessible by classical vision technologies. The reconstructed 3D model was further validated against manual ground-truth measurements of fruit size, centroid position, and orientation, confirming the geometric accuracy of the proposed low-cost monocular pipeline.
Why it matches plant phenotyping methods単なる収穫対象の位置検出ではなく、単眼Visual-SLAMと3D再構成を開発し、果実サイズ・重心位置・向きを実測値で検証しているため、植物器官形質の取得手法が中心である。
abstractThis work proposes a Visual-SLAM system using a monocular camera, which is significantly more cost-effective and specifically tailored for agricultural applications, such as mapping tomato crops in a greenhouse.
Near-infrared imagery is essential for vegetation monitoring, precision agriculture, and environmental remote sensing, but multispectral UAV systems remain significantly more expensive and less accessible than conventional RGB imaging platforms. This study presents a lightweight artificial intelligence framework for reconstructing the NIR spectral band exclusively from the red spectral band acquired by a UAV. The proposed methodology formulates the reconstruction task as a pixel-wise nonlinear regression problem and employs a compact multilayer perceptron (MLP) containing only 609 trainable parameters, without exploiting spatial neighborhood information. The framework was developed and evaluated using 280 synchronized multispectral UAV image sets acquired with a DJI Phantom 4 Multispectral platform over a heterogeneous agricultural landscape in the Republic of Moldova. Of these, 252 image sets were used for model development, and 28 were reserved as a held-out within-mission test subset. Quantitative evaluation on a held-out test dataset from the same acquisition mission yielded a mean squared error of 0.010329, a root mean squared error of 0.101632, a mean absolute error of 0.079883, a coefficient of determination of 0.253383, and a Pearson correlation coefficient of 0.683637 between measured and reconstructed normalized NIR digital intensities. The results indicate that the model captures part of the red–NIR relationship under the evaluated acquisition conditions; however, the moderate coefficient of determination suggests that the reconstructed values are an approximation rather than a replacement for measured NIR observations. An illustrative NDVI-based assessment showed that broad spatial vegetation patterns remained identifiable. Rather than introducing a new neural network architecture, this work establishes a compact empirical baseline to investigate the practical performance and limitations of pixel-wise NIR reconstruction from a single red-band value with minimal model complexity.
Why it matches plant phenotyping methodsUAV画像からNIRを再構成し、NDVIを含む植生状態の推定に用いる計算・センシング手法が研究の中心で、独立データによる技術評価も行っている。
abstractThis study presents a lightweight artificial intelligence framework for reconstructing the NIR spectral band exclusively from the red spectral band acquired by a UAV.
Occlusion is a major factor limiting accurate three-dimensional (3D) wheat phenotyping. In natural growth conditions, overlapping spikes, leaves, and stems often make only partial target regions visible in single-view images, hindering complete and reliable 3D reconstruction. To address this problem, this study proposes an amodal completion-assisted, sequential framework for single-view 3D reconstruction of occluded wheat. The framework first uses visible prompts to recover the complete appearance and structural cues of occluded targets, and then feeds the completed images into single-view 3D reconstruction models to generate complete 3D structures. We construct the MMWO (Multi-view Multi-instance Wheat Occlusion) dataset from MMW, which is captured under controlled indoor scenarios, by synthesizing diverse occlusion samples through organ-level cutouts, random geometric transformations, and region-constrained pasting, with annotations including visible masks, occlusion masks, and complete target images. Six representative reconstruction methods, including Direct3D, Real3D, SF3D, Spar3D, TRELLIS.2, and Hunyuan3D, are systematically evaluated. Hunyuan3D achieves the best geometric performance, with the lowest mean CD- \(L_1\) and CD- \(L_2\) values of 0.1286 and 0.0536, and the highest mean F-score of 0.5668. SF3D achieves the best rendering quality in terms of PSNR, SSIM, and LPIPS. In addition, Pix2Gestalt completion reduces the estimation errors of spike length, width, and area from 9.31%, 10.89%, and 32.23% to 4.64%, 9.70%, and 9.45%, respectively. These results demonstrate that amodal completion can effectively alleviate occlusion-induced information loss and provide more complete structural priors for single-view 3D wheat reconstruction. This study offers a feasible solution for 3D wheat phenotyping under occlusion and provides a systematic reference for applying 3D generative models to agricultural phenotyping.
Why it matches plant phenotyping methods遮蔽下の単一画像から小麦器官を3D再構成し、形質推定精度を改善する手法を開発・比較検証しており、植物フェノタイピング手法が中心である。
abstractTo address this problem, this study proposes an amodal completion-assisted, sequential framework for single-view 3D reconstruction of occluded wheat.
High-throughput phenotyping of lettuce seedlings is highly prone to background confusion because the seedlings are small, have weak textural features, and exhibit spectral reflectance similar to that of the substrate. Traditional single-visual-modality approaches struggle to achieve reliable structural and physiological characterization simultaneously under the repetitive backgrounds and dense arrangements typical of greenhouse tray cultivation. To address these challenges, we establish a multimodal 3D phenotyping framework tailored for controlled agriculture environments, enabling the quantification of structural and physiological characteristics of lettuce seedlings. This framework is based on an unmanned ground vehicle (UGV) platform integrating a RGBD camera and a quad-band multispectral sensor which are rigidly coupled and synchronously triggered. An alignment module based on established feature matching algorithm is introduced to register the misalignment between source multispectral and RGBD images. Subsequently, we design a novel dual-backbone instance segmentation network, MS-SegNet, to enhance segmentation accuracy by hierarchically fusing geometric information with multispectral features. A robust 3D metric pose estimation pipeline, incorporating standard SfM initialization, scale recovery, and generalized ICP refinement, is constructed to generate 3D point clouds with spectral attributes and semantic labels. Finally, key structural and physiological phenotype parameters of each seedling are calculated based on the 3D semantic multispectral point clouds. Experiments demonstrate that MS-SegNet achieves significant advantages in instance segmentation of lettuce seedlings with mAP@50:95 = 0.854. The metric 3D pose estimation pipeline exhibits reliable performance under complex controlled conditions. The quality of the 3D reconstructions is indirectly validated through downstream structural trait extraction. The estimated seedling height and crown width show high correlation with manual measurements, achieving R 2 values of 0.8379 and 0.918, and RMSE values of 10.94 mm and 11.56 mm, respectively. Overall, by systematically integrating these adapted components with the novel segmentation architecture, this framework achieves stable performance improvements in 3D reconstruction, instance segmentation, and phenotypic analysis under greenhouse conditions. It provides a scalable, integrated technical solution for non-destructive, high-throughput phenotyping of crop seedlings in controlled environments.
Why it matches plant phenotyping methodsRGBD・マルチスペクトル・UGVを統合した3Dフェノタイピング基盤を開発し、分割・再構成・構造／生理形質抽出を検証しており、フェノタイピング手法が研究の中心である。
abstractwe establish a multimodal 3D phenotyping framework tailored for controlled agriculture environments, enabling the quantification of structural and physiological characteristics of lettuce seedlings.
UAV-based multi-view reconstruction is an important approach for high-precision, non-destructive 3D crop phenotyping. However, in greenhouse environments, UAV image acquisition is often restricted to sparse viewpoints because of UAV-induced airflow disturbances and the structural complexity of the greenhouse, which severely hinders accurate 3D phenotyping. To address this challenge, this study develops a task-driven phenotyping framework for constrained UAV viewpoints in greenhouse environments, integrating a vision-triggered flight planning strategy with an improved sparse-view 3DGS pipeline, termed SparseBerry-3DGS, for multi-view image acquisition and 3D phenotyping of greenhouse strawberries in GNSS-denied environments. Specifically, 3D Gaussian Splatting (3DGS) is improved by incorporating flow-guided initialization, depth supervision, and an adaptive pruning strategy, which effectively alleviate geometric collapse and floating artifacts under sparse-view conditions. Furthermore, sequential semantic masks generated by SAM2 are utilized to guide the segmentation of strawberry point clouds, thereby reducing background interference and segmentation errors. Experimental results show that the vision-triggered flight strategy enables stable capture of 16 surrounding images for each target fruit. Under sparse-view conditions, SparseBerry-3DGS improves reconstruction stability, with the average peak signal-to-noise ratio (PSNR) reaching 18.25 dB, corresponding to an 18% improvement. The SAM2-based segmentation module achieves high accuracy, with the mean intersection over union (mIoU) above 0.95. Geometric evaluation based on strawberry longitudinal diameter yielded an of 0.88, supporting the accuracy of fruit-scale geometric reconstruction. For weight estimation, five-fold cross-validation yielded an of 0.90 and an RMSE of 3.62 g, showing better predictive performance than models based on 2D projected area and standard 3DGS point clouds. This study provides a new approach for high-throughput, non-invasive digital crop phenotyping in greenhouse horticulture.
Why it matches plant phenotyping methods温室イチゴの3D形状再構成と重量推定を目的に、制約視点UAV撮影、SparseBerry-3DGS再構成、点群セグメンテーションを統合した表現型取得手法を開発・検証しており、方法が研究の中心である。
abstractthis study develops a task-driven phenotyping framework for constrained UAV viewpoints in greenhouse environments
Salt stress can markedly alter seedling architecture, creating a need for non-destructive three-dimensional (3D) phenotyping methods capable of resolving fine plant structures. However, organ-level segmentation of plant point clouds remains challenging because of leaf overlap, slender stems, ambiguous stem–leaf boundaries, and severe class imbalance. In this study, we developed PTV-SegCo, a task-adapted Point Transformer model for organ segmentation and structural phenotyping of coriander seedlings under salt stress. PTV-SegCo integrates efficient channel attention, gated shallow–deep feature fusion, and a combined cross-entropy–Dice loss to improve representation of fine and minority organ structures. The dataset comprised 60 manually annotated 3D point-cloud samples from 12 cultivation trays repeatedly observed over five acquisition dates under six NaCl concentrations (0, 50, 100, 150, 200, and 250 mmol L −1 ). Because the earliest acquisition represented a particularly challenging developmental stage, these 12 samples were used as a fixed early-stage model-selection set, while samples from the remaining four dates were organized into four date-blocked training–validation configurations. Under this internal model-development protocol, PTV-SegCo achieved mean mAcc and mIoU values of 93.05% and 89.05%, respectively, and showed numerically higher performance than its direct backbone PTV-Seg50. These values should be interpreted as internal comparative results rather than as an unbiased estimate of generalization to unseen cultivation trays, and the present results should not be interpreted as establishing the broad competitiveness of PTV-SegCo against other point-based, convolution-based, graph-based, transformer-based, or plant-specific segmentation architectures. After semantic segmentation, reconstructed scenes were metrically calibrated using the known cultivation-tray dimensions, followed by individual-plant separation and quality control. Four reconstruction-derived structural descriptors—plant height, projected area, voxel occupancy volume, and leaf point ratio—were extracted to characterize temporal structural variation under different NaCl treatments. For treatment-level inference, individual-plant measurements were aggregated within each cultivation tray at each acquisition time, with the cultivation tray treated as the independent experimental unit. Independent manual validation showed close agreement for plant height and projected area, with R 2 values of 0.9969 and 0.986, respectively. Overall, the proposed workflow provides a feasible approach for organ-level segmentation and automated 3D structural analysis of small coriander seedlings under salt stress. The extracted descriptors primarily represent reconstruction-derived spatial characteristics and should not be interpreted as direct indicators of physiological status; voxel occupancy volume and leaf point ratio remain without direct external validation.
Why it matches plant phenotyping methods3D点群による器官セグメンテーションと構造形質抽出の手法開発・内部比較・手動検証が研究の中心であり、塩ストレスは適用対象である。
abstractwe developed PTV-SegCo, a task-adapted Point Transformer model for organ segmentation and structural phenotyping of coriander seedlings under salt stress.
Maize ear geometry (length, width, curvature, and volume) is closely tied to yield and grain-filling outcomes, but existing high-throughput phenotyping pipelines remain constrained by the cost, labor, and specialized hardware they require. We developed and validated a low-cost pipeline that reconstructs a watertight 3-D mesh of a maize ear from a single 20-second video captured with a consumer-grade DSLR on a motorized turntable under uniform LED illumination. Camera poses from a multi-seed COLMAP procedure initialize a Neural Radiance Field (NeRF), and a cylindrical holder of known diameter, visible in every frame, provides automatic metric scaling with downstream geometric quality control. Applied to 300 ears spanning a diverse maize inbred panel, 250 (83.3%) passed automated processing and quality control. Skeleton length agreed with manual caliper measurements across all 250 ears (R^2 = 0.964, RMSE = 4.68 mm), and convex-hull volume agreed with water-displacement volume on a 15-ear subset spanning the full size range (R^2 = 0.982, RMSE = 5.26 mL). Residual length error grew with ear curvature, whereas bounding-box height, which records the same straight-line chord as calipers, showed no such trend; the discrepancy therefore originates in the measurement definition, since calipers record the chord while skeleton length traces the geodesic arc. The capture hardware costs approximately 607 USD, and operator involvement fell from roughly five minutes to one minute per ear, with all downstream processing running unattended. The platform provides a foundation for breeding-scale 3-D ear phenotyping.
Why it matches plant phenotyping methodsトウモロコシ雌穂の3D形態形質を抽出する低コスト画像計測パイプラインを開発し、手動測定および体積測定で技術検証しているため、方法が研究の中心である。
abstractWe developed and validated a low-cost pipeline that reconstructs a watertight 3-D mesh of a maize ear
Surface phenotyping underpins plant science, preclinical animal research and entomology, yet across all three the measurement is almost always a photograph, which records a projection and not the surface itself. Here we present the Gentschinator3000 , an open structured-light platform that brings high-end metric surface measurement within reach of laboratories with no optics expertise, combining documented open hardware, open reconstruction software and analysis workflows for under 4000 Euro in components. It resolves a planar reference to 45 µm local flatness, registers full rotations to a loop closure of 156 µm, and performs stably across acquisition ranges that we define. Applying one workflow to a leaf before and after desiccation, to murine anatomy and to a spread lepidopteran, we find that projection underestimates surface area by 11 to 41 %. That error grows with the condition under study, with the evaluation scale and with the direction of view, so it can confound phenotype comparisons dramatically. In murine limbs a 15-degree change of viewing direction shifts a projected inter-segment angle by up to 23.2 degrees, while the three-dimensional angle does not move. Projection geometry can therefore contribute as much to a measured phenotype as the biology it is meant to quantify.
Why it matches plant phenotyping methods植物表面の三次元形状を測定するオープンな構造化光プラットフォームと再構成・解析ワークフローを開発し、葉で投影バイアスを評価しており、表現型取得手法が中心である。
abstractHere we present the Gentschinator3000 , an open structured-light platform that brings high-end metric surface measurement within reach of laboratories with no optics expertise, combining documented open hardware, open reconstruction software and analysis workflows for under 4000 Euro in components.
Reproduction assets foundThe paper explicitly deposits three public Zenodo records: reconstructed 3D surfaces of all specimens (including the leaf and hop cone phenotyping measurements), the authors' analysis notebooks with derived and per-panel source data, and the reconstruction software with build documentation and working examples. All areDataset · publicData availability
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High-throughput phenotyping depends on accurate 3D reconstruction of plants across growth stages, yet the development and evaluation of temporal completion methods are limited by the lack of datasets with complete geometric ground truth. To address this challenge, we introduce SynthCrop4D, a procedurally generated synthetic dataset of temporally evolving plant point clouds that provides controllable noise, occlusion, and complete plant geometry for benchmarking reconstruction methods. Using this dataset, we evaluate a two-stage pipeline that combines spatial denoising and temporal point cloud completion. First, a denoising module removes structural artifacts from raw laser-scanned point clouds. The resulting data are then processed by an Adaptive Temporal PoinTr model that reconstructs the current growth stage (t) using information from the previous stage (t-1), enabling recovery of regions missing due to self-occlusion. We evaluate the proposed framework on both SynthCrop4D and the real-world Pheno4D dataset (tomato and maize) under settings with and without denoising. Results show that denoising substantially improves reconstruction quality, with the best configuration achieving a Chamfer Distance of 0.0061 on SynthCrop4D (Temporal PoinTr + Mamba-DG) and an F-Score of 0.2080 on Pheno4D (Vanilla PoinTr + Mamba-DG). We further demonstrate the use of completed point clouds for phenotypic trait extraction, including plant height, canopy width, and convex hull volume, obtaining hull-volume MAEs of 0.021 on synthetic data and 0.343 on real data. Together, SynthCrop4D and the proposed pipeline provide a benchmark and methodology for temporal plant reconstruction and high-throughput crop phenotyping.
Why it matches plant phenotyping methods植物の3D点群再構成・時系列補完を開発し、合成データセットと実データで性能検証するとともに、草丈・群落幅・凸包体積を抽出する手法を中心に扱っている。
abstractwe introduce SynthCrop4D, a procedurally generated synthetic dataset of temporally evolving plant point clouds that provides controllable noise, occlusion, and complete plant geometry for benchmarking reconstruction methods.
Reproduction assets foundThe paper's authors explicitly state that source code, implementation details, and pre-trained model weights are publicly available on GitHub, and that the paper-specific SynthCrop4D synthetic dataset can be reproduced via scripts in that codebase. Pheno4D is a cited prior public dataset, not a paper-specific asset.Code · publicThe source code, implementation details, and pre-trained model weights for this study are publicly available on GitHub at https://github.com/Mrudul2006/3d_plant-reconstruction .Open asset ↗Mrudul2006/3d_plant-reconstructionlines:1724-1761Plant phenotyping relevance match · UnverifiedEurope PMC · OpenAlex · checked 15 Sept 2026
Alfalfa / lucerneX-ray / CTRoot2D/3D reconstructionVisualization / data managementGrowth / development / phenology
The symbiotic relationship between the legume Medicago sativa and the soil bacteria Sinorhizobium meliloti results in the formation of nitrogen-fixing root nodules. Traditional destructive methods, including paraffin sectioning, vibratome sectioning, and cryosectioning, have been applied to visualize how bacteria occupy the nodule, making it extremely difficult to obtain reliable three-dimensional information. These approaches are often combined with fluorescent labeling or staining, which can introduce additional stress affecting plant growth and nodule formation. MicroCT has emerged as a relatively quick, easy, and robust tool for plant biology that can non-destructively visualize plant histological features in three dimensions (3D), thereby avoiding destructive artifacts during sample preparation and ensuring high-fidelity 3D reconstruction. While microCT has been applied to legume root nodules, a detailed established protocol that documents the process from plant harvest and sample preparation to scanning and software visualization is lacking. In this study, we show a step-by-step microCT workflow using Medicago sativa as a model. The protocol includes nodule excision from roots, fixation, contrast enhancement, mounting, scanning, and three-dimensional reconstruction. Critical parameters affecting elements such as image quality, tissue preservation, and contrast are highlighted. Using this approach, it is possible to visualize the overall tissue organization, bacteroid-infected cells, and vascular bundles in three dimensions without physically sectioning the nodules. The pipeline described here provides a reproducible method for non-destructive, high-resolution imaging of native root nodules and is likely adaptable to other legume species, offering researchers a practical tool for studying nodule structure and bacterial organization within nodules in 3D.
Why it matches plant phenotyping methods根粒の組織構造と感染細胞を3Dで取得するMicroCT撮像・再構成プロトコルが研究の中心であり、植物器官の形態状態を測定する実質的なフェノタイピング手法である。
abstractIn this study, we show a step-by-step microCT workflow using Medicago sativa as a model.
While most phenotyping platforms rely primarily on image-based measurements, advanced plant characterization requires the integration of active physiological sensing modali- ties such as chlorophyll fluorescence. We present an autonomous robotic platform designed to perform targeted fluorescence measurements on plant leaves. The system combines 3D plant reconstruction, geometric analysis, and motion planning to localize suitable measurement points and generate collision-free trajectories for a robotic manipulator. A dense 3D model of the plant is reconstructed from multi-view data and used to extract candidate leaf surfaces based on orientation, accessibility, and sensing constraints. These targets are then integrated into a task-level planning framework that guides the end-effector to precise contact or near-contact configurations required for point-based fluorescence acquisition. The platform enables automated, repeatable, and spatially resolved physiological measurements that go beyond passive imaging. By tightly coupling perception, geometric reasoning, and manipulation, the proposed system provides a robotics-driven approach to high-resolution plant phenotyping and opens new directions for autonomous agricultural inspection and plant-aware manipulation.
Why it matches plant phenotyping methods植物葉の蛍光を自律ロボットで空間的・反復的に取得するプラットフォームを開発しており、植物表現型の取得手法が研究の中心です。
abstractWe present an autonomous robotic platform designed to perform targeted fluorescence measurements on plant leaves.
Reproduction assets foundThe paper states its code is publicly available in the authors' SonyCSLParis GitHub repository (Plant3DImager), which implements the phenotyping perception and motion-planning pipeline. The exact full URL is split across a line break in the supplied text, so the verifiable allowed URL prefix is used.Code · public2 The code is available at https://github.com/SonyCSLParis/ 3 See for example at https://www.youtube.com/watch?v=Open asset ↗SonyCSLParis/pdf-page:4 lines:1-61Code / dataset availability confirmedbioRxiv · checked 5 Sept 2026
Biomolecular condensates that persist through cell division must be reorganized and inherited, yet it remains unclear whether subtle defects before division are associated with later organelle or growth phenotypes. We examined the Chlamydomonas reinhardtii pyrenoid, a liquid-like condensate that concentrates ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco), the photosynthetic CO2-fixing enzyme. As part of the algal CO2-concentrating mechanism, the pyrenoid raises CO2 availability around Rubisco. We generated an RBCS1-mGold Rubisco reporter and developed an unsupervised image-analysis pipeline combining a convolutional autoencoder and a one-class support vector machine. Using 4,905 wild-type single-cell images, augmented 22-fold to 107,910 image instances, we defined the range of normal pyrenoid morphology. A combined machine-learning and visual screen of approximately 21,000 insertional mutants yielded 17 pyrenoid integrity mutants (pim1-pim17). Differential reconstruction-error maps highlighted local deviations from the wild-type reference, including phenotypes difficult to classify by eye. Four-dimensional live imaging showed defects in matrix dispersal, partitioning of Rubisco-containing foci, or pyrenoid recondensation in multiple pim strains. Growth assays identified broad defects and phenotypes that became more apparent as CO2 supply decreased. Insertion-site mapping nominated candidate loci, including STT7, which encodes a chloroplast kinase best known for regulating photosynthetic light harvesting. Independent STT7-edited lines lacked detectable STT7 accumulation and showed pyrenoid-region reconstruction-error patterns, supporting an association between impaired STT7 function and altered pyrenoid morphology. These findings show that unsupervised image screening can extend forward genetics to subtle pyrenoid phenotypes accompanied by mitotic remodeling or growth defects.
Why it matches plant phenotyping methods藻類細胞のピレノイド形態を対象に、画像解析と教師なし機械学習パイプラインを開発し、正常範囲の定義・変異体スクリーニング・検出性能の実証を行っており、表現型取得手法が研究の中心である。
abstractdeveloped an unsupervised image-analysis pipeline combining a convolutional autoencoder and a one-class support vector machine
Reproduction assets foundThe paper's custom machine-learning analysis scripts (CAE–OC-SVM pyrenoid screening pipeline) are explicitly stated to be publicly available on the authors' GitHub repository. Other data (microscopy files, anomaly scores) are only available upon request, so they do not qualify as public assets.Code · publicCustom scripts used for the machine-learning analyses are publicly available at https://github.com/Yamano-Lab/2025_Machine_Learning-based_screening .Open asset ↗Yamano-Lab/2025_Machine_Learning-based_screeninglines:103-119Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Field / plotLiDAR / point cloudStem / branchWhole plant / canopy / plot / fieldClassification2D/3D reconstructionSegmentationArchitecture / morphology / geometry
The separation of a forest plot into individual trees and the automatic extraction of their stem structures from terrestrial laser scanning data are complicated by dense stands, occlusions, and the diversity of biomorphological forms. Existing algorithms usually solve scene partitioning, voxel classification, and tree growing as independent tasks, which leads to error accumulation at subsequent processing stages. This paper proposes a unified model for spatial fragmentation and biomorphological forest segmentation comprising three interrelated stages: scene partitioning by estimated stem coordinates using a Voronoi diagram, probabilistic voxel- or point-level classification, and bottom-up tree growing guided by spatial connectivity and stem membership criteria. For the semantic module, tabular, volumetric, and point-based approaches are compared: gradient boosting with layer-by-layer inference, TabNet, a three-dimensional convolutional neural network, PointNet2, and two-stage pipelines in which gradient boosting builds an initial stem mask for subsequent neural segmentation. The experiment was conducted on 567 mixed-species trees. Considering both quality and computational performance, the {CatBoost; CNN3D} pipeline was selected as the preferred solution, achieving AUC = 0.9966 and IoU = 0.9831. The obtained results show that combining interpretable layer-by-layer classification with subsequent spatial analysis improves the quality of stem structure reconstruction, which is important for automatic forest inventory tasks.
Why it matches plant phenotyping methods地上レーザースキャンから個体樹木と幹構造を自動抽出する統合セグメンテーション手法を開発・比較しており、植物形態の取得が研究の中心である。
abstractThe separation of a forest plot into individual trees and the automatic extraction of their stem structures from terrestrial laser scanning data are complicated by dense stands, occlusions, and the diversity of biomorphological forms.
Abstract Living tissues contain dynamic biochemical information that is difficult to capture with conventional hyperspectral microscopes because sequential spectral acquisition is poorly matched to in vivo molecular processes that evolve during measurement. Here we introduce a task-specific optical encoding framework for video-rate molecular inference in living plant tissue. The system integrates a passive spectral encoder, implemented here as a low-angle scattering LDPE layer, into a 22-mm miniaturized probe and learns a supervised mapping from ultraviolet-excited autofluorescence measurements to biomolecular abundance maps. Unlike conventional pipelines that first reconstruct hyperspectral datacubes and then perform spectral unmixing, the deployed system directly estimates endogenous molecular contrast associated primarily with lignin and chlorophyll in poplar tissue, with additional suberin-associated contrast evaluated in suberin-rich tissue. This reframing makes the measurement task biomolecular inference rather than spectral reconstruction, enabling biochemical mapping under low-photon autofluorescence conditions while reducing data burden and computational latency. In living poplar stems, the platform captures autofluorescence-derived videos of embolism propagation and wound-induced biochemical remodeling, dynamic processes for which sequential spectral acquisition can introduce temporal mixing because the molecular contrast evolves during the scan itself. The system also resolves genotype-dependent reductions in lignin-associated autofluorescence in engineered poplar lines. Direct molecular inference improves biomolecular estimation relative to a reconstruction-based pipeline, while probabilistic decoding provides uncertainty estimates. These results show that compact passive spectral encoding, when optimized for biological inference rather than datacube recovery, enables deployable, label-free molecular videography of living plant tissue dynamics after task-specific calibration.
Why it matches plant phenotyping methods生体植物組織の生化学的状態を動画取得・推定する光学センシング手法を開発し、校正、比較評価、不確実性推定まで行っており、表現型取得法が中心である。
abstractHere we introduce a task-specific optical encoding framework for video-rate molecular inference in living plant tissue.
Canopy 3D architecture is a critical determinant of light interception, photosynthetic efficiency, and final yield in cotton, yet its rapid and accurate characterisation remains challenging in field conditions. To achieve efficient, non-destructive, and quantitative monitoring of the canopy structure of field-grown cotton, this study proposes a 3D structure-based technology stack for high-efficiency, low-cost, and high-precision phenotyping extraction. This stack directly addresses the technical bottlenecks of traditional 3D data acquisition, namely high cost, long processing time, and low operational efficiency, which have hindered large-scale application. We developed a pipeline that integrates a fast reconstruction algorithm with a scale-recovery mechanism using ground control points (GCPs), enabling the generation of true-scale 3D point clouds from UAV aerial images in a cost- and time-effective manner. Using only 141 UAV images and with a reconstruction time of approximately 20 min, we efficiently reconstructed high-quality, scale-accurate point clouds of two 5.5 m × 5.5 m cotton plots, significantly outperforming SfM-MVS and Instant-NGP in terms of both reconstruction efficiency and point cloud completeness. This method, whose current validation is confined to a single season, one growth stage, and two experimental plots, not only achieves a breakthrough by using fewer input images with high efficiency, but also ensures point cloud accuracy and completeness, showing strong potential for rapid field monitoring and real-time management. Based on the high-quality reconstructed point clouds, we further quantitatively evaluated canopy characteristics at harvest, analyzing the coefficient of variation of canopy height, porosity distribution, and canopy volume fraction. The core shortcomings and optimization strategies for the existing canopy structure were identified, providing scientific data support and practical technical references for precision cultivation management and mechanization-compatible planting in cotton.
Why it matches plant phenotyping methodsUAV画像からの3D点群再構成とスケール復元パイプラインを開発・比較検証し、綿花キャノピー形質を定量化することが研究の中心であるため含める。
abstractthis study proposes a 3D structure-based technology stack for high-efficiency, low-cost, and high-precision phenotyping extraction.
NeRF / 3D Gaussian SplattingFruit2D/3D reconstructionSegmentation
High-fidelity and lightweight 3D fruit models are a crucial foundation for phenotypic analysis and automated agricultural robotic operations. However, in complex agricultural scenarios characterized by varying illumination and foliage occlusion, existing 3D reconstruction methods struggle to balance reconstruction accuracy and model size, often generating massive redundant background primitives. To address this challenge, this paper proposes a novel framework for lightweight and high-fidelity 3DGS fruit reconstruction via geometric-semantic joint constraints. Specifically, the method first integrates depth priors and semantic information through a Depth-Guided Semantic Segmentation module to extract accurate target fruit masks. Next, it eliminates background noise points from the initial point cloud using a multi-view Reprojection Consistency Voting mechanism. Simultaneously, a Stochastic Background Regularized Hybrid Loss is introduced during the 3DGS training phase to decouple density and color optimization, thereby suppressing the regeneration of background Gaussian primitives. Experimental results on a multi-category fruit dataset demonstrate that while maintaining a high novel view synthesis quality (PSNR of 31.87 dB), our proposed method reduces the average model size from 230.42 MB to 62.66 MB (a 72.8% reduction), achieving robust, high-fidelity, and lightweight 3D fruit reconstruction.
Why it matches plant phenotyping methods果実の3D形状を抽出・再構成する画像ベース手法が研究の中心であり、果実形態のフェノタイピングに直接利用可能な方法を開発・評価している。
abstractHigh-fidelity and lightweight 3D fruit models are a crucial foundation for phenotypic analysis
Field / plotPhotogrammetry / SfM / MVSLiDAR / point cloudMultispectral / hyperspectralWhole plant / canopy / plot / field2D/3D reconstruction
The Greater Cape Floristic Region (GCFR) in South Africa is a fire-prone biodiversity hotspot where high species richness, structural complexity, and small plant sizes (0.0001–4 m2) pose substantial challenges for remote sensing-based biodiversity assessment. Spectral similarity among species and the mismatch between plant size and sensor pixel dimensions limit the capacity of current and forthcoming spaceborne systems to resolve individual species and accurately detect plot-level diversity changes. We therefore developed a physics-based simulation framework that couples fynbos trait measurements with radiative transfer modeling in the DIRSIG (Digital Imaging and Remote Sensing Image Generation) environment towards quantifying information loss across spectral and spatial scales and to define theoretical limits for biodiversity monitoring. We constructed a three-dimensional virtual scene of post-fire fynbos communities in Grootbos Private Nature Reserve, integrating high-resolution imagery, terrestrial laser scanning (TLS), and structure-from-motion (SfM)-derived point clouds. Field measurements of mean diameter and percent cover were used to scale vegetation models and constrain species abundance. We distributed plant instances using a blue noise sampling algorithm, guided by density maps derived from unmanned aerial system (UAS) imagery. Species-specific optical properties were parameterized using field-measured reflectance data and the PROSPECT radiative transfer model, while terrain structure was derived from SfM-based digital terrain models. The integrated scene was used to simulate multispectral (DJI Mavic 3 MSI), hyperspectral (AVIRIS-NG), and light detection and ranging (LiDAR) observations. Agreement between simulated outputs were evaluated against corresponding field-acquired datasets using spectral signatures and vegetation indices. This framework enables systematic assessment of sensor specification effects on spectral biodiversity metrics and provides a pathway for evaluating theoretical limits of species discrimination across airborne and satellite platforms.
Why it matches plant phenotyping methods植物群落の種判別・多様性指標を対象に、物理ベースの仮想シーンとマルチモーダルセンシングを開発し、実測データで検証しているため、植物状態の取得・推定法が中心である。
abstractWe therefore developed a physics-based simulation framework that couples fynbos trait measurements with radiative transfer modeling in the DIRSIG (Digital Imaging and Remote Sensing Image Generation) environment towards quantifying information loss across spectral and spatial scales and to define theoretical limits for biodiversity monitoring.
Plant secondary metabolites are mainly synthesized and stored in secretory tissues. Secretory canal development has been mainly characterized in Apiaceae. The secretory canals of Peucedanum praeruptorum contain pharmacologically active coumarins, but their organ-specific distribution and developmental dynamics remain poorly understood. This study integrated light microscopy (LM), transmission electron microscopy (TEM), X-ray microcomputed tomography (µ-CT), and high-performance liquid chromatography (HPLC) to investigate canal development, distribution, ultrastructure, 3D architecture, and coumarin accumulation in P. praeruptorum roots. Histological analysis showed that canals adjacent to the periderm originate from pericycle cells, whereas those in secondary phloem arise from parenchyma differentiation; both develop schizogenously. Canal quantity and dimensions varied temporally. Canals located in phloem showed density increasing toward the cambial zone, where cross-sectional areas were smaller. The canal density index increased from September to November, peaking on November 15, then declined. HPLC revealed dynamic accumulation of five major coumarins: content increased from September, peaked on November 15, then gradually decreased. TEM showed that epithelial cells surrounding the canal lumen were rich in Golgi, ER, mitochondria, plastids, starch grains, and osmiophilic droplets. µ-CT volumetric analysis and segmentation generated detailed 3D models, revealing spatial organization and enabling size-based grouping of canals (1000-3000 μm). These dimensional characteristics aligned with developmental progression. This study characterizes the ontogeny, distribution, ultrastructure, and 3D architecture of secretory canals, providing a structural foundation for investigating correlations between secretory tissues and compound synthesis.
Why it matches plant phenotyping methods根の分泌道の密度・寸法・3D構造という植物器官形質を、µ-CTの体積解析・セグメンテーションと顕微鏡法で取得・抽出しており、画像計測が研究の中心的要素である。
abstractµ-CT volumetric analysis and segmentation generated detailed 3D models, revealing spatial organization and enabling size-based grouping of canals (1000-3000 μm).
Unmanned aerial vehicle (UAV)-based multispectral imaging enables flexible, non-destructive crop monitoring. Although UAV imagery offers much higher spatial resolution than satellite platforms, its effective spatial detail at typical operational flight altitudes can still be insufficient for plant-level interpretation and fine canopy structure, which can reduce vegetation-index reliability. Most super-resolution (SR) research targets RGB or satellite imagery and emphasizes perceptual or pixel-wise quality, leaving the spectral fidelity of reconstructed UAV multispectral imagery under-examined. This study benchmarked an SR evaluation framework for UAV-based five-band crop imagery (Blue, Green, Red, Red-edge, and near-infrared) using the open-source AI Hub cabbage dataset, with low-resolution inputs generated by controlled downsampling at ×2, ×3, and ×4. Nine methods (bicubic, SRCNN, EDSR, RCAN, SwinIR-based, ESRGAN-based, HAT-based, DAT-based, and DRCT-based SR) were compared under joint five-channel and band-wise reconstruction on 2170 test scenes using image-quality, spectral-angle, vegetation-index (NDVI, GNDVI, NDRE), band-wise, and efficiency metrics. EDSR and RCAN gave the most balanced performance. At ×4, band-wise reconstruction was strongest for per-band spatial fidelity, where EDSR reduced RMSE by 12.6%, and RCAN lowered near-infrared RMSE by about 21% relative to bicubic, whereas joint reconstruction with its spectral-angle and vegetation-index losses best preserved spectral relationships (spectral angle and vegetation-index errors). Learning-based gains were clearest at ×4. The recently proposed HAT-based, DAT-based, and DRCT-based attention models achieved the strongest pixel-wise RMSE and PSNR but did not surpass EDSR or RCAN on spectral angle or vegetation-index preservation under the equalized training budget. These results indicate that UAV multispectral SR should be assessed by spatial fidelity together with spectral consistency and agricultural index reliability.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像の超解像法を比較・ベンチマークし、スペクトル一貫性や植生指数の信頼性を評価する研究であり、植物キャノピー形質の画像取得・抽出基盤が中心である。
abstractThis study benchmarked an SR evaluation framework for UAV-based five-band crop imagery
Functional-structural plant models simulate plant responses to environmental conditions, but their development and evaluation are often limited by the lack of datasets combining detailed architectural and physiological measurements. Here, we present a comprehensive dataset acquired from four oil palm plants ( Elaeis guinnensis) grown under controlled and contrasting climate scenarios. The dataset includes (i) three-dimensional reconstructions of plant architecture derived from terrestrial lidar point clouds, (ii) leaf-level gas exchange measurements used to parameterize photosynthesis and stomatal conductance models, and (iii) continuous plant-scale measurements of CO 2 and H 2 O fluxes obtained in a microcosm under precisely monitored and manipulated environmental conditions (light, temperature, humidity, and CO 2 concentration) across height climate scenarios. By combining detailed structural data with physiological measurements at both leaf and whole-plant scales, this database has been designed to build and evaluate digital twins (or shadows) of plants functioning under controlled conditions. It provides a valuable resource for calibrating biophysical models (light interception and photosynthesis), benchmarking model predictions across scales, and investigating the consistency between leaf-level parameterization and plant-level fluxes. All data and processing workflows are openly available, facilitating reuse for model development, evaluation, and intercomparison in plant and crop modelling communities.
Why it matches plant phenotyping methods3D LiDARによる植物構造計測と生理計測を統合したデータセットで、モデルの較正・ベンチマーク・評価を主目的としており、植物フェノタイピング手法と再利用可能なワークフローが中心である。
abstractthree-dimensional reconstructions of plant architecture derived from terrestrial lidar point clouds
Field / plotLiDAR / point cloudWhole plant / canopy / plot / field2D/3D reconstructionYield / biomass estimationBiomass / plant weight
Reliable aboveground biomass (AGB) estimates for woody crops are essential for carbon accounting and for Measurement, Reporting and Verification (MRV) frameworks. However, it remains unclear how LiDAR modality and sampling geometry influence plot-scale and tree-scale AGB predictions in intensively managed Mediterranean orchards. In this study, we benchmarked four LiDAR modalities, namely open national airborne laser scanning from the Spanish National Aerial Orthophotography Plan (PNOA/ALS), a dedicated Riegl airborne laser scanner (ALS), unmanned laser scanning (ULS) and mobile laser scanning (MLS), across three woody-crop sites in Córdoba (southern Spain): IFAPA, Doña María, and Villaseca. Plot-level LiDAR metrics (mean height, 95th height percentile, maximum height, and canopy-cover proxies) were extracted from normalized point clouds and related to field AGB using Random Forest and XGBoost regression models, together with an ensemble predictor, under an 80/20 train–test split. In parallel, TreeQSM-based Quantitative Structure Models (QSMs) were evaluated as an independent tree-level three-dimensional reconstruction approach. XGBoost achieved the lowest errors at IFAPA (RMSE = 0.400 Mg ha−1; R2 = 0.994) and Villaseca (RMSE = 0.872 Mg ha−1; R2 = 0.995), whereas PNOA/ALS was competitive at Doña María (RMSE = 0.725 Mg ha−1; R2 = 0.994). TreeQSM closely matched the field inventory at the low-biomass IFAPA site but tended to overestimate biomass at Doña María and Villaseca, and only 28% of scanned trees yielded usable reconstructions. The results support the use of cross-platform LiDAR for orchard AGB and carbon mapping and identify the conditions under which open national LiDAR can enable scalable MRV of Mediterranean woody crops.
Why it matches plant phenotyping methodsLiDAR複数プラットフォームと3D再構成を比較・検証し、樹木・区画レベルの地上部バイオマスという植物形質を推定する手法が研究の中心である。
Continuous X-ray computed tomography (XCT) combined with digital volume correlation (DVC) is presented to quantify internal three-dimensional strain and failure dynamics in apple cortex tissue during compression, revealing how the cellular microstructure governs its mechanical response. We introduce a dimensionless number Mi that is a function of the average interfacial contact area of cells, cell wall thickness, the average cell volume and tissue porosity, to describe tissue microstructure over different development stages. Mechanical softening during maturation aligned strongly with decreasing Mi, linking microstructure to effective Young's modulus, peak stress, and toughness. DVC revealed distinctive strain-distribution signatures: in young, low-porosity tissue, strain was initially diffuse with early-onset localization indicating progressive failure, whereas mature, high-porosity tissue exhibited sharply peaked strain distributions and highly localized fracture planes indicative of brittle collapse. These findings demonstrate how pore evolution, anisotropy, and cell packing jointly determine macroscopic deformation, establishing XCT-DVC as a powerful framework for connecting plant tissue architecture to mechanical function.
Why it matches plant phenotyping methods4D XCTとDVCを用いてリンゴ組織の内部三次元ひずみ、微細構造、破壊状態を定量化する手法が研究の中心であり、植物組織の構造・力学的状態を抽出しているため。
abstractContinuous X-ray computed tomography (XCT) combined with digital volume correlation (DVC) is presented to quantify internal three-dimensional strain and failure dynamics in apple cortex tissue during compression
Stomatal traits are key microscopic phenotypes for evaluating plant physiology, stress responses, and crop breeding potential. However, in vivo high-magnification microscopy often suffers from a shallow depth of field, causing noticeable defocus blur across different spatial locations and making it difficult to capture clear and complete stomatal structures in a single image. Multi-focus image fusion offers a practical solution, yet existing methods typically rely on supervised training, paired data, or hand-crafted rules, limiting their use in real agricultural microscopy scenarios. In this study, we propose an unsupervised multi-focus fusion framework for reconstructing fully focused stomatal microscopic images. The method integrates two-dimensional feature extraction with three-dimensional cross-focal-plane modeling to capture both spatial details and complementary information across focal planes. A max-response-guided spatial gating module is introduced to enhance focused regions while suppressing defocused responses. Additionally, dual sharpness priors based on perceptual features and wavelet high-frequency information enable pixel-wise pseudo-supervised learning without requiring all-in-focus ground-truth images. The model also predicts a probabilistic focal-plane volume for interpretable all-in-focus reconstruction. Experiments on a maize multi-focus image dataset demonstrate that the proposed method achieves superior or competitive performance across multiple fusion metrics, with entropy (EN), edge information preservation ( Q AB∕F ), Chen-Blum contrast metric ( Q CB ), and visual information fidelity for fusion (VIFF) reaching 7.43, 0.21, 0.41, and 1.01, respectively. Ablation studies confirm the effectiveness of the 3D modeling, spatial gating, and dual-prior sharpness supervision. More importantly, when the fused images serve as input to a YOLO-based stomatal instance segmentation model, the proposed method yields the best segmentation accuracy, with mAP50 and mAP50-95 reaching 0.9937 and 0.9121, respectively. Phenotypic measurements derived from the segmentation masks show high consistency with manual annotations, with the highest coefficient of determination R 2 = 0.97 achieved for stomatal count. These results indicate that the framework can act as an effective front-end module for automated microscopic stomatal phenotyping in agriculture.
Why it matches plant phenotyping methods植物の気孔表現型を対象に、マルチフォーカス画像融合、セグメンテーション、形質測定までを中核的に開発・検証しているため。
abstractwe propose an unsupervised multi-focus fusion framework for reconstructing fully focused stomatal microscopic images.
Reproduction assets foundThe authors state their data and code are publicly available on GitHub, covering the multi-focus stomatal microscopy dataset and the UMF-stomata fusion/phenotyping code.Code · publicOur data and code are available at: https://github.com/Longer-S/UMF-Stomata.Open asset ↗Longer-S/UMF-Stomatahtml-lines:640-655Dataset · publicOur data and code are available at: https://github.com/Longer-S/UMF-Stomata.Open asset ↗Longer-S/UMF-Stomatahtml-lines:683-756Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
Efficient facility-scale tomato ripeness monitoring remains difficult in greenhouses where uneven terrain limits conventional wheeled and rail-guided platforms and planar cameras provide restricted coverage. This study developed a wheel-legged quadruped monitoring system integrating LiDAR, a depth camera, and a panoramic camera. An adaptive gait-switching strategy supported navigation across heterogeneous terrain. Panoramic images were projected into six perspective views, and the left and right views were processed using a YOLOv8-based ripeness recognition model. Time-synchronized detections and robot poses were fused to map ripeness observations into three-dimensional greenhouse coordinates. Five field experiments in a commercial tomato facility demonstrated autonomous row traversal, inter-row transition, and avoidance of pedestrians, obstacles, and cultivation boundaries. The recognition pipeline continuously identified multiple ripeness stages under variable illumination, foliage occlusion, and robot motion, while the spatial fusion procedure produced a facility-scale three-dimensional ripeness distribution. The integration of terrain-adaptive quadruped mobility, panoramic perception, and spatial mapping provides a practical framework for continuous ripeness monitoring and can support targeted harvesting, yield forecasting, and crop management.
Why it matches plant phenotyping methodsトマト果実の成熟度を画像認識で取得するロボット型フェノタイピングシステムを開発し、実環境で評価しており、表現型取得法が研究の中心である。
abstractThis study developed a wheel-legged quadruped monitoring system integrating LiDAR, a depth camera, and a panoramic camera.
Accurate tree volume estimation is central to forest management and carbon accounting. Allometric equations are widely used but limited in transferability across species, regions, and environmental conditions. Mobile Laser Scanning (MLS) offers a promising alternative through direct measurement of tree geometry; however, the influence of tree shape on MLS accuracy remains poorly understood. This study evaluated MLS-derived estimates of stem diameters, total tree height, and merchantable stem volume against destructive reference measurements from 176 trees spanning eight species (four hardwood, four softwood) in Wallonia, Belgium. A Zeb Horizon RT scanner was used; tree architectural descriptors extracted from the point cloud were tested for associations with measurement error. Across 7,824 stem diameter measurements, MLS achieved a mean error of 0.46 cm, with precision declining above 15 m. MLS-derived height outperformed Vertex IV clinometer measurements for hardwood species (RMSE% = 6.88 vs. 8.78) but performed slightly less well for softwoods (RMSE% = 7.36 vs. 6.14). QSM-based volume estimates systematically underestimated reference values, while taper-based reconstruction produced nearly unbiased estimates with an RMSE of 15.72%. Correlation analyses and PCA showed that tree architectural variables explained only a small fraction of MLS error variability. Diameter and height errors were largely independent of structural attributes, while volume errors showed moderate associations with tree size and crown density. These findings indicate that tree architecture is not a primary source of MLS measurement uncertainty. Future MLS-based forest inventory efforts should prioritize acquisition and processing optimization, as scanning conditions and forest structure appear more influential than tree shape.
Why it matches plant phenotyping methodsMLSによる樹木形状・直径・樹高・幹材積の非破壊推定を、破壊測定と比較検証し、測定誤差の要因を評価した方法検証研究である。
titleNon-destructive tree volume estimation using mobile laser scanning: Impact of the tree shape on measurement error.
Field / plotMultimodalNeRF / 3D Gaussian SplattingLiDAR / point cloudRGB / grayscaleStereoFruitWhole plant / canopy / plot / fieldMorphology / geometry measurementObject detection
Binocular stereo vision is a low-cost and scalable 3D perception technology that shows strong potential in agricultural phenotyping and smart agriculture. By estimating depth from multi-view RGB images, it enables non-contact, high-precision sensing of crop structure, canopy morphology, growth dynamics, and livestock traits, providing essential support for digital and intelligent agricultural production. With recent advances in deep learning-based stereo matching, multimodal sensor fusion, and 3D reconstruction, its robustness and accuracy in complex field environments have been significantly improved. This paper systematically reviews recent progress in agricultural applications of binocular stereo vision, covering system architectures, traditional and deep learning-based stereo matching methods, point cloud reconstruction techniques, and emerging supervision strategies such as 3D Gaussian splatting. It further summarizes key applications, including high-throughput phenotyping, fruit localization and robotic harvesting, weed detection and precision spraying, autonomous navigation, and livestock body condition assessment, highlighting its role in multi-task agricultural perception systems. Finally, the paper discusses major challenges, including low-texture matching difficulty, occlusions in complex environments, cross-domain generalization, real-time lightweight deployment, and limited dataset availability. Future directions are outlined in foundation model-based visual perception, self- and weakly supervised learning, multimodal fusion, and edge-efficient model design, aiming to support large-scale deployment in smart agriculture.
Why it matches plant phenotyping methods農業における双眼ステレオビジョンのシステム、ステレオマッチング、3D再構成を体系的にレビューし、作物構造・群落形態・生育動態の非接触計測とハイスループット表現型解析を主要対象としているため。
abstractThis paper systematically reviews recent progress in agricultural applications of binocular stereo vision, covering system architectures, traditional and deep learning-based stereo matching methods, point cloud reconstruction techniques
Monitoring continuous agricultural canopies is fundamentally limited by the geometric constraints and computational bottlenecks of traditional 3D reconstruction. This study presents a 3D volumetric phenotyping pipeline integrating semantic mask generation with 3D Gaussian Splatting (3DGS) to quantify greenhouse cucumber canopy architecture. To drive component-specific optimization, we evaluated custom-trained convolutional networks (YOLO11) against a zero-shot foundation model (SAM3), determining that SAM3 provided the necessary boundary precision for accurate spatial isolation. The optimized 3DGS model outperformed implicit NeRF baselines, preserving fine-scale morphological details at real-time rendering speeds ( ≈ 48 FPS). To enable actionable measurement, a uniform voxelization protocol was applied to the point cloud, successfully neutralizing algorithmic densification bias. This technical framework yielded highly accurate physical geometry, achieving a Root Mean Square Error (RMSE) of ≤ 0.59 cm against in situ leaf measurements. Transitioning to agronomic interpretation, the pipeline was deployed to quantify complex canopy architecture. It mathematically mapped structural congestion zones and provided a temporal validation of a standard pruning intervention, explicitly capturing the geometric increase in lower-canopy porosity and the upward translation of biomass. This framework provides a robust, scale-accurate tool for monitoring plant architecture and guiding dynamic canopy management.
Why it matches plant phenotyping methods植物キャノピーの3D形状を取得・定量化する画像ベースの表現型解析パイプラインを開発し、実測葉寸法で精度検証しているため、方法が研究の中心である。
abstractThis study presents a 3D volumetric phenotyping pipeline integrating semantic mask generation with 3D Gaussian Splatting (3DGS) to quantify greenhouse cucumber canopy architecture.
Understanding the spatial structure of tree communities is fundamental for evaluating ecological interactions and management dynamics in agroforestry systems. However, the structural complexity and small spatial scale of traditional agroecosystems often limit the use of conventional remote sensing approaches. Recent advances in drone-based photogrammetry offer new opportunities to reconstruct the three-dimensional structure of vegetation at high spatial resolution and to quantify tree-level structural attributes. In this study, we applied aerial photogrammetry from unmanned aerial vehicles (UAVs) to characterize the spatial structure of agroforestry systems in traditional home gardens (THGs) in the Yucatan Peninsula, Mexico. The immediate neighborhood structure of the tree community of 20 THGs distributed along a south–north precipitation gradient was analyzed using two focal species as anchor references: Spondias purpurea and Annona muricata. High-resolution orthomosaics and three-dimensional point cloud models were generated to estimate structural attributes, including tree height, crown area, crown surface area, and canopy volume, which were combined with field measurements of diameter at breast height. Spatial indices describing aggregation, dominance, and neighborhood diversity were calculated to evaluate tree spatial organization and potential interaction patterns. The UAV-derived structural metrics revealed significant differences in canopy architecture across regions and between focal species. Regardless of the focal species, trees in the southern region exhibited greater height, crown diameter, and canopy volume than those in the northern region. Moreover, the spatial arrangement of tree communities also differed depending on which focal species was considered as the anchor, suggesting contrasting strategies of canopy dominance and spatial coexistence. Finally, our results validate the use of drone-based photogrammetry as an effective approach for capturing fine-scale spatial structure in complex agroforestry systems. By enabling detailed three-dimensional reconstruction of tree canopies, UAV remote sensing offers an affordable, simple approach to investigate neighborhood interactions, management effects, and structural dynamics in traditional agroecosystems that are difficult to assess using conventional field- or satellite-based methods.
Why it matches plant phenotyping methodsUAV空撮フォトグラメトリと3D点群から樹高・樹冠面積・樹冠表面積・林冠体積を推定し、その有効性も検証しており、植物形質取得法が研究の中心である。
abstractHigh-resolution orthomosaics and three-dimensional point cloud models were generated to estimate structural attributes, including tree height, crown area, crown surface area, and canopy volume
Accurate 3D crop monitoring underpins data-driven precision agriculture by enabling field-scale analysis of plant structure, growth dynamics, and management response. Modern 3D reconstruction methods perform strongly on generic benchmarks, but rendered appearance may not translate into metrically and agronomically useful geometry in crop fields. We introduce UAV3DCrop, a public benchmark of repeated multi-angle unmanned aerial vehicle (UAV) crop surveys. It contains 88,830 RGB images at $5280 \times 3956$ pixels, with a ground sampling distance of 3.6-5.8 mm, from 91 scenes spanning corn, soybean, wheat, and oat. Track A evaluates seven scene-optimized methods -- Neural Radiance Field (NeRF) and 3D Gaussian Splatting (3DGS) variants -- on held-out views, photogrammetry-referenced depth, and canopy-height recovery. Track B tests four pretrained feed-forward models on zero-shot camera-pose and geometry estimation. The scene-optimized methods rank differently across the three targets: Splatfacto-big leads appearance, whereas Scaffold-GS leads depth and is statistically tied with Splatfacto for canopy height. Among feed-forward models, MapAnything leads on seven of the eight metrics, while the remaining models vary more across crops and fail severely on absolute scale in a way that alignment conceals. Repeated acquisitions reveal further sensitivities that differ by output type and by model, associated with position within the acquisition sequence and with tie-point multiplicity. Current 3D reconstruction methods are therefore not yet interchangeable for agronomic use: no single method wins on appearance, geometry, and canopy height at once, and only one of four feed-forward models recovers usable metric scale. The dataset is publicly available at https://link-dev.github.io/UAV3DCrop/
Why it matches plant phenotyping methods植物キャノピー高さという明示的な形質を対象に、UAV 3D再構成手法をベンチマークし、公開データセットとして提供しているため、フェノタイピング手法が中心である。
abstractWe introduce UAV3DCrop, a public benchmark of repeated multi-angle unmanned aerial vehicle (UAV) crop surveys.
Reproduction assets foundThe paper introduces UAV3DCrop, a public benchmark of repeated multi-angle UAV crop surveys (88,830 RGB images, 91 scenes, four crops) with refined poses, photogrammetric depth references, and linked canopy-height and effective-LAI field measurements. The dataset is explicitly stated to be publicly available under CC BDataset · publiche acquisition sequence and with tie-point multiplicity. Current 3D reconstruction methods are therefore not yet interchangeable for agronomic use: no single method wins on appearance, geometry, and canopy height at once, and only one of four feed-forward models recovers usable metric scale. The dataset is publicly available at https://link-dev.github.io/UAV3DCrop/ .
Keywords:
UAV imagery; agricultural datasets; crop-field reconstruction; neural radiance fields;
Gaussian splatting; feed-forward geometry.
1 IntroductionOpen asset ↗UAV3DCroplines:1-90Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
Plant height is a key 3D phenotypic trait for assessing crop growth, biomass accumulation, and lodging resistance. To overcome the practical limitations of conventional plant height measurement methods, this study proposes a novel vision foundation model-based framework named Depth for Plant Height (Depth4PH) for plant height estimation in agricultural scenes using low-cost monocular RGB imaging. As part of our contributions, a synthetic–real coupled multimodal dataset was constructed by integrating Blender virtual agricultural scenes (Blender VAS) with real field images. Building upon the existing Depth Anything V2 foundation model, we developed a novel module called Transfer-based Agricultural Metric Depth Anything V2 (TAM-Depth V2) for absolute metric depth estimation through parameter-efficient fine-tuning, depth decoder reconstruction, and joint loss optimization. Furthermore, we designed a novel multi-source prompt-based segmentation framework, MSP-SAM2, to generate positive and negative prompts for zero-shot crop instance segmentation. Finally, a new inverse physical plant height estimation algorithm, RANSAC-Per, was introduced to estimate plant height by combining truncated percentile statistics with local RANSAC micro-plane fitting, thereby reducing the effects of depth noise and field microtopographic variation. The result showed that TAM-Depth V2 achieved stable absolute depth estimation, with an RMSE of 0.1162 m and an AbsRel of 4.25%. Compared to the original box-prompted SAM 2, MSP-SAM2 achieved a 4.4% improvement in mIoU, reaching 91.6% and a recall of 93.2%. On a 350-plant multi-crop test set, Depth4PH achieved R 2 = 0.948, RMSE = 12.23 cm, and MAE = 8.82 cm, and MAPE =10.15%, with crop-specific RMSEs ranging from 3.99 cm (cucumber) to 20.73 cm (maize), significantly outperforming the traditional Global-MinMax baseline (which had an RMSE of 22.62 cm). These results indicate that Depth4PH provides a promising foundational pathway for high-throughput crop phenotyping. With future optimization for edge deployment, it holds significant potential to support high-throughput monitoring in precision agriculture.
Why it matches plant phenotyping methods植物高の画像取得・深度推定・セグメンテーション・高さ抽出アルゴリズムを一体化した植物表現型計測フレームワークの開発と検証が中心であり、データセット構築と性能評価も含む。
abstractthis study proposes a novel vision foundation model-based framework named Depth for Plant Height (Depth4PH) for plant height estimation in agricultural scenes using low-cost monocular RGB imaging.
Annotation scarcity, poor model generalization and lagged data processing remain key bottlenecks hindering the practical deployment of phenotyping robots. To address these issues, we developed a novel phenotyping robot capable of online 3D reconstruction and zero-shot segmentation directly on the edge. Diverging from conventional semantic SLAM, our core contribution is RT-ZSDR, a framework featuring two key methodological novelties. First, we introduce the ForeCut pipeline for target extraction, which innovatively fuses DINO features with 3D geometric spatial information, leveraging multi-view semantic-spatial consistency to achieve annotation-free, zero-shot dense segmentation and reconstruction. Second, we designed a hardware-coupled loop closure strategy utilizing the robotic arm's kinematic feedback as prior constraints to significantly improve loop closure recall. Supported by edge computing Jetson Orin NX, the tracking and segmentation process takes approximately 0.24 s per frame after an initialization period of 1.82 s. RT-ZSDR's phenotypic measurements demonstrated strong correlations with reference baseline in both laboratory settings (n=90, PlantEye measurements as reference baseline; R 2 =0.990, 0.939, 0.725, and 0.861 for plant height, projected leaf area, surface area, and volume) and practical greenhouse environments (n=48, manual measurements as reference baseline; R 2 =0.965, 0.862 for plant height and stem diameter). Additionally, evaluated against COLMAP benchmarks (n=24), the system achieved a mean 3D reconstruction F1-score of 0.816.
Why it matches plant phenotyping methods植物フェノタイピングロボット向けに、ゼロショット分割・3D再構成・エッジ処理を開発し、植物形質を基準測定およびベンチマークと比較検証しており、取得・抽出手法が研究の中心である。
abstractwe developed a novel phenotyping robot capable of online 3D reconstruction and zero-shot segmentation directly on the edge.
Finger millet is an important cereal crop widely cultivated worldwide for food and fodder. Breeding programs aim to select genotypes with desirable architectural traits to develop new varieties with higher yields. In this effort, accurate high-throughput plant phenotyping is essential for accelerating crop improvement. To overcome the time-consuming and labor-intensive process of manual measurements, this study presents a comprehensive 3D imaging pipeline that leverages neural radiance fields (NeRF), 3D gaussian splatting (3DGS), and its advanced extensions (e.g., Feature 3DGS and Gaussian Grouping) to reconstruct, segment, and analyze finger millet yield component traits using multi-view 2D images. First, multiple-view RGB images of a single finger millet plant were captured, and COLMAP was then utilized to estimate the camera poses of the images and reconstruct the sparse point cloud, followed by advanced 3D reconstruction through 3DGS and NeRF. Second, feature 3DGS and gaussian grouping models were used to generate the 3D gaussian representation of finger millet panicles. This single-process framework enabled the generation of high-fidelity 3D point clouds and semantic feature fields without the need for expensive depth sensors or manual annotations. Our results demonstrated the effectiveness of these models in capturing morphological variations across different panicle phenotypes, including compact versus open panicle architectures. In addition, the 3D point clouds of the panicles were utilized to extract structural traits for yield prediction, achieving biologically meaningful correlations with grain productivity. This work highlights the potential of 3DGS-based phenotyping pipelines as a low-cost, near real-time, photorealistic solution for trait quantification, segmentation, and yield estimation in real-world agricultural settings.
Why it matches plant phenotyping methods3D画像再構成・セグメンテーション・形質抽出を統合した植物フェノタイピング手法の開発が中心であり、収量関連形質の定量と予測まで技術的に評価している。
abstractthis study presents a comprehensive 3D imaging pipeline that leverages neural radiance fields (NeRF), 3D gaussian splatting (3DGS), and its advanced extensions (e.g., Feature 3DGS and Gaussian Grouping) to reconstruct, segment, and analyze finger millet yield component traits using multi-view 2D images.
To address the challenges of insufficient sensor stability and poor consistency among multi-source data during crop phenotyping in unstructured field environments, this study develops a hardware-software co-optimization framework for wheat canopy sensing based on a four-wheel-drive, high-clearance phenotyping platform. A multi-sensor stabilization device integrated with an ESO-LQR control strategy suppresses pitch disturbances during motion. A ROS-based hierarchical framework coordinates LiDAR, cameras, and inertial sensors, while spatial calibration and timestamp-based software synchronization ensure spatiotemporal consistency. A tightly coupled LiDAR-IMU SLAM algorithm enables centimeter-level 3D reconstruction of farmland. To mitigate terrain effects, a two-stage ground point extraction method integrating verticality and spatial distribution features improves canopy height estimation. Field experiments demonstrate stable platform operation at 1.5 m s⁻¹ while maintain high efficiency and data quality, with a coverage efficiency of 95.2%, retained-point ratio of 85.3%, and an MTF of 0.35 for image clarity. Phenotypic evaluation shows strong agreement between predicted wheat plant height and manual measurements, with R² values of 0.898 and 0.729 and RMSE values of 1.30 cm and 1.64 cm at the jointing and grain-filling stages, respectively. Moreover, at the jointing stage, both the 2D green area index (GAI) and the 3D point-cloud-based canopy coverage exhibit strong consistency with ImageJ-derived results (R² = 0.873 and 0.910). These findings demonstrate that the proposed approach enables stable and efficient acquisition of crop phenotypic information in complex field environments, providing a solid technical foundation for digital field monitoring, data-driven crop management, and intelligent agricultural systems.
Why it matches plant phenotyping methods小麦キャノピーの表現型取得を目的としたマルチセンサープラットフォーム、データ同期・3D再構成・キャノピー高さ推定を開発し、手動測定やImageJとの一致性を検証しているため、方法が研究の中心である。
abstractthis study develops a hardware-software co-optimization framework for wheat canopy sensing based on a four-wheel-drive, high-clearance phenotyping platform.
Field / plotMRI / PETMultispectral / hyperspectralX-ray / CTRoot2D/3D reconstruction
Mycorrhizal fungi form essential symbiotic relationships with plant roots, facilitating nutrient exchange and promoting plant health. Understanding their interactions can benefit from advanced imaging techniques capable of visualizing nutrient exchange and structural colonization at subcellular resolution across large sample sizes. This review explores novel imaging approaches that are revolutionizing our understanding of root-mycorrhizal fungal symbioses. Several techniques can now visualize and characterize mycorrhizal fungi and associated root structures non-destructively and in three dimensions, for example X-ray computed tomography (micro-CT), X-ray fluorescence (XRF), and X-ray absorption near edge structure (XANES) spectroscopy. Metabolic processes and nutrient exchange can be tracked through positron emission tomography (PET), fluorescent nanoparticles (FNPs), and the monitoring of electrical signalling. Artificial intelligence (AI)-powered image processing software is enabling high-throughput analysis of complex images generated from a range of sources. Mycorrhiza systems are also able to be tracked in-field at multiple scales: hyperspectral imaging can detect mycorrhizal associations at the kilometre scale, while portable MRI imagers can detect changes at the tissue scale. These converging technologies enable the direct, continuous measurement of structural and metabolic root-mycorrhizal fungi interactions, paving the way for a mechanistic understanding of these vital symbiotic partnerships and their impact on plant health and ecosystem functioning.
Why it matches plant phenotyping methods植物根と菌根の構造・代謝・栄養交換を画像およびセンサーで直接測定する手法を扱うレビューであり、植物状態の取得技術が中心である。
abstractThis review explores novel imaging approaches that are revolutionizing our understanding of root-mycorrhizal fungal symbioses.
Background: Tropism, an adaptive growth mechanism often completely overlooked in tree phenotyping studies, is a crucial aspect of tree growth that allows them to reconfigure geometrically in relation to their immediate environment. This study introduces an integrated method to quantify tropic behaviour in plant phenotyping studies. Methods: The methodology combines cost-effective three-dimensional (3D) photogrammetric data capture from video, stem delineation techniques and 3D mathematical modelling of posture control for model-assisted identification of tropism traits. The proposed method was tested on a Pinus radiata D.Don. seedling subjected to a gravitational stimulus for 75 days. Stem posture was repeatedly measured using both 3D photogrammetry and fixed photography to create multitemporal 3D datasets and two-dimensional (2D) reference curves. Results: Individual 3D stem curves reconstructed with the proposed methodology introduced an error on spatial coordinates with a normalised RMSD ranging from 1.6 to 4.3% depending on time of capture, when compared with the 2D reference. The error for local tilt angle was higher than the error on spatial coordinates, with RMSD ranging between 5.6–12.2°, as expected for a first-order derivative. The gravitropic coefficient, capturing the sensing of and the reaction to local inclination by the plant, was underestimated by 2% if compared to the reference methodology. No contribution of autotropism (tendency to remain straight) was identified using the new methodology, but that contribution was found to be small using the 2D-approach and likely a key aspect of the gravitropic signature in the studied species. The major challenge with the proposed point cloud-based methodology arose from automated stem delineation. With dedicated algorithm enhancements to address stem occlusion in juvenile conifers and with more regular captures during plant motion, the proposed method could, however, perform identically to the 2D reference methodology. Overall, recovery of tropism traits performed equivalently whether using 2D or 3D data to fit the model of posture control. The minor discrepancies with experimental behaviour originated from fitting a simple kinematic model to complex real-world behaviour rather than data capture and digitising procedures. Conclusions: Overall, the proposed methodology, in its current form, offers a viable alternative to traditional 2D imagery methods at the cost of a small reduction in accuracy and capture time. The advantage of the 3D methodology is that it has the potential to track motion in multiple planes, whilst also measuring plant structure. With refinement, this methodology could be streamlined and adapted for deployment in field and operational environments at scale for phenotyping studies.
Why it matches plant phenotyping methods3Dフォトグラメトリ、茎の自動抽出、点群解析、姿勢モデルを統合し、植物の屈性形質を定量化・検証する方法が研究の中心である。
abstractThis study introduces an integrated method to quantify tropic behaviour in plant phenotyping studies.
Reproduction assets foundThe paper's data availability statement explicitly deposits the raw photogrammetric point clouds and derived stem curves on Figshare and the R stem-extraction pipeline code on GitHub, both with public URLs.Dataset · publicthe
Ministry of Business Innovation & Employment (MBIE)
New Zealand as part of the Tree Interactions Programme
(Catalyst Fund C09X1923).
Supplementary materials and data availability
The raw photogrammetric point clouds and the stem
curves derived from both photogrammetry and 2D
imagery can be found at the following repository:
https://doi.org/10.6084/m9.figshare.32248617. The
R code for the stem extraction pipeline is available
at https://github.com/Robin-hartley/tropism-stem-curves-3d
Hartley et al. New Zealand Journal of Forestry Science (2026) 56:11 Page 14Open asset ↗figshare · 10.6084/m9.figshare.32248617pdf-raw-page:14 lines:97-113Code · publicamme
(Catalyst Fund C09X1923).
Supplementary materials and data availability
The raw photogrammetric point clouds and the stem
curves derived from both photogrammetry and 2D
imagery can be found at the following repository:
https://doi.org/10.6084/m9.figshare.32248617. The
R code for the stem extraction pipeline is available
at https://github.com/Robin-hartley/tropism-stem-curves-3d
Hartley et al. New Zealand Journal of Forestry Science (2026) 56:11 Page 14Open asset ↗github · Robin-hartley/tropism-stem-curves-3dpdf-raw-page:14 lines:97-113Plant phenotyping relevance match · UnverifiedCrossref · checked 5 Sept 2026
Field / plotLiDAR / point cloudRGB / grayscaleRootStem / branchWhole plant / canopy / plot / fieldMorphology / geometry measurement2D/3D reconstructionVisualization / data managementArchitecture / morphology / geometry
Diameter at breast height (DBH) is a crucial indicator for obtaining tree phenotypes in orchard management, plantation monitoring, and agroforestry systems. LiDAR technology has high measurement accuracy, but it is costly and difficult to deploy flexibly in outdoor scenarios, while smartphones have emerged as a viable alternative due to their portability and low cost. In this paper, we propose a DBH estimation method based on monocular depth estimation, supported by a mobile application for algorithm deployment and result visualization. To address the limited computing resources on mobile devices, we design HR-DiffusionDepth, a lightweight diffusion-based monocular depth estimation network for smartphones, which generates pixel-wise 3D coordinates from a single image using camera intrinsics, thereby replacing LiDAR for DBH calculation. Experiments on the KITTI and SPREAD datasets show that HR-DiffusionDepth achieves the best depth estimation accuracy among similar lightweight models, reducing Abs Rel by up to 25.3% relative to the state-of-the-art (SoTA) lightweight baseline, with only 6.26 M parameters. The validation results show that the root mean square error (RMSE) of DBH estimation is 3.10 cm and the mean absolute error (MAE) is 2.25 cm, demonstrating the potential of this approach for agricultural scenarios such as orchards and plantations.
Why it matches plant phenotyping methodsスマートフォン画像と軽量深度推定ネットワークにより樹木DBHを推定する手法を開発・検証しており、植物形質取得が研究の中心である。
abstractwe propose a DBH estimation method based on monocular depth estimation, supported by a mobile application for algorithm deployment and result visualization.
Abstract. Urban vegetation is essential for mitigating the Urban Heat Island effect, yet its cooling performance depends on its three-dimensional structure. This study combines high-resolution Unmanned Aerial Vehicle - based LiDAR (Zenmuse L2) and thermal imaging (Zenmuse H20) to analyze vegetation structure and surface temperature across 4 urban parks in San Nicolás de los Garza, Mexico. LiDAR data were processed to generate Digital Terrain Model, Digital Surface Model and Canopy Height Model models, enabling the segmentation of individual trees and extraction of structural metrics such as canopy height, crown area and point density. Thermal orthomosaics were co-registered with LiDAR models to quantify temperature contrasts between vegetated and impervious areas. Results reveal consistent cooling effects in all parks, with vegetated zones showing 8–15 °C lower surface temperatures depending on canopy density and maturity. Larger parks with continuous canopies displayed the strongest thermal regulation. This integrated LiDAR–thermal approach provides a precise and scalable framework for assessing microclimatic benefits of urban vegetation, supporting climate-resilient planning in rapidly urbanizing regions.
Why it matches plant phenotyping methodsUAV LiDAR・熱画像を用いて個体樹木の樹冠高や樹冠面積などの植物構造形質を抽出する手法と統合ワークフローが中心であり、単なる環境測定ではない。
abstractLiDAR data were processed to generate Digital Terrain Model, Digital Surface Model and Canopy Height Model models, enabling the segmentation of individual trees and extraction of structural metrics such as canopy height, crown area and point density.
The present study validates a custom multi-sensor system for high-resolution, non-phenotyping. The photogrammetric workflow was optimised by evaluating image density, algorithms, and camera calibration. Beyond validation, a case study demonstrated the system’s capacity to monitor early plant development following biostimulant treatment. Technical assessment revealed that prior internal camera calibration was unnecessary for the optics used. A reduced dataset of 120 images yielded reconstruction accuracies statistically comparable to full 360-image sets ( p > 0.05), confirming potential for maximised throughput efficiency by reducing processing time by approximately 66% without compromising data integrity. Analysis demonstrated that algorithmic settings determined reconstruction accuracy. Active parameter tweaks were essential for maximising surface area precision across all plant species ( R 2 ≥ 0.98). Conversely, volumetric accuracy required deactivation of these tweaks to maintain mesh consistency. While surface area estimation remained robust, volumetric precision showed species-specific variability driven by morphological complexity. Baseline parameters for accurate plant health scaling were established by defining species-specific vegetation index ranges (e.g., 0.38–0.82 for C. sativus ). The platform’s robustness was validated through a longitudinal study evaluating four treatments: yeast autolysate (A), a fungal biostimulant (F), their combination (AF), and a control (C). The system captured distinct morpho-physiological responses, demonstrating that autolysate-based treatments (A and AF) significantly enhanced biomass growth. The developed multi-sensor system recorded surface area expansions of 122% and 102% relative to the control ( p < 0.001), alongside a 110% increase in biological height. Fidelity of these 3D reconstructions was substantiated by a strong correlation ( R 2 = 0.96) between the 3D-derived leaf area index and ground-truth measurements. A key innovation of the pipeline is the integration of vertical distribution metrics as descriptive statistical tools, enabling high-resolution characterisation of canopy architecture. The plant health status metric evidenced enhanced physiological resilience in variants A and AF. Gravimetric analysis corroborated the non-destructive findings, confirming significant increases ( p < 0.001) in dried shoot weight of 77% (A) and 79% (AF). The validated system decoupled structural biomass from physiological health, offering broad utility across diverse phenotyping tasks. Such functionality streamlines the valorisation of industrial by-products into biopreparations, driving progress in sustainable agriculture.
Why it matches plant phenotyping methodsフォトグラメトリとマルチスペクトル計測による植物表現型取得システムの最適化・技術検証が研究の中心であり、植物形態・生理状態の測定性能を評価している。
abstractThe present study validates a custom multi-sensor system for high-resolution, non-phenotyping.
Endosperm cavities within maize kernels influence quality traits such as kernel plumpness and hardness, serving as a key phenotypic indicator for assessing maize yield and quality. Research on endosperm cavities remains relatively scarce due to the small size of maize kernels and limitations in technical approaches. This study employed X-ray micro-computed tomography (μCT) three-dimensional reconstruction technology to extract morphological parameters and spatial configurations of endosperm cavities in multiple maize varieties, enabling visualisation and quantification of endosperm cavities within maize kernels. Endosperm cavities exhibit spatial heterogeneity within the kernels: embryo-adjacent cavities (EACs) are distributed in a conical pattern around the embryo, whereas internal endosperm cavities (IECs) are located in the floury endosperm at the tip region of the kernel and exhibit a boat-shaped morphology. The volume ratio of EACs to IECs is approximately 5:1. A coordinate system was established with the kernel length axis perpendicular to the horizontal plane, revealing the spatial positions of IECs (x = 3.5 mm, y = 2.1 mm, z = 1.1 mm) and EACs (x = 2.5 mm, y = 2.3 mm, z = 7.1 mm). Significant differences in endosperm cavity characteristics were observed among the different varieties. The average volume of the endosperm cavities was 4.1 mm 3 , with kernel porosities ranging from 0.4% to 3.3%. These parameters exhibited highly significant positive correlations with kernel volume, kernel thickness, cavity surface density, etc. Although manual sectioning methods cannot capture the 3D features of endosperm cavities, their operational simplicity and rapid data extraction allow them to reflect, to some extent, the characteristics of endosperm cavities across different maize varieties, as confirmed by this study. This study elucidates the morphology and spatial distribution of endosperm cavities, revealing significant varietal differences in cavity characteristics that correlate with grain morphological traits. These findings lay the groundwork for research into maize grain digital characterisation and the relationship between grain structure and function.
Why it matches plant phenotyping methodsトウモロコシ種子内の内胚乳空洞をX線マイクロCTで3次元可視化し、形態・空間配置・体積などの表現型を抽出・定量化することが研究の中心である。
abstractThis study employed X-ray micro-computed tomography (μCT) three-dimensional reconstruction technology to extract morphological parameters and spatial configurations of endosperm cavities in multiple maize varieties, enabling visualisation and quantification of endosperm cavities within maize kernels.
Blackberry micropropagation enables the rapid production of pathogen-free and genetically uniform plant material, although the evaluation of in vitro shoot development still relies on destructive and time-consuming measurements. This study investigated a low-cost smartphone-based 3D imaging approach for the non-destructive characterization of in vitro blackberry shoots (cultivar ‘Thornfree’) grown under different sucrose concentrations in the media (0, 7.5, 15, and 30 g L−1). Explants were cultured for 30 days under controlled environmental conditions in ventilated vessels containing 15 explants. Three-dimensional reconstructions generated using the viDoC RTK rover system coupled with an Apple iPhone 15 Pro Max were used to extract geometric traits, including shoot height, projected area, and shoot volume estimated through three complementary approaches, together with voxel-derived structural descriptors of shoot spatial organization and compactness. The proposed approach enabled the quantitative assessment of shoot architectural responses to sucrose availability, revealing differences in volumetric development and internal structural organization among treatments that would not be detectable by conventional measurements. The results highlight the potential of smartphone-based 3D phenotyping as a rapid, low-cost, and non-destructive tool for monitoring structural traits in micropropagated plant material and for supporting the optimization of in vitro culture conditions.
Why it matches plant phenotyping methodsスマートフォン由来の3D再構成を用いてシュート形態・構造形質を抽出する手法が研究の中心であり、非破壊植物フェノタイピングへの実質的応用である。
abstractThree-dimensional reconstructions generated using the viDoC RTK rover system coupled with an Apple iPhone 15 Pro Max were used to extract geometric traits, including shoot height, projected area, and shoot volume estimated through three complementary approaches, together with voxel-derived structural descriptors of shoot spatial organization and compactness.
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 ↗
Field / plotNeRF / 3D Gaussian SplattingThermalWhole plant / canopy / plot / field2D/3D reconstructionVisualization / data managementPlant / canopy temperature
Abstract. Urban trees provide critical ecosystem services in dense city environments, yet current workflows for monitoring their thermal behaviour remain confined to 2D desktop-based analysis with no three-dimensional spatial context or field-deployable visualization capability. This paper presents a complete pipeline for in-situ 3D thermal mesh visualization of urban trees in Augmented Reality (AR), combining Thermal InfraRed (TIR) image acquisition, Gaussian Splatting-based mesh reconstruction, quantitative validation, and mobile AR deployment. TIR images of a Tilia tomentosa acquired with a FLIR T560 camera are preprocessed with a standardized false-colour palette and fed into the MILo (Mesh-In-the-Loop Gaussian Splatting) framework to reconstruct a thermally attributed 3D mesh. Geometric evaluation against a Z+F IMAGER 5016 TLS reference using the M3C2 algorithm demonstrates that MILo recovers 13.5 times more canopy geometry than traditional multi-view stereo under thermal imagery, with a standard deviation of 4.0 cm. A colourmap inversion procedure recovers per-vertex temperature estimates from the GS-derived mesh colours, yielding a mean absolute difference of 0.7°C against direct T-Cam measurements (thermal camera mounted on the laser scanner), within the combined instrument accuracy of both sensors. The resulting thermal Gaussian Splat was deployed in a custom Android AR application supporting hybrid marker-based and GPS-based spatial anchoring for in-situ visualization. These results demonstrate the technical feasibility of GS-based thermal reconstruction and mobile AR as a medium for communicating three-dimensional canopy thermal information to educators and urban forestry practitioners.
Why it matches plant phenotyping methods都市樹木の葉冠温度と3D形状を取得・可視化する熱画像ベースの再構成パイプラインを開発し、TLSおよび熱カメラとの定量検証まで行っており、植物フェノタイピング手法が研究の中心である。
abstractThis paper presents a complete pipeline for in-situ 3D thermal mesh visualization of urban trees in Augmented Reality (AR), combining Thermal InfraRed (TIR) image acquisition, Gaussian Splatting-based mesh reconstruction, quantitative validation, and mobile AR deployment.
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.
Precision agriculture increasingly relies on detailed structural information, such as canopy height and canopy volume, to enhance crop health monitoring and operational safety. However, existing methods based on costly LiDAR or RGB-D sensors are often impractical for large-scale deployment in dynamic and unstructured horticultural environments. Furthermore, conventional 2D segmentation and SLAM-based pipelines typically generate sparse, geometrically inconsistent semantic maps which are insufficient for actionable structural analysis in agricultural applications. To overcome these limitations, we propose a monocular 3D structural mapping framework tailored for horticultural plants via semantic scene completion. At inference, the proposed model takes a single RGB image as input and predicts voxel-wise geometry and semantics, from which task-oriented structural maps, including canopy height, canopy volume, and obstacle-aware traversability layers, are derived. Specifically, we first introduce a Depth-Aware Decoder Module that explicitly recovers depth in the spatial domain and fuses 2D-to-3D features, thereby mitigating depth ambiguity and reducing reliance on accurate pose. Second, an NCS-Guided Geometry Encoder is designed to inject normalized depth into voxel positional embeddings, enabling self-attention to perform global relational modeling within a depth-aware geometric coordinate system. In addition, a Global Encoder is utilized to refine local structural details, while an occupancy head produces the final 3D semantic completion outputs. We construct a horticultural 3D semantic scene dataset using an RGB-D sensor, which serves as a benchmark for evaluating our method, while the deployed model remains RGB-only. Extensive quantitative and qualitative experiments are conducted on both the Semantic-KITTI dataset and our dataset. On our dataset, the method achieves 82.31% occupancy IoU, 84.26% mIoU, and 86.25% precision. Beyond voxel-level evaluation, manual field measurements further show canopy height MAE values of 0.019-0.026 m and canopy volume proxy relative errors of 8.4%-11.4%. These results demonstrate the effectiveness of our approach in real-world agricultural scenarios, providing actionable structural insights for crop monitoring and autonomous robotic operations.
Why it matches plant phenotyping methods単眼RGB画像から植物の樹冠高・樹冠体積などの構造形質を推定する3Dフェノタイピング手法を開発し、データセット構築と実測検証も行っているため、方法が研究の中心である。
abstractwe propose a monocular 3D structural mapping framework tailored for horticultural plants via semantic scene completion.
Traditional manual measurement of garlic bulb phenotypic traits is inefficient, subjective, poorly reproducible, and may cause sample damage. To improve the adaptability of three-dimensional reconstruction to garlic bulb morphology and grading-related parameter extraction, this study developed a non-destructive phenotypic measurement workflow based on multi-view image-based three-dimensional reconstruction. Four garlic materials with distinct bulb morphologies and epidermal characteristics were used to demonstrate the feasibility of the reconstruction workflow, and 40 Lanling white-skinned garlic bulbs were used for quantitative accuracy validation. Multi-view images were acquired using a high-resolution camera, a motorized turntable, and a controlled illumination system. Three-dimensional models were reconstructed using ContextCapture, and the resulting point clouds were processed in CloudCompare through cropping, denoising, downsampling, and pose correction. Maximum longitudinal diameter, maximum transverse diameter, and volume were extracted from the processed point clouds according to GB/T 45244-2025 (Grades and Specifications of Garlic) and validated against manual reference measurements. The coefficients of determination for maximum longitudinal diameter, maximum transverse diameter, and volume were 0.9935, 0.9909, and 0.9924, respectively, with RMSE values of 0.0529 cm, 0.0520 cm, and 0.8874 cm3, and MAPE values of 0.6647%, 0.7765%, and 1.9149%. Additional MAE, bias, confidence interval, and Bland–Altman analyses further supported the agreement between model-derived and manual reference measurements. These results demonstrate the feasibility of multi-view image-based three-dimensional reconstruction for non-destructive garlic bulb phenotypic measurement and provide a methodological basis for future grading-related assessment and three-dimensional phenotyping of bulbous horticultural crops.
Why it matches plant phenotyping methodsニンニク球の形態形質を非破壊的に取得する3D画像計測ワークフローを開発し、手動測定と定量検証しており、フェノタイピング手法が中心である。
abstractthis study developed a non-destructive phenotypic measurement workflow based on multi-view image-based three-dimensional reconstruction.
Unmanned aerial vehicle (UAV) photogrammetry offers a cost-effective approach to tree-level detection, however, Structure-from-Motion (SfM) outputs are sensitive to processing choices and site conditions, which can alter canopy representation and reduce individual-tree detection accuracy. Here, we systematically evaluate how SfM reconstruction quality and depth-map filtering influence RGB-only individual-tree detection under controlled acquisition conditions. Objectives were to (i) identify an optimal SfM-derived point-cloud configuration for delineating individual trees, and (ii) implement and test a segmentation workflow (local-maxima treetop detection plus Dalponte2016 in lidR) for detecting and counting trees. We assessed RGB-only SfM for individual-tree detection (ITD) across thirteen 1.21-ha loblolly pine ( Pinus taeda ) plots located in two counties in the state of Alabama in the southeastern United States; eight even-aged plantations and five mixed pine-hardwood stands, while holding image acquisition parameters constant. Using Agisoft Metashape Professional (Agisoft LLC, St. Petersburg, Russia), dense-cloud quality (Lowest, Low, Medium, High, Ultra High) and depth-map filtering (Disabled, Mild, Moderate, Aggressive) were varied in a 5 × 4 full-factorial design; assessment metrics included point-cloud density, canopy-surface completeness, canopy-height-model (CHM) agreement with field heights, and ITD precision/recall/F1. We identified a single high-resolution configuration (Ultra High + Disabled) by screening parameter sets for structural accuracy and suppression of false peaks. Using this configuration, CHMs matched field heights in Washington County, Alabama (R 2 = 0.96; RMSE = 0.44 m; bias = − 0.01 m) and in Cullman County, Alabama (R 2 = 0.44; RMSE = 1.14 m; bias = − 0.09 m); pooled performance was R 2 = 0.98; RMSE = 0.54 m; bias = − 0.01 m. ITD accuracy at the primary 3 m match radius yielded a precision of 0.03; recall = 0.29; F1 = 0.05 in the even-aged plantations (Washington) and a precision of 0.03; recall = 0.12; F1 = 0.05 in mixed pine–hardwood stands (Cullman); pooled F1 = 0.05. The selected parameters and workflow are reproducible and transferable, provide insight into RGB-SfM ITD performance, and indicate when lidar remains preferable for crown delineation.
Why it matches plant phenotyping methodsRGB-SfMによる個体樹の検出・樹高推定と、SfM設定およびセグメンテーションワークフローの系統的評価が研究の中心であり、植物の樹冠構造・樹高という形態形質を抽出する方法を検証している。
abstractwe systematically evaluate how SfM reconstruction quality and depth-map filtering influence RGB-only individual-tree detection
Reproduction assets foundThe paper's Code availability statement deposits the authors' SfM/ITD processing scripts publicly on OSF (DOI 10.17605/OSF.IO/UXBCZ). Phenotype/field datasets are only available on request, so they are not public assets.Code · publicThe workflow and processing scripts used in this study are publicly available through the Open Science Framework
(OSF) repository: Singh and Narine, [32]. Code Repository for Optimizing SfM Parameters for RGB-Only Individual-Tree
Detection in Loblolly Pine and Mixed Pine-Hardwood Stands. https://doi.org/10.17605/OSF.IO/UXBCZ.Open asset ↗10.17605/OSF.IO/UXBCZpdf-page:12 lines:1-70Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 11 Sept 2026
Abstract Purpose of Review Ground-based 3D point cloud technologies, including static terrestrial laser scanning (TLS), mobile laser scanning (MLS), and close-range photogrammetry, are increasingly used for estimation of aboveground vegetation biomass as they provide detailed structural representations across vegetation types; however, a comprehensive synthesis of how point-cloud data are translated into biomass estimates remains lacking. This review evaluates current approaches, performance patterns, and methodological gaps in biomass estimation using 3D ground-based point clouds. Recent Findings We systematically reviewed and analyzed 160 research articles (comprising 171 device-specific studies) published until the end of 2025 (first appearing in 2010). Research was dominated by tree-based applications (74%), with limited attention to shrubs, grasslands or crops. TLS was the prevailing acquisition technology (78%), although MLS adoption is growing. Biomass estimation primarily relied on allometric equations, volume-based reconstructions (e.g., quantitative structure models, voxelizations, convex hull), and parametric regression models. Reported model performance was generally high in tree- and shrub-based studies (median R 2 > 0.8), but more variable in non-woody vegetation types. Despite rapid advances in 3D sensing, point-cloud-native deep-learning approaches remain rarely implemented in biomass estimation workflows. Summary Ground-based 3D sensing is maturing technically, yet methodological heterogeneity persists. Many workflows still depend on destructive calibration data, semi-manual preprocessing, and non-standardized modelling strategies, limiting reproducibility and cross-study comparability. Multi-sensor integration is emerging but lacks consistent upscaling frameworks. Future research should expand coverage of underrepresented vegetation types, promote standardized and automated processing pipelines, and systematically evaluate point-cloud-native deep learning architectures, both for extracting structural proxies and for assessing their capacity to estimate biomass directly.
Why it matches plant phenotyping methods3Dセンシングによる植物バイオマス推定手法を体系的にレビューし、取得技術、推定ワークフロー、性能、再現性、標準化を評価しており、表現型測定法が中心である。
abstractThis review evaluates current approaches, performance patterns, and methodological gaps in biomass estimation using 3D ground-based point clouds.
Reliable ecological indicators of mangrove structure and carbon storage are essential for monitoring coastal ecosystem conditions, yet their accuracy remains uncertain in tall, structurally heterogeneous forests, where Earth observation products differ in sensor physics, spatial resolution, and acquisition dates. Here, we present a multi-scale framework to evaluate, calibrate, and improve two widely used ecological indicators of mangrove condition—canopy height and aboveground biomass (AGB)—across approximately 7000 ha of mangroves in the Marapanim estuary, northern Brazil. The framework integrates UAV photogrammetry, radar-derived digital elevation models (TanDEM-X and SRTM), and field measurements to quantify cross-scale discrepancies and identify the main sources of uncertainty affecting indicator retrieval. High-resolution UAV canopy-height models revealed exceptionally tall Avicennia forests reaching up to 53 m, among the tallest mangroves reported globally. At the local scale, mean AGB reached approximately 648 Mg ha −1 in the southern Avicennia -dominated sector and 430 Mg ha −1 in the northern mixed Rhizophora–Avicennia sector, with local maxima of ∼800 Mg ha −1 . In contrast, radar-derived products yielded substantially lower estimates of canopy height and biomass, with height differences of 8–10 m in tall and structurally heterogeneous stands. These discrepancies reflect the combined effects of sensor-dependent canopy representation, spatial averaging, and temporal mismatch between historical radar acquisitions and recent UAV observations. To improve the ecological interpretation of these products, we implemented a calibration strategy linking field and UAV measurements to satellite observations and complemented it with UAV-based three-dimensional volumetric reconstruction of individual trees as an independent structural check on allometric biomass estimates. Our results show that canopy height and AGB derived from coarse-resolution radar products can systematically underestimate mangrove structural condition and carbon storage in tall forests unless locally calibrated. Beyond documenting exceptionally tall and carbon-dense Amazonian mangroves, this study provides a transferable framework for evaluating and improving ecological indicators of forest structure and biomass in complex coastal ecosystems.
Why it matches plant phenotyping methodsUAV photogrammetry・レーダー・現地測定を統合し、マングローブの樹冠高と地上部バイオマスという植物形質の推定を評価・較正・改善する方法論が研究の中心である。
abstractHere, we present a multi-scale framework to evaluate, calibrate, and improve two widely used ecological indicators of mangrove condition—canopy height and aboveground biomass (AGB)—across approximately 7000 ha of mangroves in the Marapanim estuary, northern Brazil.
Hyperspectral images contain richer spectral and spatial information than multispectral images, yet traditional equipment suffers from limitations such as bulky size and complex data processing. This study constructs a task-oriented near-infrared (NIR) hyperspectral reconstruction and detection framework for the precise detection of early apple bruises. First, apple samples were collected using a hyperspectral imaging system. The Weight Extremum Method was employed to screen seven characteristic wavelengths (976.4 nm, 1064.8 nm, 1175.8 nm, 1192.5 nm, 1295.4 nm, 1449 nm, and 1631.6 nm), which were further reduced to three key wavelengths (1064.8 nm, 1175.8 nm, and 1449 nm). Based on the datasets constructed from these bands, the HSGAN framework was used as the reconstruction backbone to reconstruct hyperspectral images ranging from 866 nm to 1701 nm. Results demonstrated that reconstruction performance was optimal with seven input bands (PSNR = 37.81, SSIM = 0.973) and remained favorable with three bands (PSNR = 34.70, SSIM = 0.950). Finally, YOLOv11n was used to detect bruises on both original and reconstructed images. Detection accuracy using reconstructed spectra from the 7-band input approached that of the original images (mAP50 = 0.994), while the 3-band input also maintained high precision (mAP50 = 0.992, Recall = 0.993). These results demonstrate that reconstructing 254 NIR bands from just three characteristic wavelengths is feasible. This framework significantly reduces data acquisition costs while enabling high-precision early bruise detection, offering a practical solution for agricultural quality control.
Why it matches plant phenotyping methodsリンゴ果実の打撲（植物器官の状態）を対象に、少数波長画像からNIRハイパースペクトル画像を再構成し、検出性能を評価する画像・計算フェノタイピング手法が研究の中心である。
abstractThis study constructs a task-oriented near-infrared (NIR) hyperspectral reconstruction and detection framework for the precise detection of early apple bruises.
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.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Introduction: Plant phenotyping requires accurate and repeatable three-dimensional structural information, but practical acquisition conditions in greenhouses, seedling rooms, and indoor pot experiments often include complex backgrounds, handheld motion blur, and thin leaf structures. These factors reduce the robustness of conventional three-dimensional reconstruction methods and limit their use in low-cost and automated phenotyping. Methods: To address this problem, this paper proposes F2DMAS, an automated three-dimensional plant phenotyping workflow using consumer-grade smartphone videos. The workflow first converts multiview RGB videos into image sequences and removes motion-blurred frames through frequency-domain quality filtering. A frequency-spatial plant segmentation module, termed FSAM3, is then introduced to separate plant structures from complex backgrounds without task-specific annotated training data. The segmented image sequences are further reconstructed using 2D Gaussian Splatting, followed by TSDF-based meshing, scale recovery, and virtual measurement for extracting plant height, canopy width, leaf length, and leaf width. Results: Experiments were conducted on 15 plant species under two acquisition scenarios. The proposed workflow achieved stable plant reconstruction under non-ideal background conditions, with PSNR, SSIM, and LPIPS values of 31.09, 0.9711, and 0.0365, respectively. Compared with the baseline reconstruction workflow, F2DMAS substantially reduced the processing time for mesh extraction while improving reconstruction quality. The extracted phenotypic traits showed strong agreement with manual measurements, with R² values ranging from 0.90 to 0.99, RMSE values ranging from 0.64 to 1.21 cm, and MAPE values ranging from 4.50% to 9.73%. Discussion: These results indicate that F2DMAS can provide an end-to-end workflow from smartphone video acquisition and plant segmentation to three-dimensional reconstruction and phenotypic trait extraction. The proposed method offers a practical and deployable solution for greenhouse seedling cultivation, potted plant experiments, and low-cost three-dimensional plant phenotyping.
Why it matches plant phenotyping methodsスマートフォン動画から植物の3D構造を再構成し、複数の形態形質を抽出・検証するワークフロー自体が中心的な方法論的貢献である。
abstractThe extracted phenotypic traits showed strong agreement with manual measurements, with R² values ranging from 0.90 to 0.99
Abstract Background Accurate modeling of wildland fuelbeds requires knowledge of not only where fuels are in three-dimensional (3D) space but also what they are. In this study, we introduce an object-based scanning protocol designed to generate detailed three-dimensional mesh models of individual fuel particles (e.g., seedlings, shrubs, litter, and cones) using an industrial-grade laser scanner. While traditional terrestrial laser scanning (TLS) or photogrammetric approaches tend to require objects to be segmented from broader-scope environment-level point clouds, our approach begins with the object itself. Results By scanning discrete plant parts in controlled conditions and capturing their morphology, surface area, and volume at sub-millimeter precision, we create a methodological foundation for fuel characterization that is structurally explicit and ecologically specific. We also propose a flexible workflow to adapt the scanning process for the extensive natural range of variation in fuel object structures, classifying individual objects based on their structural complexity. Conclusions Digital twins of wildland fuel plants and particles serve as building blocks for future integration with machine learning techniques to improve wildland fuelbed classification and simulation. Our approach shifts the basis of 3D fuels modeling from environmental scanning toward object-driven understanding with implications for fire behavior, emissions, and ecological modeling.
Why it matches plant phenotyping methods個別の植物・植物部位をレーザースキャンし、形態・表面積・体積を抽出するオブジェクト中心の3D計測プロトコル自体が研究の中心であり、植物形態のフェノタイピング手法に該当する。
abstractwe introduce an object-based scanning protocol designed to generate detailed three-dimensional mesh models of individual fuel particles (e.g., seedlings, shrubs, litter, and cones) using an industrial-grade laser scanner.
Three-dimensional surface reconstruction is essential for accurately acquiring the external quality parameters of watermelons, such as size, volume, and defect area. Binocular stereo vision provides a low-cost and easily deployable solution for the single-view 3D reconstruction of watermelons. However, watermelons present highly similar surface textures, and as typical spheroid-like objects, the excessive angle between surface normals of edge regions and the camera optical axis leads to insufficient feature representation. Consequently, directly applying existing stereo matching algorithms often introduces matching ambiguities, and lightweight networks struggle to balance real-time performance with matching accuracy. This study focuses on the high-precision single-view point cloud generation of Kirin watermelons. To address these issues, we first construct a cross-modal, high-precision Kirin watermelon stereo matching dataset. Building upon the Fast-ACVNet+ architecture, we then propose MI-ACVNet, a lightweight stereo matching network tailored for high-precision watermelon point cloud acquisition. In the feature extraction stage, a Multi-Scale Stereo Feature Extraction (MSFE) module is adapted. By incorporating the re-parameterized network MobileOne and Epipolar-Enhanced Coordinate Attention (E2CA), MSFE improves the discriminative capability for weak and similar textures without compromising inference speed. For cost computation, a Coarse-to-Fine Cascaded Residual Correction (C2F-CRC) strategy is incorporated to construct a fine-grained cost volume via sub-pixel interpolation, enhancing the network’s ability to capture subtle surface fluctuations. Furthermore, a Semantics-Guided Region-Aware Loss (SGRA-Loss) is formulated, leveraging semantic masks to apply differentiated supervision weights across edge, center, and background regions to significantly improve edge matching accuracy. Ablation studies validate the effectiveness of the MSFE, C2F-CRC, and SGRA-Loss components. Compared to the baseline model, the full MI-ACVNet reduces the End-Point Error (EPE) by 19.5% and the Bad-0.5 error rate by 34.5% in the watermelon region. Furthermore, when compared against five mainstream algorithms (StereoNet, AANet, HSMNet, LightStereo-L, and NMRF-swint), MI-ACVNet achieves state-of-the-art performance: EPE and Bad-0.5 are reduced to 0.091 pixels and 1.159%, respectively, with a single-frame inference time of only 46 ms. The average depth error of the reconstructed point clouds is merely 0.26 mm. By ensuring both real-time efficiency and high-precision depth estimation, this method demonstrates promising potential for deployment in industrial Kirin watermelon sorting lines, driving sorting equipment toward higher precision and intelligence.
Why it matches plant phenotyping methodsスイカのサイズ・体積・欠陥面積などの外部形質を取得するためのステレオ画像再構成手法を開発し、データセット構築、アブレーション、既存手法比較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。
abstractThree-dimensional surface reconstruction is essential for accurately acquiring the external quality parameters of watermelons, such as size, volume, and defect area.
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.
Practical three-dimensional (3D) phenotyping in large-scale orchards with repetitive row structures remains challenging, and systematic evidence comparing both accuracy and acquisition efficiency under outdoor conditions remains limited. This study presents a field-deployable evaluation framework and implements it in a 2-ha commercial Japanese pear orchard trained under a joint V-trellis system. Using a terrestrial laser scanner (TLS) as the reference, we evaluated two handheld LiDAR systems (a low-cost SLAM-based system and a high-performance system), structure from motion / multi-view stereo (SfM/MVS) reconstructions from three camera platforms (a digital camera, an action camera, and a 360° camera), and 3D Gaussian splatting (3DGS) constructed from action-camera video. Measurements were taken at two spatial scales to capture scale-dependent effects. In the span-scale survey (4 m), location error was derived from TLS-referenced target coordinate differences, and reconstruction error was quantified using cloud-to-mesh distances with cubic targets. In the row-scale survey (one tree row), positional stability during continuous mapping was evaluated as location error. Operational metrics (acquisition time, data volume, and processing effort) were also documented. The results demonstrate clear trade-offs among the methods: LiDAR enables rapid wide-area acquisition but is susceptible to cumulative drift in row-structured environments, whereas SfM/MVS provides superior geometric fidelity at the cost of increased time and data volume. Although 3DGS is less suitable for precise quantitative measurement, it demonstrates strong potential for intuitive visualization of orchard structure and fruit distribution. These findings highlight the need for staged, purpose-specific, and seasonally adaptive strategies for orchard-scale digital twin development.
Why it matches plant phenotyping methods果樹園における複数の3D取得法を比較・評価し、樹体構造や果実分布の定量的取得精度、安定性、運用性を検証しており、植物フェノタイピング手法が中心である。
abstractPractical three-dimensional (3D) phenotyping in large-scale orchards with repetitive row structures remains challenging
Spaceborne laser scanning (SLS) presents a cost-effective means for frequent, global-scale monitoring of forest ecosystem parameters. Compared to airborne laser scanning (ALS), SLS offers substantially greater spatial coverage and revisit frequency, but at the cost of larger footprints, sparser sampling, and attenuated return signals. These constraints typically result in a loss of fine-scale vertical canopy structure in large-footprint waveform LiDAR, thereby limiting the retrieval of ecologically meaningful forest structural metrics. To address this challenge, we developed an encoder–decoder Transformer architecture to reconstruct high-resolution vertical canopy profiles from large-footprint waveform LiDAR observations. Using waveform data acquired by NASA’s Land, Vegetation, and Ice Sensor (LVIS) – a high-altitude ALS instrument commonly used as a proxy for spaceborne missions – we trained the model to recover fine-scale canopy structure by leveraging overlapping ALS point clouds as reference data. The proposed Transformer leverages long-range vertical dependencies within waveform signals to infer canopy structural details that are degraded or unresolved in large-footprint, high-altitude observations. Results show that the proposed approach substantially improves the agreement between LVIS-derived and ALS-derived canopy structural complexity metrics, increasing correlations from R = 0.62 to 0.84 and from R = 0.76 to 0.90 for two representative metrics. This framework is readily transferable to current and future SLS missions, enabling the retrieval of super-resolved vertical canopy profiles and supporting large-area assessment of ecologically meaningful canopy structural metrics.
Why it matches plant phenotyping methodsLiDAR波形から植物キャノピーの垂直構造プロファイルを再構成するTransformer手法の開発と検証が研究の中心であり、植物構造形質を推定している。
abstractwe developed an encoder–decoder Transformer architecture to reconstruct high-resolution vertical canopy profiles from large-footprint waveform LiDAR observations.
Reproduction assets foundThe paper's data availability statement provides two paper-specific public assets: the complete codebase including the best-performing Transformer model checkpoint on GitHub, and the preprocessed LVIS waveforms with corresponding ALS reference canopy profiles on Zenodo. Both are directly used for this paper's canopy-geCode · publicoach could help extend ALS-like structural characterization to broader
734 spatial extents sampled by spaceborne laser scanning.
735 Data and code availability
736 The complete codebase for training and implementing the proposed encoder–decoder
737 Transformer, including the best-performing model checkpoint, is available at
738 https://github.com/tahriribraq/Transformer-waveform-reconstruction. The preprocessed
739 LVIS waveforms and corresponding ALS reference profiles used in the study are
740 available at https://doi.org/10.5281/zenodo.21154804.
741 Acknowledgements
742 This work was supported by the National Aeronautics and Space Administration’s
743 (NASA) Decadal Survey Incubation (DSIOpen asset ↗https://github.com/tahriribraq/Transformer-waveform-reconstructionpdf-layout-page:38 lines:1-48Plant phenotyping relevance match · UnverifiedOpenAlex · checked 8 Sept 2026
Early-generation sesame (Sesamum indicum L.) breeding requires high-throughput phenotyping of large unreplicated populations across contrasting environments. A DJI Phantom 4 Multispectral UAV was flown at 40, 80, and 120 m above ground level (AGL) over 588 M2 genotypes under full irrigation (ENV1) and terminal drought (ENV2; irrigation withheld from reproductive onset) on four dates (July–September 2025). Structure-from-motion canopy height models were compared with ground measurements, and four spectral reflectance indices—Normalised Difference Vegetation Index (NDVI), Normalised Difference Red Edge (NDRE), Green Normalised Difference Vegetation Index (GNDVI), and Leaf Chlorophyll Index (LCI)—were derived from 40 m imagery. Ordinary least squares (OLS), Random Forest, and Gradient Boosting were evaluated under leave-one-genotype-out (LOGO), leave-one-environment-out (LOEO), and leave-one-date-out (LODO) cross-validation; genotypic repeatability was quantified by intraclass correlation (ICC), and drought performance was ranked by a composite Stress Tolerance Index (STI) validated against an independent breeder assessment. The 40 m altitude gave the highest height accuracy (R2 = 0.812 in ENV1; 0.663 in ENV2). LOGO accuracy (R2 ≈ 0.83) fell to R2 ≈ 0.55 under LODO—the operationally relevant figure for a new phenological stage—and the full structural–spectral OLS model collapsed (R2 = −0.203) where tree ensembles remained stable. Spectral-index repeatability was up to ~2-fold higher under stress (ICC(3,4) > 0.84). The composite STI flagged 38 elite genotypes (7.6% of 498); 10 of its top 30 were confirmed in the breeder’s 48-best selection from all 588 rows—a 4.1-fold enrichment over chance (hypergeometric p = 4.5 × 10−5).
Why it matches plant phenotyping methodsUAV画像から草冠高・スペクトル形質を抽出し、飛行高度、予測モデル、再現性、交差検証を体系的にベンチマークしているため、植物表現型取得法が中心である。
abstractEarly-generation sesame (Sesamum indicum L.) breeding requires high-throughput phenotyping of large unreplicated populations across contrasting environments.
NeRF / 3D Gaussian SplattingLiDAR / point cloudLeafWhole plant / canopy / plot / field2D/3D reconstructionGrowth / time-series analysisTrackingArchitecture / morphology / geometryGrowth / development / phenology
Quantifying plant growth dynamics from sparse longitudinal 3D observations is fundamental for agriculture and plant sciences. Yet, plants pose unique challenges: they undergo intricate non-rigid deformations, exhibit changing topology as new organs emerge, and often lack explicit temporal correspondences between consecutive data acquisitions due to newly formed tissue. Methods designed for general scenes struggle to model topology changes and asynchronous organ growth characteristic of plants. To address these challenges, we introduce GrowFields, a compositional dynamic neural field representation for organ-aware 4D plant growth modelling from point cloud time series. Our approach decomposes a plant into its constituent organs and aligns each organ into its own canonical coordinate frame, isolating intrinsic growth patterns from global plant motion. We then learn a shared continuous neural deformation field that models temporal dynamics across all organs, conditioned on learnable per-organ latent codes capturing organ identity and growth characteristics. The resulting modular yet unified representation naturally accommodates the asynchronous development of plant organs while remaining grounded in the practical setting of organ-level plant tracking. We evaluate GrowFields on growth sequences from four plant species, assessing geometric fitting and organ tracking accuracy using manually annotated leaf-tip trajectories. Results demonstrate consistent improvements in spatial precision, temporal coherence, and morphological fidelity over a range of existing representations.
Why it matches plant phenotyping methods植物の3D時系列観測から器官追跡と成長動態を抽出する、オルガン認識型4Dニューラルフィールド手法の開発・評価が中心である。
abstractwe introduce GrowFields, a compositional dynamic neural field representation for organ-aware 4D plant growth modelling from point cloud time series.
Computed tomography enables non-destructive phenotyping of fruit internal structure but traditionally requires hundreds of projections, limiting throughput. Under sparse-view conditions, conventional and learning-based methods both suffer from streaking artifacts and regional distortions that degrade trait quantification. This study present CitrusGS, an integrated framework that achieves high-fidelity 3D reconstruction and precise morphological phenotyping of citrus fruit from only 15 projections using radiative 3D Gaussian splatting. Our method employs sparse-point initialization, optimized loss composite, and dual-stage pruning to suppress artifacts while preserving anatomically critical details with significantly higer convergence efficiency. In the citrus fruit datasets, CitrusGS achieves 29.78 dB PSNR and 0.870 SSIM, outperforming corresponding baseline method by 1.58 dB and 0.067 in SSIM, and enables automated extraction of ten external and internal phenotypic traits with R 2 larger than 0.944. Moreover, the framework shows initial zero-shot transferability across pathological citrus samples and additional horticultural specimens without retraining. By reconciling acquisition efficiency with anatomical fidelity using low-cost X-ray hardware, CitrusGS provides a promising framework for high-throughput, non-destructive phenotyping in breeding and grading applications.
Why it matches plant phenotyping methods柑橘果実の疎視野CT再構成法を開発・検証し、内部・外部形質を自動抽出するフェノタイピングが中心である。
abstractThis study present CitrusGS, an integrated framework that achieves high-fidelity 3D reconstruction and precise morphological phenotyping of citrus fruit from only 15 projections using radiative 3D Gaussian splatting.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicOur codes are available at https://github.com/Petrichoror/CitrusGS .Open asset ↗Petrichoror/CitrusGSlines:325-387Plant phenotyping relevance match · UnverifiedOpenAlex · arXiv · checked 5 Sept 2026
3D plant phenotyping is notoriously known to be procedure-complicated and of low throughput due to the extensive multi-view imaging, the fragile 3D reconstruction pipeline, and the additional cost from reconstructed geometry to phenotypic extraction. These limitations are further amplified in low-cost data acquisition, where smartphone videos or sparsely sampled multi-view images provide limited view overlap and self-occlusion. In this work, we show that the conventional 3D plant phenotyping pipeline could be streamlined and significantly accelerated with 3D Foundation Models (3DFMs), and particularly, present one of the first cross-crop 3D phenotyping frameworks powered by 3DFMs. The framework replaces COLMAP-style sparse initialization with 3DFM-based feed-forward geometric recovery, combines geometry-constrained 3D Gaussian Splatting for dense reconstruction, enables few-view reconstruction through iterative view synthesis and refinement, and converts reconstructed geometry into measurable organs through 2D-to-3D semantic transfer, metric scale recovery, and organ instance separation. We further construct a cross-crop dataset with smartphone-based image acquisition, diverse plant morphologies, and manual annotations for segmentation and phenotypic evaluation. Experiments across 26 plant sequences show that 3D Foundation Models reduce the average reconstruction time from 6.52 minutes to 1.58 seconds while maintaining high reconstruction quality and phenotyping accuracy. These results suggest a fresh technical route for high-throughput 3D plant phenotyping, from low-cost image acquisition to fast reconstruction, perception, scale recovery, and phenotypic measurement.
Why it matches plant phenotyping methods3D画像再構成から器官分離・形質測定までを統合した高速植物フェノタイピング手法を開発し、複数作物・データセットで性能評価しているため。
abstractpresent one of the first cross-crop 3D phenotyping frameworks powered by 3DFMs
Arbuscular mycorrhizal fungi (AMF) contribute to plant nutrient and water uptake via their extraradical hyphal networks. However, in situ methodologies to quantify architectural and morphological traits of these networks in soil are largely lacking, limiting our understanding of AMF-mediated resource transport. Using synchrotron-based X-ray computed microtomography (micro-CT), we established a workflow to cultivate, noninvasively image, and quantitatively analyze AMF hyphosphere and rhizosphere structures in the interaggregate space across two soil textures and biological contexts. We developed a pipeline for quantitative three-dimensional (3D) assessment of key architectural and morphological traits including structure counts, hyphal length, branching frequency, volume, and surface area. Our method further permits (1) measurement of AMF-soil and AMF-root interface areas and (2) microscale quantification of pore space occupancy by AMF. Micro-CT offers a tool for noninvasively visualizing AMF in air-filled soil pore space. We outline how such quantitative 3D information can be incorporated into image-based and functional-structural soil-plant models, thereby supporting a better mechanistic understanding of AMF-mediated processes in soils and plants.
Why it matches plant phenotyping methods土壌中のAMF構造を対象に、micro-CT撮像と3D画像解析パイプラインを開発し、菌糸の形態・構造形質を定量化する方法が研究の中心であるため。
abstractin situ methodologies to quantify architectural and morphological traits of these networks in soil are largely lacking
The objective of this study was to develop a 3D plant modeling strategy that enables camera pose recovery from segmented plant images and the reconstruction of an initial point cloud. A lightweight, contour-aware framework leverages the view-consistent and surface-oriented representation of 2D Gaussian Splatting, making it suitable for plant surface reconstruction under the Plant-to-Camera mode. A contour-weighted Laplacian regularization suppresses depth discontinuities around plant boundaries, while simplified Gaussian primitives improve computational efficiency without compromising geometric fidelity. Organ-level semantics are integrated into the reconstructed geometry to distinguish plant organs such as leaves, stems, and ears. On maize and wheat datasets, our method outperformed existing approaches in terms of morphological fidelity, organ-level structural consistency, and processing speed, demonstrating its suitability for plant phenotyping
Why it matches plant phenotyping methods植物器官の3D再構成と形態情報抽出を目的とする計算手法を開発し、既存法と形態忠実度・器官構造整合性・処理速度で比較評価しており、フェノタイピング手法が中心である。
abstractThe objective of this study was to develop a 3D plant modeling strategy that enables camera pose recovery from segmented plant images and the reconstruction of an initial point cloud.
Accurate perception and 3D reconstruction of fruit tree branch structures are fundamental to smart orchard development, with broad applications in intelligent harvesting, crop phenotyping, and precision management. However, the slender and highly branched morphology, multi-scale distribution, weak surface texture, and severe occlusion inherent to fruit tree branches pose substantial challenges to high-fidelity modeling. This paper systematically reviews advances in branch feature extraction and 3D reconstruction for fruit tree canopies. A structured literature search was conducted using the Web of Science, Scopus, and Google Scholar databases, with search terms including “fruit tree branch”, “point cloud reconstruction”, “3D canopy modeling”, “branch feature extraction”, and “agricultural robotics”. Studies published between 2000 and 2025 were considered, with inclusion criteria requiring relevance to branch structure perception, reconstruction accuracy, or orchard application; non-peer-reviewed sources and studies lacking quantitative evaluation were excluded. We trace the evolution of feature extraction from classical 2D image processing and geometric fitting, through point cloud segmentation and skeleton extraction, to modern deep learning approaches and multimodal perception techniques. For 3D reconstruction, we compare active and passive sensing strategies alongside both explicit and implicit scene representation methods, discussing their respective strengths and applicable scenarios. A five-dimensional evaluation framework is also proposed, encompassing geometric accuracy, structural consistency, feature stability, computational efficiency, and generalization capability. Finally, we identify key bottlenecks in fine-grained structure recovery, occlusion handling, and cross-scene generalization, and highlight future directions in structural prior integration, multimodal collaborative modeling, and lightweight neural representations—offering a structured reference for advancing 3D perception research in smart orchards.
Why it matches plant phenotyping methods果樹の枝構造の特徴抽出と3D再構成を対象とする、植物形態計測・表現型取得手法のレビューであり、方法論が中心です。
abstractThis paper systematically reviews advances in branch feature extraction and 3D reconstruction for fruit tree canopies.
To overcome the inefficiency and subjectivity of manual seedling surveys, this study presents a unsupervised framework for evaluating maize sowing quality and emergence uniformity via UAV-based remote sensing. Centimeter-level multispectral imagery was captured to reconstruct 3D point clouds using SfM and MVS techniques. At the algorithmic level, an improved unsupervised pipeline was developed: the Otsu method was employed for plant segmentation, followed by a Fourier Transform to extract 2D spatial frequency features for precise crop row identification and automated spacing measurement. Subsequently, the Combined Entropy Uniformity (CEU) index was developed using Shannon entropy, and a proxy for canopy closure (CCP) was derived using a porosity model, thereby enabling the simultaneous relative quantification of seedling height consistency, spatial distribution uniformity, and canopy geometric structure variability. At the application level, the framework was validated through field trials involving 19 precision planters of diverse configurations. Performance was assessed using indices such as qualified spacing, miss-sowing, and the Coefficient of Variation of Plant Spacing (PSCV). Results indicate that: (1) Vacuum-type planters exhibited optimal stability at speeds of 7–9 km/h, achieving an average qualified spacing rate of 76.7% and a PSCV of approximately 24%, whereas finger-pickup planters were more sensitive to seed size variation and mechanical vibration. (2) The results from the Generalized Additive Model (GAM) suggest a possible nonlinear relationship between seeding rate and certain uniformity indices, indicating that appropriately adjusting operational parameters could help balance operational efficiency and seeding quality; however, this trend requires further validation with larger sample sizes and repeated observations. (3) Point cloud CEU metrics and canopy structure proxies based on the Gap Fraction model showed statistical correlations with certain manually collected indicators, indicating that this method has the potential for rapid screening of seeding quality and relative evaluation of seedling population structure at the field scale under the current experimental conditions.
Why it matches plant phenotyping methodsUAV画像・3D点群から作物の出芽、草丈均一性、空間分布、群落構造を抽出する解析ワークフローを開発し、19種のプランターで検証しており、植物表現型取得法が中心である。
abstractthis study presents a unsupervised framework for evaluating maize sowing quality and emergence uniformity via UAV-based remote sensing.
Electron Tomography is a widely used 3D imaging tool for biological specimens because it offers higher resolution than optical imaging and greater accessibility than X-ray sources. While transmission electron microscopy (TEM) tomography can offer advantages for thin specimens under well-controlled imaging conditions, imaging thick, amorphous specimens becomes increasingly challenging due to reduced transmission and loss of usable contrast at large thicknesses. Alternatively, bright-field scanning transmission electron microscopy (BF-STEM) tomography, due to improved dose control and tolerance to multiple scattering, is preferred for thick samples, as it can image samples thicker than 400 nm while maintaining sufficient resolution and signal [1][2]. However, BF-STEM tomography of sheet-like laminar specimens remains strongly limited by incomplete tilt ranges, and the mismatch between conventional linear reconstruction algorithms and the underlying nonlinear image-formation physics limits reconstruction quality. In this work, we employ a physics-informed automatic differentiation (PIAD) based limited-angle tomography framework that uses a multislice TEM forward model to approximate the BF-STEM bright-field contrast, motivated by the reciprocity between TEM and STEM image formation in thick, amorphous specimens. By formulating reconstruction as a physics-informed inverse problem, the proposed approach enables stable three-dimensional recovery from severely limited angular data [3]. Comparisons with weighted back-projection (WBP) reconstruction demonstrate a substantial reduction of missing-wedge artifacts and improved morphological consistency across slices, highlighting the potential of PIAD-based multislice modeling for interpretable 3D reconstruction from BF-STEM data in regimes where conventional tomography fails. The physical basis for this improvement arises from the multislice model, which explicitly accounts for nonlinear multiple scattering in thick specimens. We chose multislice TEM, a simple plane-wave propagation, as our forward model for BF-STEM image formation, because the BF-STEM contrast can be approximated as the incoherent angular average of TEM multislice intensities over the probe convergence aperture [4]. We further simplify this weighted incoherent sum by noting that, for thick amorphous specimens, the dominant contributions arise from low-angle (near-zero-angle) components, as higher-angle components are preferentially scattered outside the bright-field acceptance. Under this assumption, BF-STEM image formation is well approximated by a blurred BF-TEM plane-wave multislice output, which we adopt as an effective forward model for thickness-dominated bright-field contrast in limited-angle tomography. To test this framework experimentally, we image a butterfly wing scale (Bicyclus anynana) with nanoscale features on a laminar sheet with lateral dimensions of a few hundred microns [5]. We acquired a tilt series of 35 BF-STEM images between -51° and 51°. In limited-angle reconstructions, conventional WBP reconstructions fail to resolve cross-rib structures and are affected by anisotropic smearing and missing-wedge artifacts (Fig. 1a & 1d). In contrast, the proposed PIAD reconstruction yields improved crossrib continuity and junction definition in both volume rendering (Fig. 1b) and orthogonal slices (Fig. 1c’ &1d’), enabling more interpretable 3D morphology of the crossrib architecture. To assess the predictive capability of the forward model, we perform a leave-one-out validation in which a single BF-STEM projection (Fig. 2a) is excluded from the reconstruction. The resulting volume is then forward-projected at the held-out angle using the TEM multislice model and compared with the unseen experimental projection. Despite this effective approximation for BF-STEM, the predicted projections capture the dominant contrast trends associated with the crossrib network (Fig. 2a-2b). The residual map shows differences in the cross-rib edges, as expected from blur, yet has an RMSE of 0.08 and a Pearson correlation coefficient of 0.86. The back-propagated loss gradients highlight spatial regions that are well constrained by the data. Although the cross-ribs are more clearly resolved in PIAD reconstructions, the lower lamina remains unresolved in both WBP and PIAD reconstructions due to the absence of projections near 90°; future PIAD reconstructions using a laminography geometry may address this limitation. Importantly, the present results demonstrate that PIAD reconstructions using an approximate forward model that accounts for multiple scattering outperform conventional linear projection methods for BF-STEM tomography under limited-angle acquisition. Physics-informed auto-differentiation improves recovery of crossrib morphology under limited-angle acquisition in BF-STEM tomography. a) Conventional weighted back-projection (WBP) reconstruction from a single-axis BF-STEM tilt series, showing preservation of coarse rib geometry but loss and anisotropic smearing of thin crossrib features (red dashed region). b) PIAD reconstruction using a TEM multislice forward model improves cross-rib continuity and reduces missing-wedge artifacts. The inset shows a schematic of the actual scale structure. (c,d) Representative xy- and xz-slices from the WBP reconstruction. (c′,d′) Corresponding slices from the PIAD reconstruction, demonstrating enhanced cross-rib definition and thickness consistency. Scale bar = 300 nm. Leave-one-out projection validation of a TEM multislice forward model for BF-STEM tomography. One BF-STEM projection (a) is excluded from reconstruction, and the resulting volume is forward-projected at the held-out angle using the TEM multislice model. The simulated projection (b) is compared with the unseen experimental projection after fitting a per-projection gain and offset (c), (RMSE 0.08; Pearson correlation 0.86). (d) Back-propagated loss gradients (projection alone z-axis) from the leave-one-out error localize data-supported regions of the volume, including crossrib features, distinguishing them from underconstrained regions dominated by missing-wedge artifacts. Scale bar = 500 nm.
Why it matches plant phenotyping methodsBF-STEMトモグラフィー画像から蝶翅の三次元形態を復元する物理情報型手法を開発し、従来法との比較およびleave-one-out検証を行っており、植物ではないが動物試料のため本インデックスの対象外です。
X-ray / CTRootMorphology / geometry measurement2D/3D reconstructionRoot system architecture
A dual-substrate X-ray CT platform using high-contrast media and modular scanning overcomes traditional resolution and size limits to enable large-scale, high-resolution 3D in situ root phenotyping.
Why it matches plant phenotyping methods作物根系のin situ表現型測定を目的とするX線CTプラットフォームの開発であり、取得・解析手法が研究の中心である。
abstractA dual-substrate X-ray CT platform using high-contrast media and modular scanning overcomes traditional resolution and size limits to enable large-scale, high-resolution 3D in situ root phenotyping.
Understanding elemental distributions in plants is critical for agricultural productivity, nutritional quality, and limiting the transfer of toxic elements into the food chain. Conventional elemental mapping techniques typically require thin sectioning or complex tomographic reconstructions, making three-dimensional analysis labor-intensive and destructive. Here, we present a non-destructive approach for three-dimensional elemental mapping in intact plant tissues using confocal X-ray fluorescence (XRF) microscopy with a collimating channel array (CCA) [1]. This method defines a localized 3D detection volume within the sample, enabling the generation of elemental virtual cross-sections without physical sectioning while preserving native spatial relationships. We demonstrate the capability of this technique by mapping Fe distributions in carrot (Daucus carota) roots and shoots and Cd distributions in root tips of near-isogenic wheat (Triticum aestivum) lines. Integration of multiple virtual sections enabled three-dimensional reconstructions that reveal distinct Cd translocation pathways between accumulating and non-accumulating wheat lines, tracing elemental movement from the epidermis through cortical layers into vascular tissues. The method is applicable to diverse plant morphologies, including cylindrical roots and irregular leaf and stem tissues. This approach enables high-sensitivity, non-destructive 3D elemental imaging, providing a powerful tool for studying elemental transport in plants with direct relevance to crop breeding, food safety, and agricultural sustainability [2].
Why it matches plant phenotyping methods植物組織内の元素分布という生理状態を、非破壊3D XRFで取得・再構成する手法の開発と植物試料での実証が中心である。
abstractHere, we present a non-destructive approach for three-dimensional elemental mapping in intact plant tissues using confocal X-ray fluorescence (XRF) microscopy with a collimating channel array (CCA) [1].
In recent years, automation of electron microscopy has enabled high-throughput acquisition of large, high-resolution serial-section image volumes. Three-dimensional reconstruction of these data has become essential for visualizing fine structural details in biological and material samples [1]. However, despite rapid advances in automated data acquisition, image analysis remains a major bottleneck. Conventional image processing methods, such as gray-level thresholding and manual annotation, require extensive labor and suffer from reduced reproducibility due to operator subjectivity [2]. To establish a highly efficient three-dimensional measurement workflow from imaging to quantitative analysis, we applied machine-learning (AI) to the most challenging image-analysis step and benchmarked its effectiveness against conventional methods. We prepared serial sections of Chlamydomonas for this evaluation. Continuous serial-section SEM images were acquired using a Hitachi High-Tech scanning electron microscope equipped with Auto Capture for Array Tomography (ACAT) and a focused ion beam scanning electron microscope (FIB-SEM) [3,4]. We performed appropriate sample pretreatment and optimized imaging conditions to clearly visualize the target chloroplast structures, followed by the automatic acquisition of continuous serial-section SEM image stacks. The obtained images were processed by cropping regions of interest, aligning images, adjusting contrast, and applying filters to facilitate structural identification. For segmentation and three-dimensional reconstruction, both a conventional method combining thresholding and manual correction [2] and a machine-learning-based approach (AIVIA, Leica Microsystems) trained on annotated data were employed [5]. In Figure 1(b), the region selected by the conventional method is shown in blue. Regions with contrast resembling that of the U-shaped chloroplast in Chlamydomonas were also selected. In contrast, the deep-learning-based method automatically extracted multi-scale features, such as intensity (gray-level), edges, and curvature, from the annotated regions and classified pixels individually. This facilitated the extraction of chloroplast regions, even in images containing structures with similar contrast (Figure 1(c)). The three-dimensional images reconstructed from the automatically segmented regions (Figures 2(a) and 2(b)) confirmed the presence of large openings and multiple micropores in the chloroplasts. Only 10 out of 60 annotation slices were required, significantly reducing manual annotation time compared to the conventional method. High reproducibility was also achieved in three-dimensional measurements. Furthermore, we acquired continuous serial-section SEM images of HIPS resin and an aluminum alloy using FIB-SEM and performed three-dimensional reconstruction combined with machine-learning-based segmentation. This presentation shows that integrating automated image acquisition with deep-learning-based segmentation streamlines the workflow from acquisition through three-dimensional reconstruction. It presents quantitative evaluation results and demonstrates the method’s utility for high-throughput three-dimensional analysis [7]. Comparison of chloroplast segmentation in Chlamydomonas. (a)An SEM image acquired using an FE-SEM equipped with ACAT, (b) the regions segmented using a threshold-based method, (c) the regions segmented using AIVIA. Three-Dimensional reconstruction of a Chlamydomonas chloroplast. (a) Three-dimensional reconstruction of the chloroplast obtained through automatic segmentation, (b) The same chloroplast viewed from a different orientation. The large opening and multiple micropores are found at the positions indicated by the white arrows.
Why it matches plant phenotyping methodsChlamydomonasの chloroplast 構造を対象に、機械学習セグメンテーションと3D再構築を従来法と比較・評価しており、植物構造の定量的取得ワークフローが研究の中心である。
abstractTo establish a highly efficient three-dimensional measurement workflow from imaging to quantitative analysis, we applied machine-learning (AI) to the most challenging image-analysis step and benchmarked its effectiveness against conventional methods.
SUMMARY Plants display complex structural tissue arrangements and cell shapes that are intimately related to their functionality and whose precise geometry influences the metabolic and physical processes performed by different organs. Analyzing these structure–function relationships requires accurate information on the complex 3D anatomy and its changes over time at meaningful spatial resolution. A non‐invasive approach, micro‐CT imaging, can produce such 3D or 4D datasets and can be leveraged for finite element (FE) simulations of mechanical and physical processes. This combination of techniques has been employed to study biomechanical properties, gaseous diffusion, light propagation, hydraulics, and thermodynamic processes in plant organs. A deep understanding of structure–function relationships also paves the way to design bio‐inspired structures using plant anatomy as a reference. Here, we illustrate how the combination of micro‐CT‐based imaging and FE modeling can be leveraged in plant science for advanced investigation of structure–function relationships.
Why it matches plant phenotyping methods植物器官の3D/4D構造をmicro-CTで取得し、有限要素モデルと組み合わせて構造・機能特性を解析する方法を中心に扱うレビューであり、植物フェノタイピング手法に該当する。
titleQuantification of plant structure–function relationships through micro‐ CT imaging‐based finite element modeling
Continuous monitoring of canopy temperature (Tc), a key indicator of crop water-heat stress and physiological dynamics, using unmanned aerial vehicle (UAV) imagery is inherently limited by temporal discontinuity and the limited physical realism of purely data-driven models. This study develops a physics-informed neural network (PINN) framework to transform temporally sparse UAV thermal observations into continuous hourly rice Tc reconstruction and 48 h forecasting products. The model leverages sparse UAV thermal measurements as supervisory signals while integrating them with continuous meteorological forcing and daily UAV-derived crop phenotypic features. Validated through a comprehensive season-long rice field experiment using walk-forward cross-validation, the proposed PINN framework demonstrated superior performance. It achieved R 2 values of 0.92 for reconstruction and 0.90 for forecasting, with RMSE of 0.71 °C and 0.82 °C, respectively. Ablation analysis further showed that crop phenotypic variables contributed more strongly than temporal descriptors, reducing predictive uncertainty by approximately 4.8–14.3 %, while the integration of SEB physical constraints and uncertainty modeling improved R 2 by 8.4–9.5 % and reduced Total STD by 28.4–37.7 %. The model successfully captures diurnal dynamics, spatial variability, and canopy thermal hysteresis while maintaining physical consistency through improved energy closure. This framework bridges sparse aerial observations with continuous physiological monitoring and highlights its potential to support precision irrigation, early stress detection, and high-throughput phenotyping in smart agriculture.
Why it matches plant phenotyping methodsUAV熱画像による疎な観測からイネ群落温度を連続再構成・予測するPINNを開発し、交差検証とアブレーション分析で性能評価しており、表現型取得・推定手法が研究の中心である。
abstractThis study develops a physics-informed neural network (PINN) framework to transform temporally sparse UAV thermal observations into continuous hourly rice Tc reconstruction and 48 h forecasting products.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Field / plotLiDAR / point cloudWhole plant / canopy / plot / fieldMorphology / geometry measurement2D/3D reconstructionSegmentationStress / disease detectionArchitecture / morphology / geometryGrowth / development / phenology
Three-dimensional (3D) crop phenotyping is increasingly used to capture crop structure, but its value for crop protection is conditional rather than automatic. 3D approaches are operationally justified only when reconstructed geometry adds decision-relevant information beyond simpler 2D, spectral, scalar, or conventional baselines. This review examines 3D crop phenotyping through a reconstruction-trait-task-maturity framework for crop protection and synthesizes evidence across disease assessment, pest and stress interpretation, pesticide dose adjustment, spray deposition, weed-target perception, protection-oriented breeding, and digital-twin development. The literature is organized through four connected lenses: reconstruction routes that generate crop geometry, 3D traits that may alter protection reasoning, decision pathways that link traits to intervention variables, and maturity levels that distinguish static 3D models, validated phenotypic traits, process-coupled systems, protection outputs, and outcome-updated decision twins. The strongest decision-facing evidence currently comes from canopy-based dose adjustment, deposition prediction, drift reduction, and related spraying applications in which 3D traits are linked to intervention variables and field-facing comparators. Disease, stress, and architecture-aware modelling provide important but more heterogeneous evidence, while many point-cloud datasets, segmentation pipelines, neural reconstruction methods, and agricultural digital-twin frameworks remain upstream of practical crop-protection decisions because they do not yet connect 3D measurements to validated protection labels, comparator baselines, decision thresholds, intervention outputs, or outcome updating. A central conclusion is that high-fidelity 3D representation should not be conflated with decision-twin maturity. Protection-oriented digital twins require explicit coupling among synchronized crop geometry, functional or epidemiological models, decision rules, and recorded field outcomes. This review therefore identifies the evidence and reporting priorities needed to move 3D crop phenotyping toward validated, deployment-oriented, and feedback-aware crop-protection support.
Why it matches plant phenotyping methods3D作物フェノタイピングの再構成、形質抽出、検証、デジタルツイン成熟度を中心に扱うレビューであり、植物フェノタイピング手法が中核。
abstractThis review examines 3D crop phenotyping through a reconstruction-trait-task-maturity framework for crop protection and synthesizes evidence across disease assessment, pest and stress interpretation, pesticide dose adjustment, spray deposition, weed-target perception, protection-oriented breeding, and digital-twin development.
Background Cell geometry plays a central role in determining division orientation and body axis formation during early embryogenesis in Arabidopsis thaliana . However, quantitative analysis of dynamic three-dimensional (3D) morphology remains challenging because live-imaging studies often rely on two-dimensional (2D) projections, while existing 3D reconstruction approaches, including mesh-based methods, often lose the original orientation information relative to the ovule and require labor-intensive mesh correction. In addition, embryo positional fluctuation caused by floating in liquid medium and continuous growth makes it difficult to analyze temporal morphological changes within a common coordinate system. Results We developed a robust framework for quantitative 3D and four-dimensional (4D; 3D + time) analysis of embryo initial cell (apical cell) morphology. The method first establishes a standardized 3D coordinate system by normalizing cell orientation based on the bottom plane and the optical axis of the observation. Cell morphology is then reconstructed through ellipse-based approximation of serial cross-sections extracted from stacked imaging data, enabling accurate geometric characterization without the need for complex surface mesh reconstruction. To evaluate shape anisotropy, we quantified the apical cell shape in 3D. The framework further supports the characterization of volumetric features of subsequent division, providing a basis for quantifying 3D embryogenesis. Conclusion Our framework provides a simple and noise-reduced approach for quantitative analysis of living cell morphology in 3D. We named the integrated method of combining coordinate normalization with elliptical cross-section-based reconstruction Apical3DTip. This method enables consistent comparison of cell shapes without extensive manual corrections. The method overcomes key limitations of 2D projection-based and mesh-dependent analyses and offers a practical platform for quantifying cell shape and daughter cell shapes in 3D. More broadly, it provides a quantitative foundation for exploring the relationship between cell geometry, morphodynamics, and developmental patterning in living plant embryos.
Why it matches plant phenotyping methods植物胚の細胞形態を3D・4D画像から定量化する再構成手法を開発しており、表現型取得・抽出法が研究の中心である。
abstractWe developed a robust framework for quantitative 3D and four-dimensional (4D; 3D + time) analysis of embryo initial cell (apical cell) morphology.
Reproduction assets foundThe paper's Methods availability statement explicitly deposits the Apical3DTip analysis code and associated datasets on two public GitHub repositories (main implementation and ImageJ plugin). These are paper-specific author assets for the 3D/4D apical cell reconstruction and phenotyping analysis. No separate phenotype/Code · publicctor of the fitted vertical plane:
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Because the fitted plane passes through the centroid s, the offset e was calculated as
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Then, the fitted plane was represented as
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Availability of data and materials
The code for Apical3DTip, along with all associated datasets, is available on Github:
https://github.com/blues0910/Apical3DTip.
Apical3DTip is also available as an ImageJ plugin:
https://github.com/YusukeKimata-Moo/Apical3DTip.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Funding
This work was supported by a Japan Society for the PromotOpen asset ↗https://github.com/blues0910/Apical3DTippdf-layout-page:12 lines:1-49Code · publictroid s, the offset e was calculated as
N
MQ.
Then, the fitted plane was represented as
O G N O
MQ 0.
Availability of data and materials
The code for Apical3DTip, along with all associated datasets, is available on Github:
https://github.com/blues0910/Apical3DTip.
Apical3DTip is also available as an ImageJ plugin:
https://github.com/YusukeKimata-Moo/Apical3DTip.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Funding
This work was supported by a Japan Society for the Promotion of Science (JSPS) KAKENHI
Grant (No. JP22K15135 to H.M., JP25H01809 to Y.K., JP26K02023 tOpen asset ↗https://github.com/YusukeKimata-Moo/Apical3DTippdf-layout-page:12 lines:1-49Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Crop ideotype breeding aims to design plant architectures that enhance yield and resource use efficiency. Accelerating this process demands a framework for assembling accurate three-dimensional (3D) architecture. This Perspective synthesizes advances in technologies and methodologies in 3D architectural phenotyping. Rapid progress has enabled translating these advances into tangible gains in breeding efficiency. To this end, we propose integrating functional-structural plant models as an overarching framework that optimizes plant architecture combinations, shifting breeding from experience-driven to predictive ideotype design. Convergence of 3D phenotyping, plant modeling and artificial intelligence holds transformative potential to accelerate breeding cycles, enhancing productivity, sustainability, and food security.
Why it matches plant phenotyping methods3D植物形態フェノタイピング技術・方法論を中心に統合し、機能構造モデルやAIとの連携を論じる方法論的Perspectiveである。
abstractThis Perspective synthesizes advances in technologies and methodologies in 3D architectural phenotyping.
Melon (Cucumis melo L.) is a globally significant horticultural crop, characterized by high nutritional value and substantial commercial status. However, frequent outbreaks of powdery mildew severely threaten its yield and fruit quality. Current early detection methods primarily focus on detached leaf assays, which often lack sufficient model generalization. This study proposes a temporal 3D multispectral point cloud reconstruction method for melon plants by integrating multispectral imaging with 3D reconstruction technology. An Artificial Neural Network (ANN) model for 3D spatial light field distribution was developed based on a hemispherical white reference to achieve precise reflectance calibration of the multispectral point clouds. Post-calibration, the coefficient of variation (CV) for the spectral reflectance of the hemispherical reference in 3D space was reduced to less than 2.4%. On this basis, an early classification model for melon powdery mildew was constructed using Partial Least Squares Discriminant Analysis (PLS-DA) based on the mean reflectance spectra of individual plant point clouds. The results demonstrate that the average recognition accuracy reaches 85.94% from 4 days post-inoculation onwards, enabling disease early warning three days in advance. This research provides critical theoretical support and technical reference for the non-destructive early monitoring and precision smart plant protection of crops in facility agriculture.
Why it matches plant phenotyping methodsメロン個体の病徴状態を対象に、時系列3Dマルチスペクトル点群の再構成・反射率校正と早期病害分類を開発しており、植物表現型取得手法が中心である。
abstractThis study proposes a temporal 3D multispectral point cloud reconstruction method for melon plants by integrating multispectral imaging with 3D reconstruction technology.
A comprehensive intelligent system for corn yield prediction based on the synergy of a Spatio-Temporal Generative Adversarial Network (ST-cGAN) and a recurrent CNN-LSTM architecture has been developed and tested. A multimodal fusion of weather-independent Sentinel-1 radar data, ERA5-Land meteorological factors, and historical Sentinel-2 optical observations was applied. The problem of "biochemical blindness" in radar signals, where radar captures physical plant structure but fails to detect photosynthetic activity, and cloud cover limitations in optical remote sensing was successfully resolved. To achieve this, an early fusion strategy was implemented. The ST-cGAN simultaneously processes current SAR data for the structural macro-state, historical optical data for the last known biochemical baseline, and a 10-day history of meteorological priors to enforce physiological constraints. Consequently, if radar indicates high biomass but meteorological data reveals severe drought, the neural network mathematically recognizes biological stress and synthesizes proportionally depressed NDVI and NDRE indices, preventing the hallucination of falsely healthy crops. Dynamic synthesis of missing NDVI and NDRE vegetation indices was conducted under prolonged continuous cloud cover (up to 21 days), achieving a high structural similarity index (SSIM = 0.89). Temporal discriminators for frame sequence analysis were introduced into the architecture, improving the edge-preserving index by 18 % and minimizing spatial artifacts at field boundaries. Pixel-level regression was performed for a 15,000-hectare test area in the Western Forest-Steppe of Ukraine based on reconstructed time series covering 15 critical phenological stages. It was established that the proposed architecture reduces the root mean square error (RMSE) to 0.48 t/ha with a coefficient of determination (R2) of 0.90. Statistical analysis proved the significant superiority of the developed method over industry-standard gap-filling algorithms (e.g., STARFM). A scalable decision-support system for optimizing harvest logistics and financial planning under any atmospheric conditions was presented. Future research directions involving neural network knowledge distillation for IoT devices and UAV data integration were outlined.
Why it matches plant phenotyping methods雲下で欠測するNDVI・NDREなど植物状態指標を再構成する深層学習手法の開発と、SSIM・RMSE・R2および既存手法との比較検証が中心であり、単なる収量予測ではなく植物表現型推定手法に該当する。
abstractDynamic synthesis of missing NDVI and NDRE vegetation indices was conducted under prolonged continuous cloud cover (up to 21 days), achieving a high structural similarity index (SSIM = 0.89).
Tomographic microscopy enables three-dimensional internal imaging but often requires expensive optical or X-ray instrumentation. Here we present an ultra-low-cost continuous-wave diffusive tomography (CWDT) system for biological samples. The system uses a smartphone microscope, a white LED coupled into an optical fiber, 3D-printed micropositioners, and a physics-based forward model optimized with machine learning. We demonstrate full-color volumetric reconstructions from a tartrazine-cleared poplar section, a scattering phantom, fungal mycelium near an Arabidopsis root, and thick poplar branch imaging with an inserted side-emitting fiber. The current results are qualitative and exploratory, but they show that scanned fiber illumination and inexpensive hardware can produce useful three-dimensional reconstruction outputs for low-cost microscopy experiments.
Why it matches plant phenotyping methods低コスト三次元断層イメージング法そのものを開発し、ポプラ組織・枝やシロイヌナズナ根近傍を対象に植物の内部構造を可視化しているため、植物形態の取得法として中心的です。
abstractHere we present an ultra-low-cost continuous-wave diffusive tomography (CWDT) system for biological samples.
Reproduction assets foundThe paper's raw imaging inputs, configurations, and reconstruction outputs for Figures 2–5 are publicly deposited on Kaggle. The analysis code repository is only 'prepared for release' (no confirmed public deposit yet), so it is listed as request-only. Hardware CAD mirrors are public but are instrument designs, not theDataset · publicFigure-level raw inputs, model configurations, selected outputs, and manifests are available through the Kaggle dataset https://www.kaggle.com/datasets/alingold/continuous-wave-diffusive-tomography .Open asset ↗continuous-wave-diffusive-tomographylines:108-129Code / dataset availability confirmedEurope PMC · Crossref · checked 5 Sept 2026
Plants are geometrically and topologically complex objects, and methods and devices that produce plant point clouds often miss parts due to self occlusions, making further analysis, such as phenotypic trait extraction or 3D reconstruction, difficult. We introduce A-Occ-Plant , a novel method for point cloud completion. The first novelty of our algorithm is converting point clouds into a set of images, which are then completed using 2D amodal segmentation. The images are then converted into a complete point cloud by using view-consistent Gaussian splats. The second novelty is the use of a coarse-to-fine hierarchical Transformer with cross-scale attention. The completed soft masks are fused into a continuous 3D density field using Gaussian splatting, removing the need for external pose estimation or fixed-size inputs. We introduce a synthetic dataset using a procedural model and a real-world plant reconstruction benchmark with artificially generated occlusions. We further benchmark A-Occ-Plant against representative 3D point-cloud completion methods, demonstrate that it recovers downstream phenotypic traits (leaf count, leaf angle, plant height), and show that it generalizes to another crops (soybean). A-Occ-Plant achieves a 264.8% improvement in LPIPS and an 8.3% gain in SSIM compared to the current state of the art, while using only 2.3% of the parameters and running 39.4× faster. We release our code at https://github.com/JaeLee18/PlantPhenomics_Occlusion.
Why it matches plant phenotyping methods植物の遮蔽点群を補完し、葉数・葉角度・草丈という表現型形質を復元する手法を開発しており、データセット作成とベンチマーク検証も中心的に行っている。
abstractWe introduce A-Occ-Plant , a novel method for point cloud completion.
Reproduction assets foundThe paper explicitly releases authors' code and sample data (inference code, sample data for reproducing results) via a Google Drive project download and a GitHub repository, both with explicit availability statements and public URLs.Code · publicThe full code and data at https://github.com/JaeLee18/PlantPhenomics_Occlusion .Open asset ↗JaeLee18/PlantPhenomics_Occlusionlines:386-410Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Accurate characterization of plant 3D architecture and semantic parsing of key cotton organs represent an essential prerequisite for crop phenotyping, precision field management, and cultivar breeding and selection. However, the overall 3D structure of field-grown cotton plants is highly complex in field environments. The morphological traits and spatial distribution of various organs are modulated by multiple factors including genetics, environmental conditions, and cultivation management practices, resulting in pronounced phenotypic variation under field conditions. Thus, this study proposed an integrated technical framework for UAV-based 3D reconstruction and organ semantic segmentation tailored for field-grown cotton. High-fidelity 3D point clouds of the crop canopy are generated by coupling an field-adapted low-altitude unmanned aerial vehicle (UAV) acquisition strategy with neural radiance fields (NeRF). Despite these attractive characteristics, segmenting and analyzing such intricate point clouds can be quite challenging. To effectively parse these complex geometric structures, a novel deep learning architecture named FieldCotSeg-Net is introduced. This model integrates an Anisotropy-aware Local Attention (ALA) module and a Hierarchical Feature Refinement Gate (HFRG) module to capture fine-grained features for precise point cloud segmentation. Experimental results demonstrate that the proposed model achieves outstanding performance on both the Huaxing3Dcot and Crops3D cotton datasets. On the Huaxing3Dcot dataset, the model yields a mean intersection over union (mIoU) of 75.7%, representing a 4.5% improvement over the baseline model. On the Crops3D cotton dataset, after retraining the model on this dataset, it attains an mIoU of 74.5%, showing substantial adaptability and effectiveness in organ segmentation. This technical system provides a scalable and robust solution for field cotton phenotyping analysis in breeding research and commercial production.
Why it matches plant phenotyping methods綿花の圃場表現型解析を目的に、UAVによる3D再構成と器官セグメンテーション手法を開発・評価しており、植物形態の取得が研究の中心である。
abstractThis technical system provides a scalable and robust solution for field cotton phenotyping analysis in breeding research and commercial production.
Three-dimensional (3D) reconstruction of seedlings commonly suffers from under-constrained issues in areas with severe occlusion and insufficient observation, leading to false geometry and rendering artifacts. The existing methods primarily rely on pixel-level consistency constraints, resulting in poor stability when reconstructing seedlings with slender branches and complex structures. To address these challenges, this paper proposes a 3D reconstruction method for seedlings which is based on improved DIFIX3D+. By integrating the Stable Diffusion-VAE module into the DIFIX3D+ framework, this approach enhances the reconstruction constraint mechanism at the representation level. Firstly, high-precision segmentation of seedlings was achieved by eliminating background interference using the Florence-2 semantic priors and the SAM2 (Segment Anything Model 2) instance segmentation model. Then, by jointly mapping DIFIX3D+ restored images and their corresponding rendered images into the latent space of Stable Diffusion-VAE, consistency supervision was established within the latent representation domain. It worked in tandem with traditional pixel-level constraints, thereby suppressing the propagation of pixel noise caused by lighting variations, specular reflections, and texture repetitions at the representation level. Experimental results demonstrated that on the self-built dataset, compared to the original 3DGS (3D Gaussian splatting) and DIFIX3D+, the proposed method achieved the peak signal-to-noise ratio (PSNR) improvement of 28.99% and 24.25%, respectively, and the structural similarity index measure (SSIM) improvement of 3.16% and 3.74%, while reducing the learned perceptual image patch similarity (LPIPS) by 9.24% and 41.94%. The average PSNR, SSIM, and LPIPS values reached 31.54 dB, 0.9759, and 0.054, respectively, demonstrating significantly superior reconstruction accuracy compared to mainstream models such as Nerfacto, Plenoxels, and Mip-Splatting. This method combines high visual fidelity with excellent geometric structure representation capabilities, providing a technical reference for high-quality, low-cost 3D reconstruction of seedlings in complex scenes.
Why it matches plant phenotyping methods苗の3D形状・構造を抽出する画像ベース再構成手法の開発が中心であり、植物表現型取得の方法論に該当する。
abstractThis method combines high visual fidelity with excellent geometric structure representation capabilities, providing a technical reference for high-quality, low-cost 3D reconstruction of seedlings in complex scenes.
The objective of this study is to propose a digital fruit tree reconstruction method based on neural radiation field theory that will enable the efficient, accurate, and non-destructive acquisition of phenotypic information from fruit trees, while simultaneously reducing the cost of collecting this data. Firstly, a low-cost information acquisition platform is constructed for the purpose of shooting a multi-view video around a fruit tree. The video is then extracted and framed using a motion recovery structural algorithm, thereby obtaining a multi-view image sequence of the fruit tree with positional data. Secondly, the the image sequence is employed to train the neural radiation field, thereby obtaining a converged three-dimensional scene of the fruit tree. Ultimately, the three-dimensional scene is derived in the form of a point cloud, thus yielding a high-phenotypic detail point cloud model of the fruit tree. A multi-period point cloud model of an apple tree in an orchard environment, encompassing the flowering, fruiting, and dormant periods, was reconstructed using the aforementioned method. The experimental results demonstrated that the error associated with the tree shape data recorded by the multi-period point cloud model established by the aforementioned method was, on average, below 5%. Furthermore, the average error across all periods was 2.69%, representing a 75.50% reduction compared to traditional reconstruction methods. The dimensional accuracy of the point cloud model at the organ scale can reach the millimetre level, with an average error of 3.10% for fruit diameter, which is 66.19% lower than that of the traditional reconstruction method. The reconstruction method is robust to all periods of fruit trees and can meet the majority of cases of digital fruit tree reconstruction.
Why it matches plant phenotyping methods果樹の多視点画像からNeRFと点群を用いて樹形・器官寸法などの表現型を非破壊取得する手法を開発・検証しており、方法が研究の中心である。
abstractpropose a digital fruit tree reconstruction method based on neural radiation field theory that will enable the efficient, accurate, and non-destructive acquisition of phenotypic information from fruit trees
MaizeRoot2D/3D reconstructionGrowth / development / phenologyRoot system architecture
Maize (Zea mays L.) is a major cereal crop whose productivity across diverse agro-ecological environments is strongly influenced by belowground traits. The root system functions as the primary plant-soil interface, regulating water and nutrient uptake, providing mechanical support and enabling adaptive responses to abiotic and biotic stresses. Despite its central importance, maize root biology has historically received less attention than aboveground characteristics. The maize root system comprises primary, seminal, nodal and lateral roots differing in developmental origin, growth behaviour and physiological role. Key architectural traits-such as rooting depth, root growth angle and branching density-play a crucial role in root system development. These traits, along with anatomical features like cortical aerenchyma and root hair development, are governed by complex genetic networks involving regulatory genes, quantitative trait loci and hormone-mediated signalling pathways. Root growth and spatial distribution are further shaped by soil properties and agronomic practices, including irrigation and nutrient management. Recent advances in high-throughput phenotyping, three-dimensional (3D) reconstruction and artificial intelligence (AI) based image analysis technologies have enhanced quantitative assessment of root traits. This review consolidates recent progress in maize root research with emphasis on root system architecture (RSA), developmental regulation and their functional relevance to crop productivity. Uniquely, it integrates structural, genetic and phenotyping advances in maize root research into a unified framework, while explicitly linking RSA with its functional significance, an aspect often treated separately in earlier reviews. Optimising maize root systems is therefore essential for improving productivity, resource-use efficiency and agricultural sustainability under changing climatic conditions.
Why it matches plant phenotyping methodsトウモロコシ根系研究のレビューであり、根系形態形質の定量評価に用いるハイスループット表現型解析、3D再構成、AI画像解析を明示的に扱うため、表現型解析手法レビューとして中心的です。
abstractRecent advances in high-throughput phenotyping, three-dimensional (3D) reconstruction and artificial intelligence (AI) based image analysis technologies have enhanced quantitative assessment of root traits.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 13 Sept 2026
Modern agriculture operates at an unprecedented crossroads, it must simultaneously accelerate crop yields to feed an expanding global population and adapt to the severe, fluctuating pressures of climate change, structural soil degradation, abiotic water deficits, and evolving biological threats. Historically, selecting resilient crop varieties and implementing field-scale management strategies relied extensively on destructive, labor-intensive, and fundamentally subjective visual metrics. This manual processing approach has long been recognized as the primary operational bottleneck in agricultural advancement.To bridge the gap between rapidly expanding genomic data and actual field performance, the systematic, non-destructive quantification of structural and functional plant traits, plant phenotyping, has emerged as a transformative frontier. By integrating high-throughput engineering, multi-scale remote sensing, deep learning, and advanced molecular biology, modern phenotyping transitions crop science away from qualitative estimation toward highly reproducible, multidimensional data frameworks. This Research Topic presents new advances in advanced 3D reconstruction and deep semantic segmentation at the seedling stage; amodal fruit segmentation, morphological extraction, and early water-stress diagnostics; high-throughput in-field seedling counting and dynamic density modeling; multimodal foundation models, network pruning, and intelligent phytoprotection; aerial and spaceborne remote sensing for canopy analysis and weed monitoring; plant physiology, functional spectroscopy, and functional genomics under abiotic stress; and automated diagnostics for real-time orchard scouting and vineyard management.Automating the characterization of complex spatial layouts under controlled or greenhouse environments is essential for early variety selection and early-stage structural evaluation. Several contributions within this volume provide key breakthroughs in navigating overlapping tissues, severe occlusions, and low-contrast edge regions. showcases how substituting standard convolutions with deformable convolutions enables deep neural networks to accurately isolate the main stem of mature, high-density crops like soybeans. This architecture overcomes the traditional challenges of color mimicry and severe occlusion by pods and leaves, achieving an outstanding mIoU of 90.58% and providing reliable indices for lodging resistance and structural yield modeling (R 2 = 0.9746).Accurately extracting fruit morphology under commercial greenhouse conditions remains heavily constrained by overlapping crop structures, foliage cover, and variable shadows. Simple semantic masks typically fail when a target fruit is partially blocked, leading to a loss of key volumetric data.To resolve the challenge of hidden boundaries, Li, Yin, et al. (2025) developed CGA-ASNet, a specialized RGB-D amodal segmentation network driven by a Contextual and Global Attention (CGA) module designed to restore occluded tomato regions. Trained on a high-fidelity synthetic greenhouse dataset (Tomato-sim) generated via NVIDIA Isaac Sim's Replicator Composer and optimized with a mean coordinate fusion algorithm for real-world validation, this architecture expands the network's receptive field to predict the complete, hidden circular forms of occluded tomatoes, achieving an F@0.75 score of 94.2 and an amodal mIoU of 82.4%. This proves that simulation-to-real (Sim2Real) domain pathways can successfully decode full physical volumes under dense commercial canopies.Complementing this structural restoration, Yang, Li, et al. (2025) designed an integrated diagnostic framework to identify early water stress dynamics in greenhouse tomatoes. Built upon an optimized YOLOv11n core, their system integrates adaptive kernel convolutions (AKConv) into the network backbone's C3k2 modules and implements a recalibration feature pyramid detection head based on the specialized P2 small-target layer. This combination achieved a 5.4% increase in mAP50-95 for identifying fine phenotypic parts. By applying automated geometric analysis to the extracted bounding boxes, the system extracts plant heights and petiole count with low relative errors, feeding these phenotypic parameters into a Random Forest classification routine that flags water-stressed plants with 98% accuracy to guide targeted, automated drip irrigation.Accurate plant stands during early vegetative stages represent the foundational metric required to establish true field emergence rates, validate seed vigor across diverse breeding blocks, and perform early yield predictions.To solve the challenges of small targets, extreme spatial density, and adjacent leaf overlap, Zang et al. (2025) designed DM_IOC_fpn, a wheat seedling counting framework that balances local and global contextual features. By structuring a point-annotated dataset and embedding a densityenhanced encoder module, their network balances micro-scale spatial limits with macro-scale canopy structures. Optimized through a combined loss function tracking counting, classification, and regression parameters, this architecture achieved low error scores (RMSE = 2.91; MAE = 2.23), outperforming standard object-detection benchmarks in complex field environments.At the same time, scaling up to real-time aerial monitoring required major reductions in model complexity to support resource-constrained edge computers on autonomous aerial platforms. Feng, Nie, and Li (2025) engineered an ultra-lightweight YOLOv8n variant tailored for real-time maize seedling counting from high-speed UAV RGB overflights. By reparametrizing RepConv with HGNetV2, they constructed a lean Rep_HGNetV2 backbone, integrated a Bidirectional Feature Pyramid Network (BiFPN) for multi-scale feature alignment, and implemented a Task Dynamically Aligned Detection Head (TDADH). This architecture compressed total model parameters by 47% and reduced weight sizes to 3.5 MB while maintaining a 96.5% detection accuracy and an ultra-fast processing speed of 146.3 FPS, paving the way for low-cost, real-time field scouting.Automated phytoprotection requires machine-vision architectures capable of generalizing across highly diverse species, complex field conditions, and varying computational boundaries. A significant subset of the published papers addresses these challenges through foundation model adaptation, multi-modal alignment, and efficient network compression.A major paradigm shift presented in this collection involves moving away from task-specific training and toward foundation model adaptation. Chen, Ruan, et al. (2026) introduce a novel architecture integrating the DinoV3 foundation model with a Unet framework to achieve robust leaf lesion segmentation across diverse species (such as coffee and black gram). By incorporating a Spatial Prior Module (SPM), their approach surpassed standard benchmark networks by over 10.5% in IoU while reducing inference times by approximately 93.6%, demonstrating that highparameter foundation models can be highly optimized for resource-constrained edge devices in real-time scouting.To solve the perennial problem of limited training data for rare or emerging crop diseases, Cooper et al. ( 2026) developed an ingenious synthetic data generation pipeline. Combining 3D procedural leaf modeling in Blender with diffusion-based disease synthesis (Stable Diffusion fine-tuned with LoRA and ControlNet), they synthesized highly accurate plant disease images with perfect groundtruth annotation masks. When deployed in low-resource data settings, combining these synthetic pipelines with restricted real-world datasets consistently drives significant improvements in downstream segmentation tasks. To tackle specific, complex pathologies, Xu, Chang, et al. (2025) developed the TSSC deep learning model, which embeds three-neighbor channel attention paired with a complementary squeeze-and-excitation mechanism. This specific architecture minimizes structural degradation risks while pushing classification accuracy to 99.61% for highly complex pea leaf pathologies. Similarly, Feng, Liu, et al. (2025) tackled overlapping leaf occlusions and small lesion footprints in citrus groves with YOLO-Citrus, an optimized framework integrating C3K2-STA, ADown modules, and a Wise-Inner-MPDIoU loss function to strike a balance between edge computational constraints and field deployment.UAVs and high-resolution satellite imagery have expanded the operational scale of phenotyping from individual pots to vast breeding blocks and commercial fields, allowing researchers to capture macro-dynamic parameters over time.In complex canopy systems that defy standard top-down aerial sensing, such as single-staked white Guinea yams, Iseki et al. (2026) demonstrated the distinct advantage of utilizing multi-angle (combined nadir and oblique) UAV imaging configurations. When coupled with support vector regression, this method captures complementary canopy-structure information to model shoot biomass trajectories (R 2 = 0.79) across multiple years and management zones. These nondestructive, time-series datasets enabled the fitting of genotype-specific Richard's growth curves using Bayesian inference, isolating valuable genetic variations in early growth allocation.To capture full-season vertical physiological changes over large scales, Li, Yue, and Luo (2025) developed a hybrid CNN-LSTM-Attention (CLA) model designed to estimate the full-period Leaf Area Index (LAI) in rice using multi-temporal UAV multispectral imagery. By using the CNN layer to extract instantaneous spatial features, the LSTM block to process seasonal time-series intervals, and a self-attention mechanism to weight critical growth transitions, their platform achieved a high coefficient of determination (R 2 = 0.92) and kept relative root mean square errors (RRMSE) below 9%. This network minimized soil background noise during early vegetative stages (LAI values 1-
Why it matches plant phenotyping methods植物フェノタイピングの技術動向を扱うEditorialであり、画像解析、UAVセンシング、深層学習、形質抽出などの方法が中心的に整理されている。
In Japan, the quantity of domestically produced fruit has been gradually decreasing, while wholesale prices have continued to rise due to declining production volumes and a shift toward high-quality varieties. To address these trends, improving quality and reducing labor through automation have become urgent challenges. In precision viticulture, monitoring the growth of grape clusters plays a key role in yield estimation, disease management, and optimal harvest timing. Although recent advances in deep learning and 3D reconstruction have enabled accurate fruit detection and modeling in vineyards, tracking the same clusters on different days remains challenging because of branch movement, fruit growth, and varying imaging conditions. This study proposes a branch-based 3D alignment framework for the cross-day tracking of grape clusters. Stable vine structures, such as trunks and main branches, are reconstructed using Structure from Motion, and their spatial correspondences are estimated through SIFT-based matching and similarity transformation. Once the coordinate systems of different days are aligned, the grape clusters detected by CenterNet are associated based on spatial proximity in the unified 3D space. Experiments over multiple observation days demonstrated that the proposed method successfully maintained the consistent tracking of grape clusters throughout the growth period. These results indicate that branch-based alignment effectively stabilizes multi-day observations and facilitates the temporal monitoring of fruit growth, supporting automated phenotyping and future field robot applications in viticulture.
Why it matches plant phenotyping methodsブドウ房の経日追跡を目的とする3D画像アライメント手法を開発し、果実成長の時系列モニタリングと自動フェノタイピングへの利用を実験的に検証しているため、フェノタイピング手法が中心である。
abstractThis study proposes a branch-based 3D alignment framework for the cross-day tracking of grape clusters.
Three-dimensional (3D) reconstruction based on structure from motion and multi-view stereo (SfM-MVS) is increasingly used in plant phenotyping, but its performance is influenced by crop architecture, viewpoint configuration, and image preprocessing. For compact crops such as peanut, dense branching and severe within-canopy occlusion make reliable reconstruction challenging. This study evaluated the effects of reconstruction pipeline, angular interval, and image preprocessing on 3D reconstruction of peanut plants under controlled rotary imaging. A total of 10,800 RGB images from 30 plants were used to compare representative implementations of incremental and global SfM-MVS pipelines in terms of geometric quality, phenotypic accuracy, and processing efficiency. At the 1° baseline, the tested global pipeline implementation reduced the root mean square reprojection error (RMSRE), point-density coefficient of variation (CV), vertical root mean square error (VRMSE), and the 95th percentile of the absolute point-cloud distance values (P95) by 15.05%, 14.08%, 39.87%, and 33.33%, respectively, and increased average phenotypic accuracy from 96.08% to 97.37%, compared with the tested incremental pipeline implementation. In contrast, the tested incremental implementation showed a lower voxel void ratio and shorter processing time. In both pipelines, increasing the angular interval reduced processing time but also reduced geometric stability, internal voxel filling, and phenotypic accuracy. In the present dataset, angular intervals of 3°–5° provided a favourable balance between reconstruction accuracy and efficiency. Cropping reduced peripheral redundancy, whereas cropping combined with background removal produced the best overall results, with the lowest reprojection error and the highest phenotypic accuracy. These results provide practical guidance for selecting reconstruction pipeline, viewpoint configuration, and preprocessing strategy in close-range indoor 3D phenotyping of peanut plants and crops with similar canopy architectures.
Why it matches plant phenotyping methods落花生の3D表現型取得について、SfM-MVSパイプライン、視点間隔、画像前処理を比較・検証しており、フェノタイピング手法が研究の中心である。
abstractThis study evaluated the effects of reconstruction pipeline, angular interval, and image preprocessing on 3D reconstruction of peanut plants under controlled rotary imaging.
Accurate 3D plant models are crucial for computational phenotyping and physics-based simulation; however, current approaches face significant limitations. Learning-based reconstruction methods require extensive species-specific training data and lack editability for hypothesis-driven research. Procedural modeling offers parametric control and large model variability but demands specialized expertise in geometric modeling and an in-depth understanding of complex procedural rules, making it inaccessible to domain scientists. We present FloraForge, an LLM-assisted framework that enables domain experts to generate biologically accurate, fully parametric 3D plant models through iterative natural language Plant Refinements (PR) during template creation, minimizing the need for programming expertise. Our co-design workflow leverages LLM-assisted code generation to progressively refine Python scripts that generate parameterized complex plant geometries as Non-Uniform Rational B-Spline (NURBS) surface representations, with botanical constraints. Plant organs are represented as spline surfaces that can be easily tessellated into polygonal meshes with arbitrary precision, ensuring compatibility with functional structural plant analysis workflows such as light simulation, computational fluid dynamics, and finite element analysis. We demonstrate the framework by generating procedural models of multiple maize genotypes, soybean (which shares the procedural generator with mung bean), and mung bean, with one plant tracked across different developmental stages. We fit procedural models to empirical LiDAR and NeRF-derived point cloud data through manual refinement of the Plant Descriptor (PD), a human-readable YAML file originally templated by the LLM, obtaining consistently low mean symmetric Chamfer distances that indicate close agreement between generated models and measured plant geometry. The pipeline generates dual outputs: triangular meshes (represented as STL or OBJ files) for visualization and triangular meshes with additional parametric metadata for quantitative analysis (stored as SMESH files). We further illustrate analysis-ready use by coupling the procedurally generated models to the HELIOS framework to simulate diurnal photosynthetically active radiation interception in virtual maize and mung bean fields across growth stages. Our framework uniquely combines pre-trained LLM-assisted template creation, mathematically continuous representations that support both phenotyping and rendering, and direct parametric control through the PD. The framework makes sophisticated geometric modeling accessible to plant science researchers while maintaining mathematical rigor through biologically interpretable parameterizations; additionally, the iterative PR dialogue produces an explicit record of model properties that is typically absent in conventional procedural modeling pipelines.
Why it matches plant phenotyping methods植物の3D形状を生成・編集し、LiDAR/NeRF点群との適合で検証する、計算機フェノタイピング向けの中心的手法開発である。
abstractWe present FloraForge, an LLM-assisted framework that enables domain experts to generate biologically accurate, fully parametric 3D plant models
This study evaluates the performance of LiDAR sensing and UAV photogrammetry for three-dimensional canopy reconstruction and structural parameter estimation in precision agriculture under Mediterranean conditions. Experiments were conducted in Sicily, Italy, on Moringa oleifera Lam. and Ficus macrophylla subsp. columnaris, representing contrasting canopy architectures. LiDAR and UAV photogrammetric data were used to generate canopy models and estimate canopy height, canopy volume, and vegetation density distribution. A voxel-based approach was applied to LiDAR-derived point clouds to quantify internal canopy structure and vegetation density within the canopy volume. Accuracy was assessed by comparing remote sensing-derived canopy metrics with ground-truth field measurements. LiDAR outperformed UAV photogrammetry in canopy height estimation, achieving lower RMSE values than UAV-derived models (0.19–0.21 m vs. 0.52–0.60 m), corresponding to an approximate error reduction of 60–65%. LiDAR also provided more accurate canopy volume estimation, with lower relative errors than UAV photogrammetry (3.5–4.2% vs. 13.7–16.1%). The voxel-based LiDAR approach enabled the quantification of vegetation density distribution within the canopy volume, showing higher sensitivity to internal canopy layers compared with UAV photogrammetry, particularly in the structurally complex Ficus macrophylla canopy. UAV photogrammetry provided reliable estimates of the external canopy surface but underestimated structural parameters in dense vegetation due to canopy occlusion and limited penetration into inner canopy layers. Differences between the two methods were more pronounced in Ficus macrophylla than in Moringa oleifera, confirming the strong influence of canopy complexity on sensing performance. These findings demonstrate that LiDAR-derived structural and voxel-based metrics can improve canopy characterization and support precision agriculture applications such as biomass estimation, irrigation planning, yield prediction, and canopy management in Mediterranean cropping systems.
Why it matches plant phenotyping methodsLiDARとUAVフォトグラメトリによる植物キャノピーの3次元再構築・構造形質推定を比較検証し、地上計測との精度評価まで行っており、フェノタイピング手法が研究の中心である。
abstractThis study evaluates the performance of LiDAR sensing and UAV photogrammetry for three-dimensional canopy reconstruction and structural parameter estimation
High-throughput phenotyping is essential for resolving genotype-by-environment interactions and accelerating crop breeding. In greenhouse potted-plant systems, narrow aisles, global navigation satellite system (GNSS)-denied operation, variable pot layouts, and plant-level data traceability constrain repeatable automated phenotyping. This study presents PhenoRob-P, a modular autonomous robotic system designed for potted crops in structured facility environments. The system integrates a compact two-wheel differential chassis, a LiDAR–vision fusion framework for row-level navigation, pot-level target identification and local alignment, a six-degree-of-freedom robotic arm with inverse-kinematics-based real-time pose compensation for repeatable multi-view close-range imaging, and a three-tier User–Cloud–Robot platform for task scheduling, remote monitoring, and closed-loop data management. Greenhouse validation showed throughputs of 520 pots/h in continuous scanning mode and 187 pots/h in multi-view fine inspection mode. At travel speeds of 0.2–0.3 m/s, mean terminal positioning errors remained within 30 mm, and approximately 87% of lateral and longitudinal errors fell within ±30 mm. Biological validation demonstrated time-resolved stress phenotyping in wheat, with color indices capturing drought progression and rewatering recovery. For maize, multi-view three-dimensional reconstruction estimated plant height and stem diameter with R 2 values of 0.940 and 0.845, respectively, relative to manual measurements. These results show that PhenoRob-P provides an integrated perception-localization-acquisition-analysis workflow for high-throughput, traceable, and time-resolved phenotyping of potted crops.
Why it matches plant phenotyping methods植物形質の取得を中核とする自律ロボット型ハイスループット表現型解析プラットフォームを開発・検証しており、画像取得、3D再構成、ストレス・形態形質の推定性能も評価している。
abstractThis study presents PhenoRob-P, a modular autonomous robotic system designed for potted crops in structured facility environments.
Reproduction assets foundThe paper's Data availability statement explicitly deposits authors' source code and sample datasets in a public GitHub repository, matching the allowed URL.Code · publicThe source code and sample datasets supporting the findings of this study are openly available at the following GitHub repository: https://github.com/Sunniersy/PhenoRob-P .Open asset ↗https://github.com/Sunniersy/PhenoRob-P · Sunniersy/PhenoRob-Plines:388-431Plant phenotyping relevance match · UnverifiedEurope PMC · OpenAlex · Crossref · checked 13 Sept 2026
Accurate tree counting from remote sensing data is essential for forest inventory, biomass estimation, carbon accounting, and ecological monitoring. However, existing approaches predominantly rely on airborne RGB imagery and often struggle in complex forest scenes where neighboring crowns exhibit highly similar textures and colors and where overlapping crown boundaries become ambiguous. To address this limitation, the LiDAR-derived Canopy Height Model (CHM) is introduced as a complementary modality that provides explicit cues on canopy height variation and vertical structure to support RGB-based analysis. Building on this, we propose BCAR-Net, a broker-guided RGB and depth (RGB-D) multimodal framework that couples bidirectional cross-modal interaction, adaptive tri-branch fusion, and auxiliary reconstruction within a two-stage optimization scheme. Specifically, a bidirectional cross-attention U-Net generates an intermediate broker RGB-D representation from paired RGB images and depth maps through symmetric bidirectional cross-attention between the two modalities and direction-aware gating. The original RGB image, depth map, and broker representation are then jointly encoded by three weight-sharing branches and adaptively aggregated by a spatial fusion gate for density-map regression. To regularize the fused latent feature, a multi-scale cross-attention reconstruction decoder provides auxiliary RGB and depth reconstruction supervision by querying multi-scale BCA-UNet encoder features through 2D cross-attention, and a reconstruction-oriented first stage replaces externally generated fused-image supervision, yielding a task-consistent optimization scheme. Experiments on the NEONTreeEvaluation benchmark show that BCAR-Net consistently outperforms single-modality settings and direct RGB-D concatenation multimodal baseline. Additional experiments on a public UAV RGB-LiDAR dataset provide a small-scale supplementary evaluation under a different acquisition setting, where BCAR-Net achieves modest but consistent improvements over RGB-only and depth-only baselines. These results demonstrate that the proposed framework offers an effective but computationally cautious solution for tree counting in complex forest environments.
Why it matches plant phenotyping methodsRGB画像とLiDAR由来データから樹木数を推定する深層学習手法を開発し、複数ベンチマークで比較評価しており、植物個体の計測手法が研究の中心である。
abstractwe propose BCAR-Net, a broker-guided RGB and depth (RGB-D) multimodal framework
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 5 Sept 2026
Abstract High-fidelity 3D reconstruction of plants from video is a key enabler of digital phenotyping, but the dense frame streams produced by modern cameras impose prohibitive compute, memory, and energy costs on the embedded platforms used in the field. Existing frame-selection methods reduce this load using geometric or photometric heuristics, yet they provide no statistical guarantee on the reconstruction quality that the retained subset will deliver. We present a conformal predictiondriven frame selection strategy that augments heuristic selection with a calibrated, distribution-free decision rule: a frame is processed only when the reconstruction model’s predicted uncertainty for that view exceeds a threshold whose miscoverage rate is controlled at a user-specified level α. Because conformal prediction makes no assumption on the underlying error distribution, the resulting frame budget carries a finite-sample guarantee on the probability that reconstruction error stays below the target tolerance. On a multi-view plant dataset, the proposed method attains reconstruction fidelity comparable to processing the full stream while using 41% fewer frames, and reduces the frame budget by 23% relative to a strong uncertainty-agnostic baseline at matched quality. The approach is lightweight enough for on-device deployment and turns frame selection from a tuned heuristic into a procedure with explicit, controllable risk—a property we argue is essential for trustworthy edge agriculture.
Why it matches plant phenotyping methods植物の3D再構成を対象に、予測保証付きのフレーム選択手法を開発・評価しており、植物フェノタイピング用の画像取得・再構成ワークフローが中心である。
abstractHigh-fidelity 3D reconstruction of plants from video is a key enabler of digital phenotyping
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · Crossref · checked 5 Sept 2026
1 Abstract Non-structural carbohydrates (NSCs) are central to plant carbon allocation and physiological regulation, yet their quantification typically relies on destructive biochemical assays that lack spatial resolution. Here, we developed a shortwave infrared (SWIR) hyperspectral imaging workflow for non-destructive estimation and spatial reconstruction of starch-associated variation in strawberry leaves. The workflow combined automated hyperspectral segmentation, spectral preprocessing, Partial Least Squares Regression (PLSR), and constrained wavelength selection. Sample-level spectra extracted from 114 strawberry leaf samples grown across three different metabolic conditions were paired with destructive starch measurements and used to train models across the 900–1750 nm spectral range. A constrained greedy band-selection strategy revealed that predictive performance approached a plateau at approximately 12 wavelengths, indicating substantial spectral redundancy within the full hyperspectral dataset. The final reduced-band model achieved a cross-validated coefficient of determination (R 2 ) of 0.771 ± 0.066 and a root mean squared error (RMSE) of 0.743 ± 0.098 mg g −1 fresh weight using repeated stratified 5-fold cross-validation. Pixel-wise application of the final model generated spatial starch-associated maps that preserved pronounced intra-leaf heterogeneity, including vein-associated spatial structure. These results demonstrate that starch-associated spectral information can be reconstructed from a constrained reduced-band SWIR framework while retaining sufficient predictive performance for spatial mapping. The identified wavelength reduction supports the feasibility of deployable multispectral systems for non-destructive carbohydrate sensing in plant phenotyping applications.
Why it matches plant phenotyping methods植物葉のデンプン状態を非破壊推定・空間再構成するSWIR画像計測とケモメトリック解析ワークフローを開発・検証しており、フェノタイピング手法が中心です。
abstractHere, we developed a shortwave infrared (SWIR) hyperspectral imaging workflow for non-destructive estimation and spatial reconstruction of starch-associated variation in strawberry leaves.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 6 Sept 2026
Abstract Automated three-dimensional reconstruction of plant architecture under- pins high-throughput crop phenotyping, yet its deployment in practical field settings is constrained by two fundamental limitations of Neural Radiance Fields (NeRF): degraded geometry under sparse, unstructured image capture, and a complete absence of calibrated uncertainty estimates that would allow practitioners to distinguish reliable geometry from reconstruction artefacts. We present UA-PlantNeRF, a unified framework that resolves both limita- tions through three tightly coupled contributions. First, building on VISAR, our prior intelligent video frame-selection strategy (weights α1 = 0.4, α2 = 0.3, α3 = 0.3), the pipeline identifies maximally informative, non-redundant view- points from raw footage with as few as 15 frames. Second, a dual-head NeRF architecture augmented with Monte Carlo Dropout produces jointly decom- posed aleatoric and epistemic uncertainty alongside each reconstructed voxel, trained under a heteroscedastic negative log-likelihood objective. Third, split conformal prediction—with calibration performed on held-out plants to preserve exchangeability— yields provable, distribution-free per-ray cov- erage guarantees at any user-specified confidence level. Evaluated across three benchmarks (Pheno4D, ROSE-X, and our novel UA-Field greenhouse dataset) at sparsity levels N ∈ {15, 30, 50}, UA-PlantNeRF achieves PSNR 28.7±0.6 dB and SSIM 0.89±0.01 at N = 30, outperforming all sparse-view baselines (p
Why it matches plant phenotyping methods植物の3D構造・アーキテクチャを推定する画像ベースのNeRF手法を開発し、複数ベンチマークで評価しているため、植物表現型計測法が中心です。
abstractEvaluated across three benchmarks (Pheno4D, ROSE-X, and our novel UA-Field greenhouse dataset)
The core volume ratio (CVR) is a key indicator for evaluating the proportion of edible fraction in stone fruits. Traditionally, CVR is determined through destructive sampling by separately measuring the masses of the core and entire fruit. Recently, low-field nuclear magnetic resonance imaging (LF-NMRI) has been introduced as a non-destructive alternative, but its sparse sampling limits the ability to achieve accurate spatial and volumetric quantification of fruit quality. To address this limitation, we propose a novel method for high-precision three-dimensional (3D) modeling of stone fruits. The method acquires tomographic LF-NMRI sequences along three orthogonal axes. Each sequence is segmented into pulp and core regions using a SwinUNet deep learning model and converted into point clouds for each view. Point clouds from the three orthogonal views are registered via a genetic algorithm to align structural information from complementary perspectives and fused into a unified 3D model through Poisson surface reconstruction. Using prunes as a representative case, the method enables accurate quantification of core and entire fruit volumes, achieving a CVR estimation with a mean absolute error of 0.13% compared to manual measurements. The proposed three-view reconstruction strategy yields a volumetric error of only 0.73%, significantly outperforming single-view (4.57%) and dual-view (3.73%) approaches. This technology provides a robust and accurate non-destructive solution for 3D internal quality analysis of fruits.
Why it matches plant phenotyping methodsLF-NMRI、深層学習セグメンテーション、3D再構成を組み合わせ、果実内部の芯・可食部体積という植物器官形質を非破壊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中心である。
abstractwe propose a novel method for high-precision three-dimensional (3D) modeling of stone fruits.
Accurate digital canopy height models (DCHMs) derived from historical aerial photographs are essential for reconstructing long-term forest structural dynamics; however, the influence of photogrammetric software on DCHM quality and reliability remains insufficiently evaluated. This study compared the performance of two structure-from-motion (SfM) photogrammetric platforms, Metashape and Pix4Dmatic, for processing old aerial photographs and generating DCHMs in Ishikawa prefecture. Software performance was assessed using image processing efficiency, geometric accuracy based on root mean square error (RMSE), and correlation between derived DCHMs and National Forest Inventory (NFI) measurements. The results revealed that Metashape required shorter image processing times for the digital surface model generation and produced denser point clouds with broader spatial coverage. By contrast, Pix4Dmatic achieved higher geometric accuracy, with RMSE values of 0.571 m, 0.870 m, and 2.120 m in the X, Y, and Z directions, respectively. The Metashape-derived DCHM showed a higher mean value (15.267 ± 5.882 m) than Pix4Dmatic (14.749 ± 5.834 m), but Pix4Dmatic-generated DCHMs showed a closer relationship (r = 0.880) with NFI data (15.322 ± 5.451 m). These findings demonstrate that photogrammetric software selection substantially influences three-dimensional reconstruction from old aerial imagery and affects the reliability of DCHM generation. This study provides practical guidance for selecting SfM software for forest structural analysis and long-term forest monitoring.
Why it matches plant phenotyping methods森林樹冠高という植物群落の構造形質を対象に、SfMソフトウェアを比較評価し、DCHM生成の精度・信頼性を検証しているため、方法検証が中心である。
abstractThis study compared the performance of two structure-from-motion (SfM) photogrammetric platforms, Metashape and Pix4Dmatic, for processing old aerial photographs and generating DCHMs in Ishikawa prefecture.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
High-fidelity 3D reconstruction and precise phenotypic parameter extraction of banana plants are critical for crop growth monitoring and yield estimation in precision agriculture. However, traditional methods encounter significant bottlenecks: LiDAR systems are cost-prohibitive for widespread adoption, while traditional photogrammetry often fails to handle the complex canopy structures, severe occlusions, and weak texture features characteristic of banana leaves. To address these limitations, this article proposes a novel framework for 3D reconstruction and automatic phenotyping based on multi-view images captured by mobile phones. We introduce BN-NeRF, an enhanced Neural Radiance Field method built upon Instant-NGP. Specifically, we integrate three key technical improvements: (1) frame-level geometric calibration to correct camera pose drift caused by handheld motion; (2) sparse geometric anchoring to explicitly constrain depth and scale using sparse point clouds; and (3) thin-leaf prior regularization to suppress artifacts and improve the geometric accuracy of leaf surfaces. Building on this reconstruction, we establish a complete pipeline to recover explicit metric geometry from implicit radiance fields. By combining mesh topological analysis with geodesic algorithms, we achieve automated and precise extraction of key morphological parameters. Extensive experiments were conducted on a dataset of 90 banana plants in a real-world orchard. The results demonstrate that BN-NeRF achieves superior rendering quality (PSNR of 32.4 dB, SSIM of 0.951, and LPIPS of 0.152) while maintaining inference speeds comparable to Instant-NGP. Furthermore, the extracted phenotypic parameters showed strong agreement with manual ground truth across both leaf-level and structural traits. In addition to trait-specific regression performance, the evaluation also includes normalized completeness analysis, calibration-cube-based scale validation, and Bland-Altman agreement analysis, supporting the measurement reliability of BN-NeRF for field phenotyping. This study demonstrates that low-cost smartphone-based acquisition, combined with BN-NeRF, can support accurate field phenotyping of banana plants. In addition, an implemented mobile-cloud system was functionally validated through repeated end-to-end runs on an iPhone 13 client and a cloud workstation.
Why it matches plant phenotyping methodsスマートフォン画像からの3D再構成と植物形態形質抽出を中核とするBN-NeRF手法を開発し、圃場データで精度・再現性を検証しているため。
abstractthis article proposes a novel framework for 3D reconstruction and automatic phenotyping based on multi-view images captured by mobile phones
Field / plotNeRF / 3D Gaussian SplattingPhotogrammetry / SfM / MVSLiDAR / point cloudStem / branchMorphology / geometry measurement2D/3D reconstructionArchitecture / morphology / geometry
Accurate characterization of tree stem geometry is essential for forest inventories, yet conventional field measurements of diameter at breast height (DBH) are limited to a single cross-section and do not capture vertical variability along the trunk. This study compares five approaches for stem characterization in a Mediterranean forest: mobile laser scanning (MLS), consumer-grade iPad-LiDAR, Structure from Motion (SfM) photogrammetry, Gaussian Splatting (GS), and manual field measurements. Data were acquired simultaneously within a 2.5 m radial plot. DBH was estimated through RANSAC-based circular fitting, and stem sections were extracted every 20 cm to assess diameter stability along the trunk. All techniques produced similar mean DBH values closely matching field measurements (23 cm), with MLS achieving the lowest RMSE (1.29 cm), followed by SfM (1.52 cm), GS (1.60 cm), and iPad-LiDAR (2.26 cm). However, marked differences were observed in vertical completeness. MLS captured the full vertical profile of the stems, reaching 14.11 m, whereas SfM and GS from iPhone, and iPad-LiDAR were limited to approximately 6 m or less. The results indicate that although low-cost image-based approaches can provide accurate DBH estimates under controlled conditions, MLS remains the most robust solution for comprehensive vertical stem characterization.
Why it matches plant phenotyping methods森林樹幹のDBHと垂直方向の形状を、複数の3Dセンシング手法で推定・比較し、RMSEや垂直完全性を評価している。植物形状計測法の技術比較・検証が中心である。
abstractThis study compares five approaches for stem characterization in a Mediterranean forest: mobile laser scanning (MLS), consumer-grade iPad-LiDAR, Structure from Motion (SfM) photogrammetry, Gaussian Splatting (GS), and manual field measurements.
Abstract Background This study evaluates the performance of two UAV (Unmanned Aerial Vehicle) platforms for vineyard monitoring, focusing on geometric accuracy, effective resolution, and vegetation index consistency. Methods Two UAV (Unmanned Aerial Vehicle) platforms were compared over a vineyard using multiple flights with a consumer-grade drone and a single RTK (Real-Time Kinematic) enabled flight at similar altitude over a single vineyard block under comparable acquisition conditions, followed by photogrammetric reconstruction, DSM (Digital Surface Model) co-registration, effective resolution analysis, and RGB (Red-Green-Blue) based vegetation index comparison. Results This study demonstrates that the integration of RTK (Real-Time Kinematic) technology in the Mavic 3E improves the absolute accuracy of DSMs (Digital Surface Models), eliminating systematic vertical offsets of ~ 35 m observed in the Mavic 2E products. After applying a robust Z-shift (vertical) correction, DSMs (Digital Surface Models) from both platforms became directly comparable, with RMSE (Root Mean Square Error) values reduced to ~ 1.3 m while NMAD (Normalized Median Absolute Deviation) remained stable. RTK (Real-Time Kinematic) positioning in the Mavic 3E ensured reliable absolute georeferencing, whereas DSMs (Digital Surface Models) derived from the Mavic 2 Enterprise Zoom remained internally consistent after post-processing, though with lower absolute positional accuracy. Effective resolution analysis further showed that the Mavic 3E imagery preserves higher spatial detail than the Mavic 2E, underscoring the importance of sensor optics and stability for vineyard monitoring. Comparisons of vegetation indices revealed that normalized indices such as NGRDI (Normalized Green Red Difference Index) and VARI (Visible Atmospherically Resistant Index) provide consistent results across platforms, while ExG (Excess Green Index) exhibited strong biases and wide limits of agreement, reflecting its sensitivity to radiometric differences. Conclusions For cross-platform or multi-temporal monitoring, NGRDI (Normalized Green Red Difference Index) and VARI (Visible Atmospherically Resistant Index) proved to be more robust, whereas ExG (Excess Green Index) should only be applied after radiometric harmonization. This study provides a replicable workflow for UAV (Unmanned Aerial Vehicle) based vineyard monitoring that integrates geometric alignment, DSM (Digital Surface Model) correction, effective resolution assessment, and index comparison, offering practical recommendations for researchers and practitioners aiming to ensure reliable and comparable UAV (Unmanned Aerial Vehicle) derived vineyard metrics.
Why it matches plant phenotyping methodsUAV画像・写真測量・DSM補正・植生指数を比較検証し、ブドウ園の植物状態を再現可能に測定するワークフローを中心に扱っているため、植物フェノタイピング手法研究に該当する。
abstractThis study evaluates the performance of two UAV (Unmanned Aerial Vehicle) platforms for vineyard monitoring, focusing on geometric accuracy, effective resolution, and vegetation index consistency.
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.
High-quality, real-time three-dimensional (3D) reconstruction and phenotyping of field crops are essential for advancing digital farm management. However, challenges such as diverse crop postures and fluctuating illumination in open-field environments significantly hinder reconstruction accuracy. To address these issues, this study proposes AgriGaussian, a novel 3D phenotyping method based on 3D Gaussian Splatting (3DGS), designed to enable low-cost, high-fidelity reconstruction and phenotypic analysis of agricultural fields, with chili pepper plants as an example. Specifically, a dataset encompassing three distinct growth stages of chili pepper plants was collected and preprocessed using a phenotyping robot (PR) equipped with oblique-view imaging. The AgriGaussian method integrates three core components: (1) a Depth-Supervised Strategy (DSS) for adaptively enhancing phenotypic features based on gradient variations; (2) a Crop Appearance Extraction (CAE) module that suppresses the effects of illumination and environmental interference while preserving fine phenotypic details; and (3) a Multi-scale Chunking Training (MCT) strategy that enables scalable reconstruction of large agricultural scenes. Experimental results demonstrate that AgriGaussian achieves high-fidelity reconstruction, with peak signal-to-noise ratio (PSNR) of 26.85 dB, structural similarity index (SSIM) of 0.86, and learned perceptual image patch similarity (LPIPS) of 0.24—surpassing existing baseline algorithms. Compared to manual measurements, the reconstructed canopy height and volume exhibit strong agreement, with coefficients of determination (R2) reaching 0.90 and 0.86, respectively, and average absolute percentage errors as low as 5.1%. Furthermore, the method accurately reconstructs fine-grained features such as foliar lesions and surface textures. In summary, AgriGaussian enables large-scale, high-fidelity reconstruction of field crop phenotypes and provides a promising foundation for advancing 3D phenotyping and digital twin technologies in precision agriculture.
Why it matches plant phenotyping methods植物の3D形態・構造を抽出する低コスト画像ベース表現型解析法を開発し、手動測定との一致性も検証しているため、表現型取得法が研究の中心である。
abstractthis study proposes AgriGaussian, a novel 3D phenotyping method based on 3D Gaussian Splatting (3DGS)
Because conventional vegetative propagation methods for Kiwifruit (Actinidia deliciosa (A.Chev.) C.F.Liang & A.R.Ferguson) often are constrained by their need for extensive plantation areas, high labour inputs, and intensive weed management. Therefore, in vitro micropropagation has emerged as an effective approach for the large-scale production of uniform, disease-free kiwifruit plant material. In the present study the shoot growth dynamics and spatial competition between explants for kiwifruit (cv. Hayward) in vitro-grown were investigated considering different explant densities (3, 5, and 7) and two subculture durations (30 and 45 days). Growth performance was assessed integrating traditional measurements (shoot viability, number and length, callus formation, fresh and dry biomass) with high-resolution three-dimensional photogrammetric reconstruction. Image acquisition was performed using a smartphone-based system (iPhone+viDoc RTK rover), and dense point clouds were generated through the Structure from Motion photogrammetric processing, providing the basis for quantitative geometric analysis. Consistent correlations were observed between manually measured growth traits and smartphone-derived morphometric parameters at both 30 and 45 days of subculture. Specifically, point cloud–based estimates of surface area, height, and volume were significantly associated with shoot number, shoot length, and biomass accumulation, supporting the reliability of 3D photogrammetry as a non-destructive tool for phenotyping in vitro kiwifruit growth. The proposed approach demonstrates the potential of 3D photogrammetry to enhance the objectivity, resolution, and repeatability of growth assessment in in vitro culture systems, offering new insights into shoot development and density-dependent interactions. Graphical Abstract
Why it matches plant phenotyping methodsスマートフォン撮影とSfMによる3Dフォトグラメトリで、キウイフルーツ苗条の表面積・高さ・体積を非破壊推定し、手動測定およびバイオマスとの相関で信頼性を検証しており、表現型取得手法が中心である。
abstractdense point clouds were generated through the Structure from Motion photogrammetric processing, providing the basis for quantitative geometric analysis.
Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 15 Sept 2026
Accurate acquisition of plant phenotypes is crucial for elucidating plant growth and development, underlying genetic mechanisms, and responses to environmental stimuli. Traditional three-dimensional (3D) phenotyping mainly captures geometric traits such as height, leaf area, and canopy volume, while overlooking physiological and biochemical information. Here, we present a hyperspectral point clouds generation method based on PlantGaussian (a 3D Gaussian Splatting technique) that integrates structural and spectral information, extending 3D phenotyping beyond geometry to include physiology. High-quality plant point clouds were first reconstructed using PlantGaussian, and hyperspectral images(HSI) were mapped onto them to produce hyperspectral point clouds. In potted soybean experiments, we built predictive models linking hyperspectral reflectance to SPAD (chlorophyll content) and EWT (equivalent water thickness), and visualized their 3D distributions. The hyperspectral point clouds achieved strong predictive performance for SPAD ( R 2 = 0.78, RMSE = 2.05) and EWT ( R 2 = 0.80, RMSE = 1.07), thereby validating the approach. It further revealed clear vertical stratification within the canopy, highlighting significant spatial heterogeneity of SPAD and EWT in individual plants. Temporal monitoring from August 6 to 21, 2025, captured a sharp increase in EWT after heavy rainfall on August 11. Overall, our results demonstrate that hyperspectral point clouds enable accurate, non-destructive trait estimation and provide a powerful tool for exploring plant function, monitoring stress responses, and advancing precision agriculture.
Why it matches plant phenotyping methods植物の3D形態とハイパースペクトル情報を統合してSPAD・EWTを推定する手法を開発し、予測性能を検証しているため、植物フェノタイピング手法が中心である。
abstractHere, we present a hyperspectral point clouds generation method based on PlantGaussian (a 3D Gaussian Splatting technique) that integrates structural and spectral information, extending 3D phenotyping beyond geometry to include physiology.
Abstract Occlusion is a major factor limiting accurate three-dimensional (3D) wheat phenotyping. In natural growth conditions, overlapping spikes, leaves, and stems often make only partial target regions visible in single-view images, hindering complete and reliable 3D reconstruction. To address this problem, this study proposes an amodal completion-driven framework for single-view 3D reconstruction of occluded wheat. The framework first uses visible prompts to recover the complete appearance and structural cues of occluded targets, and then feeds the completed images into single-view 3D reconstruction models to generate complete 3D structures. To support model training and evaluation, we construct the MMWO (Multi-view Multi-instance Wheat Occlusion) dataset from MMW by synthesizing diverse occlusion samples through organ-level cutouts, random geometric transformations, and region-constrained pasting, with annotations including visible masks, occlusion masks, and complete target images. Six representative reconstruction methods, including Direct3D, Real3D, SF3D, Spar3D, TRELLIS.2, and Hunyuan3D, are systematically evaluated. Hunyuan3D achieves the best geometric performance, with the lowest mean CD-L 1 and CD-L 2 values of 0.1286 and 0.0536, and the highest mean F-score of 0.5668. SF3D achieves the best rendering quality in terms of PSNR, SSIM, and LPIPS. In addition, Pix2Gestalt completion reduces the estimation errors of spike length, width, and area from 9.31%, 10.89%, and 32.23% to 4.64%, 9.70%, and 9.45%, respectively. These results demonstrate that amodal completion can effectively alleviate occlusion-induced information loss and provide more complete structural priors for single-view 3D wheat reconstruction. This study offers a feasible solution for robust 3D wheat phenotyping under occlusion and provides a systematic reference for applying general-purpose 3D generative models to agricultural phenotyping.
Why it matches plant phenotyping methods遮蔽下小麦の単視点3D再構成とアモーダル補完を開発し、データセット構築、複数手法の系統評価、器官形質推定誤差の検証まで行っており、植物フェノタイピング手法が中心である。
abstractthis study proposes an amodal completion-driven framework for single-view 3D reconstruction of occluded wheat
While unmanned aircraft system (UAS)-based photogrammetry and light detection and ranging (LiDAR) are increasingly used for canopy height estimation in forestry and other orchard systems, their application to pecan orchards remains limited. Accurate measurements of tree height and canopy structure are essential in pecan production for assessing tree growth and health, and for supporting precision orchard management. This study provides one of the first systematic evaluations of UAS-based structure-from-motion (SfM) photogrammetry and UAS-mounted LiDAR for estimating pecan tree height. A rotary-wing UAS equipped with RGB and near-infrared (NIR) cameras collected imagery at 60 and 120 m aboveground over two pecan orchards containing 480 and 308 trees, and LiDAR data were acquired at 70 m. UAS imagery was processed to generate three-dimensional (3D) point clouds, digital surface models (DSMs), digital terrain models (DTMs), and orthomosaics. DTMs were derived using point cloud classification and DSM filtering, and tree heights were calculated relative to these terrain models using canopy height models (CHMs) and point cloud–based approaches. LiDAR data were processed to produce calibrated point clouds, DSMs, and DTMs, from which tree heights were extracted using comparable methods. Image-based tree heights showed strong agreement with manual measurements, with point cloud–derived high percentiles or maxima [ R 2 = 0.982–0.996; root mean square error (RMSE) = 14 to 25 cm] consistently outperforming CHM-based estimates across ground elevation methods, camera types, and flight altitudes. LiDAR-derived tree heights exhibited similarly high accuracy. Image-based and LiDAR-derived heights were strongly correlated across all trees at 120 m ( R 2 = 0.982–0.995; RMSE = 18–25 cm), confirming the reliability of SfM photogrammetry. However, incomplete canopy reconstruction in some 60 m datasets led to underestimation, highlighting the importance of sufficient image overlap for accurate 3D canopy modeling. These results demonstrate that UAS image-based point clouds can provide pecan tree heights comparable to LiDAR, offering a cost-effective approach for tree growth monitoring, orchard management, and precision agriculture applications.
Why it matches plant phenotyping methodsUAS画像測量とLiDARを用いた pecan 樹高推定法を系統的に比較・検証しており、植物形態形質の取得が研究の中心である。
abstractThis study provides one of the first systematic evaluations of UAS-based structure-from-motion (SfM) photogrammetry and UAS-mounted LiDAR for estimating pecan tree height.
Abstract Rice disease identification is a critical technique for ensuring yield and quality in precision agriculture. However, complex field backgrounds, subtle lesion features, and similar symptomatic manifestations have led to low accuracy and poor robustness in traditional classification methods. To address these issues, this study proposes an improved ConvNeXt network model (Improve‑ConvNeXt) for the fine classification of rice diseases under field conditions. A high‑quality dataset containing six categories (healthy rice, rice blast, brown spot, bacterial leaf blight, bacterial leaf streak, and bacterial grain rot) was constructed from field images and public datasets, with a total of 5,663 samples. Using ConvNeXt‑Tiny as the backbone, the model integrates a Hybrid Attention Transformer (HAT) to enhance the perception of lesion regions and key channels, and introduces Spatial and Channel Reconstruction Convolution (SCConv) to reduce feature redundancy and strengthen effective information expression. Experiments show that the Improve‑ConvNeXt model achieves 96.27% accuracy on the test set, which is 4.85% higher than that of the original ConvNeXt and significantly outperforms ResNet and DenseNet. The precision, recall, and F1‑score reach 95.84%, 96.11%, and 95.95%, respectively. Confusion matrix and Grad‑CAM visualization prove that the model can accurately focus on lesion areas and effectively distinguish similar diseases. This method provides high precision and strong generalization for rice disease identification in complex field environments, and offers a reliable technical reference for intelligent monitoring and precise management of rice fields.
Why it matches plant phenotyping methodsイネの病徴画像から病害状態を分類する画像・深層学習手法を開発し、データセットと性能比較で技術的に検証しているため、植物フェノタイピング手法が中心です。
abstractthis study proposes an improved ConvNeXt network model (Improve‑ConvNeXt) for the fine classification of rice diseases under field conditions.
Among carnivorous plants, the Venus flytrap (Dionaea muscipula) is known for its rapid (<1 s) trap closure. Although buckling instability, hydrostatic pressure, and hydroelastic coupling have all been proposed to be involved, the nature of this process and the relationship between trap size and curvature remain elusive. Here, we monitored the closure of Venus flytraps and performed micro-CT scanning and 3D reconstruction, revealing that increasing angular velocity was correlated with higher values of a non-dimensional shape index. Based on these experimental data, we constructed a geometric model of the trap that takes leaf orientation into account. We found that leaf curvature is dependent on leaf size, a relationship we denote as a size-curvature constraint. We further propose a curvature design derived from differential deformations of a two-layer model of the leaf, which could be a powerful tool to control the curvatures of soft and bending surface structures in the field of biomimetics.
Why it matches plant phenotyping methodsマイクロCTと3D再構成で葉の閉鎖運動・曲率を定量化し、幾何モデルでサイズ–曲率関係を推定することが研究の中心であり、植物形態・運動状態のフェノタイピング手法に該当する。
abstractHere, we monitored the closure of Venus flytraps and performed micro-CT scanning and 3D reconstruction, revealing that increasing angular velocity was correlated with higher values of a non-dimensional shape index.
Reproduction assets foundThe paper's Data Availability statement points to an authors' GitHub page hosting all data files and related rendering files for the Venus flytrap closure measurements and 3D reconstructions, matching an allowed URL.Dataset · publicAll data files and related rendering files are available from the github ( https://satorutsugawa.github.io/flytrap_geometric_model_datashare/) .Open asset ↗githublines:105-144Code / dataset availability confirmedEurope PMC · OpenAlex · checked 5 Sept 2026
The vertical heterogeneity of rice canopy structure limits the accuracy of inverting leaf physicochemical parameters using traditional radiative transfer models, while LiDAR-based 3D reconstruction remains costly for large-scale applications. To address these challenges, this study proposes a method for constructing 3D rice canopy scenes using "Precision Mode" and "Rapid Mode" strategies. The Precision Mode builds detailed structural models based on measured morphological parameters, validated via the LESS 3D radiative transfer model. To overcome the limitations of obtaining detailed morphology via UAV remote sensing, the Rapid Mode employs machine learning algorithms-specifically Support Vector Machine (SVM), Random Forest (RF), and XGBoost-to map easily accessible parameters (LAI, Above-ground Biomass, Plant Height, and Transplanting Date) to detailed 3D structural parameters. Results indicate that XGBoost achieves the highest accuracy in the Rapid Mode. Furthermore, simulated spectra under both modes showed high consistency with measured spectra, yielding average RMSE values of 0.0104 (R 2 = 0.9965) for the Precision Mode and 0.0307 (R 2 = 0.9694) for the Rapid Mode. Although the spectral accuracy of the Rapid Mode is slightly lower, its modeling efficiency is significantly enhanced, retaining a strong capability to reproduce spectral response characteristics across growth stages. This approach provides an effective tool for analyzing vertical spectral response mechanisms and offers an efficient data simulation scheme for UAV remote sensing parameter inversion based on 3D radiative transfer models.
Why it matches plant phenotyping methodsイネ群落の3D構造を構築・推定する手法を開発し、放射伝達モデルと実測スペクトルで検証しており、植物形質の取得・再現が研究の中心です。
abstractthis study proposes a method for constructing 3D rice canopy scenes using "Precision Mode" and "Rapid Mode" strategies.
Reproduction assets foundThe paper's Data Availability statement says the collected phenotype/structural/spectral data are publicly available on the authors' GitHub repository (allowed URL), while the analysis code is only available from the corresponding author upon request (request_only, no public URL).Dataset · publicThe data collected and used in this study are publicly available at: https://github.com/baijc4095-code/2024data . The code used for analysis can be obtained from the corresponding author upon reasonable request.Open asset ↗baijc4095-code/2024datalines:240-256Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 5 Sept 2026
Abstract Climate change increasingly threatens global agriculture by intensifying abiotic stresses and destabilizing crop productivity, necessitating a deeper understanding of root-mediated traits governing resource acquisition and stress resilience. Here, we synthesize recent advances in root-centred plant phenomics, emphasizing how high-throughput phenotyping enables high-resolution, scalable characterization of complex root traits and robust comparative analysis across diverse genotypes and environments. Innovations in multimodal imaging, notably X-ray computed tomography, MRI, and machine learning-integrated rhizotrons, facilitate detailed reconstruction of root system architecture and its temporal dynamics under both controlled and semi-field conditions. Furthermore, root phenotyping is increasingly interpreted within an integrated whole-plant framework. The integration of organ-specific assessments with physiological phenomics leveraging spectral and thermal data enables the characterization of developmental plasticity and root-mediated processes, including water-use dynamics, nutrient acquisition, and canopy stress responses under heterogeneous field conditions. These approaches link root traits such as rooting depth and spatial distribution to canopy-level physiological responses under stress. Despite these advances, significant bottlenecks persist in data interoperability, analytical scalability, and protocol standardization. Future progress will require integration of root phenomics with genomics, predictive modelling, and digital twin frameworks to improve resource-use efficiency, yield stability, and climate resilience in global cropping systems.
Why it matches plant phenotyping methods根系フェノタイピングの高スループット画像化、計算ツール、機械学習統合、データ標準化を中心に扱う方法論レビューであり、植物形質の取得・解析手法が主題である。
titleAdvances in root phenotyping: high-throughput imaging, computational tools, and integrative approaches for crop improvement.
Forest digital twins play a crucial role in modern precision forestry by supporting biomass estimation and carbon cycle monitoring. However, existing 3D reconstruction methods struggle to simultaneously achieve metric-level structural accuracy and visual realism in complex understory environments. This study proposes a semantically constrained 3D Gaussian Splatting framework that fuses handheld LiDAR point clouds with unmanned aerial vehicle imagery. First, a multi-modal fusion mechanism is constructed to extract geometric anchors from registered LiDAR data for precise 3DGS spatial initialization, which mitigates rendering artifacts and geometric drift caused by poor initialization in purely visual methods. Second, a semantic regularization optimization strategy is proposed to realize differentiated modeling of tree trunks and canopies, effectively balancing the structural accuracy of rigid trunks and the photorealistic rendering of non-rigid canopies. Experiments conducted on three study plots demonstrate that the proposed approach achieves an average PSNR of 24.94 dB, SSIM of 0.773, and LPIPS of 0.231 across all plots, outperforming standard NeRF and baseline 3DGS, while enabling DBH estimation with R2 = 0.848 and RMSE = 2.705 cm. This method provides a solution for high-fidelity forest digital twin construction in open-canopy forest environments such as urban and campus forests.
Why it matches plant phenotyping methodsLiDAR・UAV画像を統合した3D再構成法を開発し、樹幹・樹冠の構造モデル化とDBH推定を評価しており、植物形質取得が中心的な技術貢献である。
abstractThis study proposes a semantically constrained 3D Gaussian Splatting framework that fuses handheld LiDAR point clouds with unmanned aerial vehicle imagery.
Accurate and efficient 3D reconstruction of trees is of paramount importance for studying forest spatial structures and dynamic resource patterns, optimizing forest management, protecting environments, and analyzing carbon cycles. Currently, Light Detection and Ranging (LiDAR) remains the dominant method for generating 3D models of forest scenes. However, with advancements in computer vision, photogrammetry has emerged as a crucial tool for forest inventory and 3D reconstruction due to its cost-effectiveness. Nevertheless, in practical forestry applications, traditional photogrammetry often suffers from low reconstruction efficiency and poor quality during feature extraction and matching. These issues stem from the complex structure of forest scenes, severe occlusion, and repetitive texture patterns. To address these challenges, this paper proposes an improved 3D tree reconstruction approach based on images, integrating deep learning-based methods. In the sparse reconstruction stage, we utilize the ALIKED (A LIghter Keypoint and descriptor Extraction network with Deformable transformation) algorithm and construct an image pyramid to extract multi-scale robust features. Furthermore, by combining the LightGlue matching algorithm with a neighborhood search constraint strategy, we enhance the stability of camera pose recovery while reducing redundant computations. Experimental results demonstrate that our method outperforms traditional algorithms in both accuracy and robustness regarding image matching. Compared to baseline models, the proposed approach increases the number of feature points by approximately 50% with a more widespread distribution, improves matching accuracy by 4% to 8%, and achieves a 100% image registration rate. Consequently, under the condition of maintaining equivalent re-projection errors, the subsequent sparse point clouds exhibit an average track length increase of 0.6 to 1.4 and a density increase of up to 1.2 times. Notably, this method effectively mitigates artifacts and spurious reconstructions caused by pose drift in forest photogrammetry.
Why it matches plant phenotyping methods森林内の樹木の3次元形態を画像から再構成する特徴抽出・マッチング手法を開発し、精度と頑健性を評価しており、植物形態の取得方法が研究の中心である。
abstractthis paper proposes an improved 3D tree reconstruction approach based on images, integrating deep learning-based methods.
Three-dimensional (3D) procedural plant architecture models have emerged as an important tool for simulation-based studies of plant structure and function, extracting plant architectural parameters from field measurements, and for generating realistic plants in computer graphics. However, measuring the architectural parameters for these models at the field and population scales remains prohibitively labor-intensive. We present a novel algorithm that generates the 3D plant architecture from an image, to create a functional structural plant model from an image that reflects organ-level geometric and topological parameters, providing a more comprehensive representation of the plant’s architecture. Instead of using 3D sensors or processing multi-view images with computer vision to obtain the 3D structure of plants, we propose a method that generates token sequences containing a procedural definition of the plant architecture. This work uses only synthetic images for training and testing, where “exact” architectural parameters were known, which allowed for testing of the hypothesis that organ-level architectural parameters could be extracted from imagery data using a vision language model (VLM). A synthetic dataset of cowpea plant images was generated using the Helios 3D plant simulator, with the detailed plant architecture encoded in XML files. We developed a plant architecture tokenizer for the XML file defining plant architecture, converting it into a token sequence that a language model can predict. Then, a VLM was trained to predict plant architecture token sequences from images. Our results demonstrate that the model can predict plant architecture tokens with an F1 score of 0.73 in a teacher-forcing method. Evaluation of the model was performed through autoregressive generation, achieving a BLEU-4 score of 94.00% and a ROUGE-L score of 0.5182. Our model achieves lower MAPE than feature regression-based methods in estimating bulk plant-level traits that require understanding of the occluded 3D structure of the plant, such as leaf count and leaf area. We conclude that generating plant architecture and parameter extraction from synthetic imagery are feasible using a VLM approach, supporting future extension to real imagery.
Why it matches plant phenotyping methods画像から器官レベルの植物構造と形態形質を抽出するVLM手法の開発・評価が中心であり、植物フェノタイピング手法に該当する。
abstractWe present a novel algorithm that generates the 3D plant architecture from an image, to create a functional structural plant model from an image that reflects organ-level geometric and topological parameters
Reproduction assets foundThe paper's footnotes explicitly state that the authors' code is available on GitHub and the synthetic cowpea image/XML dataset is available on Hugging Face, both paper-specific and publicly actionable. The Helios URL is a generic third-party simulator library, not a paper-specific asset.Code · public1. ^ Code is available at: https://github.com/GEMINI-Breeding/Image2PlantArchitecture .Open asset ↗GEMINI-Breeding/Image2PlantArchitecturelines:600-676Dataset · public2. ^ Dataset is available at: https://huggingface.co/datasets/heesup/Cowpea-Architecture-XML .Open asset ↗heesup/Cowpea-Architecture-XMLlines:600-676Code / dataset availability confirmedOpenAlex · arXiv · checked 15 Sept 2026
Accurate estimation of wheat spike volume is important for yield component analysis and stress resilience assessment, yet field-based measurement remains challenging. Active 3D sensing methods such as Light Detection and Ranging (LiDAR) or time-of-flight (ToF) are sensitive to plant motion or poorly suited to outdoor conditions, while 3D reconstructions are computationally expensive. Direct 2D image processing would offer computational advantages, but image-based models lack explicit geometric information. We therefore propose a hybrid 2D-3D approach with knowledge distillation during training while enabling efficient image-only inference. First, we train a rigid-invariant point cloud network using distance-based histogram features to obtain pose-robust geometric representations. We then combine the 3D model with a proposed multi-view image-based regulated Transformer (RT) in an ensemble architecture. Finally, we distill the ensemble knowledge into a purely image-based student model using either feature-based or label-based distillation. The two distilled RTs reduce the mean absolute error (MAE) from 654.31 mm$^3$ of the non-distilled RT to 639.93 mm$^3$ and 644.62 mm$^3$, and increase correlation from 0.76 to 0.77 and 0.82, respectively. At the same time, inference time is reduced from 160 ms to 1.4 ms per spike. Distillation further mitigates volume-dependent bias and reshapes the latent representation of the image model toward a geometry-aware shape. Our results demonstrate that 3D-informed training of a 2D Transformer allows for scalable and efficient spike volume estimation for high-throughput field phenotyping.
Why it matches plant phenotyping methods小麦穂の体積を画像・3D再構成・知識蒸留で推定する手法の開発と性能評価が中心であり、高スループット植物フェノタイピングへの応用も明示されている。
abstractWe therefore propose a hybrid 2D-3D approach with knowledge distillation during training while enabling efficient image-only inference.
Reproduction assets foundThe paper explicitly states that links to its wheat spike dataset (multi-view images and 3D scans) and its analysis code are available via the authors' project webpage, which is an allowed URL. Other URLs (pyrender, CORDIS projects) are generic libraries or unrelated funding projects, not paper-specific assets.Dataset · publictance of around 2.5 m with a ground sampling distance of 0.3 mm (Fig. S1 a). The tagged and imaged spikes (Fig. S1 b) were sampled and ground truth volumes were acquired with a 3D light scanner (Shining 3D Einscan-SE V2, SHINING3D, Hangzhou, China) following the protocol of [ 76 ] . Links to the dataset and code can be found at https://oliviazum.github.io/3DKD-wheat/ . Detailed information about the dataset can be found in Sec. A .
3.3 Data Pre-Processing
Field images contained approximately 300-500 spikes per genotype within a plot of about 1.5 m 2 m^{2} . To reduce background inference, spike detection was first performed, and all subsequent processing was restricted to the detected regioOpen asset ↗lines:91-104Plant phenotyping relevance match · UnverifiedEurope PMC · OpenAlex · checked 13 Sept 2026
Volume electron microscopy based on serial sectioning allows for three-dimensional (3D) visualization and analysis of the internal structures of tissues, cells, and organelles. One such technique, focused ion beam (FIB) scanning electron microscopy (SEM), has the advantages of nanoscale sectioning and high z-resolution, but the disadvantage of limited volume processing. Because of this limitation, targeting localized objects by FIB-SEM is difficult. Here, we developed a FIB-SEM observation workflow that enables the analysis of the filiform apparatus of synergid cells enclosed in the Arabidopsis ovule. In this protocol, plant samples are stained, embedded, trimmed, and carbon-coated while maintaining their orientation within the tissue. Then, sequential observations are performed using Cut & See function of FIB-SEM, followed by image processing for 3D reconstruction. Utilization of multi-scanning and image cropping from high-resolution data helps to identify localized targets within plant tissue. The filiform apparatus, which is an invaginated cell wall structure of the synergid cells, shows distinct contrast in each image, allowing for segmentation using brightness-based binarization. Such segmentation avoids the need to manually trace complex structures and facilitates 3D reconstruction by volume electron microscopy. Key features • Sampling and trimming of the resin block enable directionally loading in FIB-SEM. • Multi-scanning by FIB-SEM and target extraction by image processing software enable 3D reconstruction of local areas within the sample block. • Binarization using distinctive brightness of cellular structures enables segmentation without manual tracing of complex structures such as the filiform apparatus cell wall.
Why it matches plant phenotyping methods植物組織内の構造をFIB-SEMと画像処理で3D再構成・セグメンテーションするワークフローを開発しており、フィリフォーム装置形態という植物器官形質の取得が中心である。
abstractHere, we developed a FIB-SEM observation workflow that enables the analysis of the filiform apparatus of synergid cells enclosed in the Arabidopsis ovule.
High-precision plant phenotyping requires efficient 3D reconstruction methods with high geometric quality. 3D Gaussian Splatting (3DGS) has recently emerged as a promising approach for real-time 3D reconstruction, achieving impressive visual quality. However, in crop environments dominated by monochromatic and low-texture regions, existing 3DGS methods often produce ambiguous geometries and fail to recover geometry-consistent 3D surfaces. To address these limitations, we propose LV-3DGS (Leafy Vegetables-3DGS), an optimized 3DGS-based framework tailored for the reconstruction of leafy vegetable scenes. First, a blurred reconstruction module is introduced to mitigate reconstruction artifacts caused by camera motion blur during multi-view image acquisition. Second, we propose a planar optimization strategy and design both local and global geometric consistency regularizations to optimize the model, thereby improving the surface reconstruction quality and geometric accuracy. Third, based on an analysis of individual Gaussian contributions, a contribution-based pruning strategy is developed to selectively remove inaccurate geometric components, achieving accurate scene geometry while reducing memory consumption and improving rendering efficiency. In addition, a quantitative geometric evaluation method is proposed for assessing reconstruction quality. Experimental results demonstrate that the proposed method achieves the highest accuracy among the tested baselines, with SSIM, PSNR, and LPIPS reaching 0.94, 34.53 dB, and 0.11, respectively. Moreover, the geometric consistency (GC) metric attains 0.317 cm. Finally, phenotypic parameters are measured from the reconstructed leafy vegetable point clouds. Compared with ground truth measurements, the proposed approach yields coefficients of determination (R2) of 0.9959, 0.9651, and 0.9895 for plant height, leaf number, and leaf area, respectively. These results are significantly outperform to some existing phenotyping methods, providing a new methodology and technical solution for high-precision, low-cost, and high-throughput crop phenotyping.
Why it matches plant phenotyping methods葉菜類の3D再構成とそこからの形質抽出を中心に、手法開発・幾何評価・実測値との検証を行っているため、植物フェノタイピング手法として中心的である。
abstractwe propose LV-3DGS (Leafy Vegetables-3DGS), an optimized 3DGS-based framework tailored for the reconstruction of leafy vegetable scenes.
GrapevineNeRF / 3D Gaussian SplattingLiDAR / point cloudRGB / grayscaleFruitStem / branchPose / keypoint estimation2D/3D reconstructionSegmentation
• End-to-end pipeline from neural reconstruction to physical grape berry manipulation. • Efficient point clouds generation using NeRF and the metric scale derived directly from robot kinematics. • RANSAC sphere fitting achieves 92.1% berry detection precision without annotated training data. • Stem-aligned 6-DoF pose optimization improves end-to-end grip success by 17.2%. Table grape thinning requires selective removal of 20–40% of berries from dense clusters. In practice, workers decide which berries to remove by considering both the approximate berry count and local 3D spatial characteristics such as crowding and relative positioning. Automating this task is challenging because conventional 2D image-based approaches suffer from occlusion-related counting errors and lack explicit 3D spatial information necessary for reliable manipulation. We propose a robot-integrated vision pipeline that reconstructs grape bunch structure from posed multi-view RGB images. Neural Radiance Fields (NeRF) is used to learn a volumetric scene representation, from which a dense, low-noise point cloud is extracted via depth back-projection, and RANSAC-based geometric fitting models individual berries and stems, enabling berry-level segmentation and orientation estimation for manipulation planning. The perception pipeline uses an eye-in-hand RealSense D405 camera mounted on a Fanuc CRX-5iA collaborative robot. Camera poses are derived from the robot kinematic chain, allowing the reconstructed point cloud and detected berry centers to be expressed directly in the metric robot base frame without external scale recovery. In robot-mounted RealSense D405 experiments on 10 grape bunches, RANSAC sphere fitting achieved a counting MAE of 0.50 berries, RMSE of 0.71 berries, and mean center localization error of 2.71 mm. On a 52-bunch benchmark, RANSAC sphere fitting outperforms the learning-based SoftGroup++ method for 3D berry instance segmentation (92.1% vs 82.8% average precision) without requiring annotated training data. In 35 manipulation trials, the system achieved an 85.7% pre-grasp reachability rate and an 83.3% conditional target success rate demonstrating an end-to-end pipeline from neural reconstruction to manipulation-ready berry poses.
Why it matches plant phenotyping methodsブドウ房の3D再構成、ベリー分割・計数・位置推定を中核とするロボット統合型フェノタイピング手法であり、技術性能も定量評価している。
abstractWe propose a robot-integrated vision pipeline that reconstructs grape bunch structure from posed multi-view RGB images.
Background High-throughput automated image analysis holds great promise for plant breeding by enabling faster, more accurate assessment of traits relevant to crop improvement. Imaging-based systems, such as the CropReporter, allow automated quantification of photosynthetic parameters like PSII efficiency under ambient light from a top-down 2D perspective. However, standard analysis tools average values across the 2D top view, overrepresenting upper leaves and underrepresenting those in the lower canopy. Upper leaves may occlude lower ones, and due to the pinhole projection of the camera, lower leaves of the same size appear smaller in the image. Consequently, vertical heterogeneity in PSII efficiency within the canopy cannot be resolved using a single 2D image. Results To address these issues, we integrated top-view PSII efficiency data (by CropReporter) with 3D structural data from RGB point clouds (by MaxiMarvin). Alignment accuracy between MaxiMarvin and CropReporter was high, with R² ≥ 0.98 for the x-axis and R² ≥ 0.99 for the y-axis. The method was tested using Chenopodium quinoa, Glycine max, and Solanum tuberosum, exposed to salinity, waterlogging and drought stress respectively. In Chenopodium quinoa, it allowed precise determination of when senescence began in the lower leaves. In Solanum tuberosum, the reduction in PSII efficiency by drought was the same for all leaf layers, while in Glycine max, waterlogging stress most strongly affected the middle layer of the canopy. Conclusions This framework enables the 3D mapping of PSII efficiency across the vertical plant profile by combining top-view chlorophyll fluorescence imaging (CropReporter) with 3D structural data (MaxiMarvin). It reveals vertical variation in photosynthetic activity across canopy layers. With standard 2D chlorophyll fluorescence imaging it is difficult to distinguish between non-photosynthetic tissues like flower heads and lower layers of leaves, that might have the same PSII values. Using height-based filtering, taking data from the 3D mapping, such distinction can be made with the method presented in this paper. This allows estimating the PSII efficiencies of leaves only. By capturing layer-specific responses to abiotic stress and developmental changes, the method provides physiologically relevant input for crop growth modelling and highlights the importance of accounting for canopy structure in photosynthetic analyses.
Why it matches plant phenotyping methods2Dクロロフィル蛍光によるPSII効率を3D植物構造へ投影し、群落層別の葉の生理形質を推定する手法の開発・検証が中心である。
abstractTo address these issues, we integrated top-view PSII efficiency data (by CropReporter) with 3D structural data from RGB point clouds (by MaxiMarvin).
Reproduction assets foundThe authors state that the analysis scripts (2D–3D alignment pipeline) and the phenotyping data used in the study are included with the publication as supplementary material, accessible via the article DOI. This is a paper-specific, publicly available asset containing the authors' analysis code and data.Dataset · publicThe scripts and the data that were used in the current study are available and added to this publication.Open asset ↗lines:143-180Plant phenotyping relevance match · UnverifiedCrossref · checked 13 Sept 2026
Leaf Area Index (LAI) is a key biophysical parameter for characterizing terrestrial vegetation dynamics and land surface processes. Time-series MODIS LAI products are widely used in ecological and land-related research, but cloud contamination and sensor noise lead to widespread spatio-temporal gaps, limiting their ability to support long-term, consistent vegetation monitoring over large areas. To address this issue, this study proposes a novel self-supervised LAI reconstruction framework (SSLAI) for generating gap-free and ecologically consistent LAI datasets across China. The framework integrates cross-modal environmental fusion, multi-scale spatio-temporal modeling, and adaptive phenological constraints to ensure the reconstructed LAI aligns with realistic vegetation growth rhythms. SSLAI outperforms seven traditional and state-of-the-art deep learning methods, maintaining a root mean square error (RMSE) below 0.20 even with 16 missing time windows. Field validation confirms its high accuracy, with a coefficient of determination (R2) of 0.885 and an RMSE of 0.477. Furthermore, SSLAI’s response to meteorological changes aligns with ecological principles, demonstrating favorable physical interpretability and ecological rationality. The reconstructed LAI exhibits superior spatial completeness and temporal consistency compared with MODIS, VIIRS, and GLASS products, and performs robustly under variable climatic conditions. This study provides an effective self-supervised solution for MODIS LAI gap-filling over large regions, and the generated high-quality LAI dataset can serve as a reliable data foundation for vegetation dynamics monitoring, land surface modeling, and global change research.
Why it matches plant phenotyping methods植物群落のLAIという明示的な植物形質を対象に、欠測補完の計算手法を開発し、既存手法との比較および野外検証を行っているため、方法開発・検証が中心である。
abstractthis study proposes a novel self-supervised LAI reconstruction framework (SSLAI) for generating gap-free and ecologically consistent LAI datasets across China.
Accurate quantification of forest coverage and combustible biomass (fuel load) is critical for wildfire risk assessment and ecosystem management. However, traditional methods relying on airborne LiDAR or field surveys are cost-prohibitive and time-intensive, while satellite imagery often lacks the vertical resolution required for canopy volume analysis. This paper proposes a novel, automated pipeline for rapid forest inventory using virtual remote sensing data derived from Google Earth Studio (GES). Our approach first generates low-altitude orbital imagery and camera poses for a target region. For dense 3D reconstruction, we employ Pi-Long, developed within the VGGT-Long framework. This model serves as a scalable extension of the Pi-3 feed-forward Transformer architecture. To address the inherent scale ambiguity in monocular reconstruction, we introduce a metric recovery module that aligns the reconstructed trajectory with GES ground truth poses via Sim(3) Umeyama optimization. The metric-scale point cloud is then orthogonally projected into Bird's-Eye-View (BEV) height and density maps. Finally, we employ a watershed-based segmentation algorithm combined with height variance analysis to classify tree species (conifer vs. broadleaf), calculate Leaf Area Index (LAI), and estimate total fuel load. Experimental results demonstrate that this pipeline offers a scalable, cost-effective alternative to physical scanning, enabling near-real-time estimation of forest biomass with high geometric consistency.
Why it matches plant phenotyping methods森林の3D再構成、BEVマップ、分割・高さ分散解析を組み合わせ、LAIと燃料量という植物・群落形質を推定するパイプライン自体が中心的な技術貢献であるため。
abstractThis paper proposes a novel, automated pipeline for rapid forest inventory using virtual remote sensing data derived from Google Earth Studio (GES).
Abstract To address the problem of fine branch identification and pruning decision for dormant apple trees, this study proposes a 3D point cloud branch recognition method integrating Neural Radiance Fields (NeRF) and the PointNeXt network. This method employs the neural radiance field theory to construct a point cloud model of apple trees, achieving fine detail representation and providing a high-precision, high-standard dataset for subsequent branch pruning experiments. First, a panoramic video is captured by circling the fruit tree, and a multi-view image sequence is obtained through frame sampling. Subsequently, the Structure from Motion (SfM) algorithm is employed for sparse reconstruction to recover the pose information of the images. On this basis, a neural radiance field model is trained. Hierarchical sampling is performed using ray casting, and the sampled points, combined with positional encoding, are fed into a multi-layer perceptron (MLP). The radiance field is then generated via volume rendering, from which a high-fidelity 3D point cloud model of the fruit tree is derived. Finally, the point cloud is processed using the PointNeXt semantic segmentation network to achieve the identification and segmentation of branches to be pruned and branches to be retained. To verify the effectiveness of the method, this study reconstructed point cloud models of dormant apple trees and selected 10 of them for experimental analysis. The algorithm achieved an average overall recognition accuracy of 75.15% and an average false negative rate (FNR) of 24.85%. The experimental results demonstrate that the proposed method constructs a 3D point cloud model with multi-scale, multi-modal, and high-precision phenotypic information at a relatively low cost. It not only overcomes the limitations of traditional 3D reconstruction methods, such as insufficient point cloud accuracy and difficulty in accurately identifying thin branches, but also effectively mitigates the high misrecognition rate observed in conventional branch recognition approaches. This provides technical support for unmanned agricultural machinery pruning in orchards and holds significant implications for achieving precision agriculture and sustainable development.
Why it matches plant phenotyping methodsNeRFとPointNeXtを用いてリンゴ樹の3D点群を構築し、剪定対象枝を認識・分割する手法が研究の中心であり、植物の形態・構造状態を直接推定して性能評価している。
abstractthis study proposes a 3D point cloud branch recognition method integrating Neural Radiance Fields (NeRF) and the PointNeXt network.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
X-ray / CTRootMorphology / geometry measurement2D/3D reconstructionRoot system architecture
Summary Plant–microbe interactions are inherently spatial, yet the physical structure of the soil and rhizosphere is rarely treated as a mechanistic variable in experimental design. X‐ray computed tomography (X‐ray CT) enables nondestructive, three‐dimensional, and time‐resolved imaging of intact root–soil systems, providing direct access to the structural context in which plant–microbe interactions occur. Rather than a secondary imaging technique, X‐ray CT can offer a wealth of data as a primary experimental platform for future plant–microbe research. Here, we highlight key structural traits that X‐ray CT can quantify and discuss how they may shape microbial behaviour, plant immune responses, and disease outcomes. We expand on how X‐ray CT could be employed in future to provide a framework to disentangle direct microbial effects from indirect, structure‐mediated feedbacks. For breeding and management, it could enable selection for root traits and soil practices that engineer favourable microhabitats rather than targeting organisms in isolation. Despite this potential, broader adoption will require overcoming current limitations related to access to instrumentation, analytical expertise, and the integration of structural data with biological measurements. Overall, we suggest that resolving these issues will enable the integration of X‐ray CT‐derived structure with molecular, microbiome, and modelling approaches to enable the development of digital rhizospheres, offering a pathway from descriptive observations to predictive, structure‐aware in silico frameworks in plant–microbe research.
Why it matches plant phenotyping methodsX線CTを用いて根・土壌系の構造形質を定量する方法を、植物・微生物相互作用研究の主要な実験プラットフォームとして論じる方法論レビューであり、植物フェノタイピング手法が中心です。
abstractX‐ray computed tomography (X‐ray CT) enables nondestructive, three‐dimensional, and time‐resolved imaging of intact root–soil systems, providing direct access to the structural context in which plant–microbe interactions occur.
Abstract Spatio-temporal fusion (STF) has been widely used across various remote sensing applications, including environmental monitoring, land cover change detection, and water resource management by integrating multi-sensor data with different spatial and temporal resolutions. The objective of this study was to generate spatially and temporally fine-resolution imagery and to evaluate the performance of multiple STF algorithms and Consistent Adjustment of the Climatology to Actual Observations (CACAO) post-processing for parcel-level crop monitoring. The study was conducted in a soybean field located in Anseong, South Korea, using Planet SuperDove satellite imagery which has 3 m spatial resolution and near-daily temporal resolution, and Phantom 4 Multispectral Unmanned Aerial Vehicle (UAV) data which has 0.05 m spatial resolution, downscaled to target resolution 0.5 m, and 1–4 week irregular temporal resolution. Relative radiometric normalization was applied, followed by the implementation and comparison of four STF algorithms— Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), Fitting, spatial Filtering and residual Compensation (FitFC), Flexible Spatiotemporal Data Fusion (FSDAF), and Variation-based Spatiotemporal Data Fusion (VSDF)—within a 4-fold cross-validation framework. CACAO post-processing was then employed to reconstruct temporally continuous NDVI trajectories, from which growth metrics such as Vegetation Growth Metrics (VGM)85 and VGMmax were derived. The validation results indicated that ESTARFM achieved the highest Normalized Difference Vegetation Index (NDVI) performance among the evaluated algorithms, with an Root Mean Square Error (RMSE) of 0.113 and the Universal Image Quality Index (UIQI) of 0.697, and CACAO further improved the results, with CA-ESTARFM providing the highest NDVI accuracy, with an RMSE of 0.108 and a UIQI of 0.740. NDVI histogram and spatial analyses demonstrated that CA-ESTARFM achieved the most consistent agreement with UAV observations while preserving fine-scale spatial heterogeneity. In addition, intra-field vegetation assessment using VGM85 and VGMmax confirmed that CA-ESTARFM enhanced the reliability of crop growth evaluation compared to simple linear interpolation of UAV observations. The proposed framework demonstrates strong potential for applications in comprehensive crop monitoring, precision agriculture management, and yield forecasting.
Why it matches plant phenotyping methods衛星・UAV画像の時空間融合アルゴリズムを比較検証し、NDVI軌跡から作物生育指標を抽出する方法が研究の中心であるため、植物フェノタイピング手法として採択する。
abstractThe objective of this study was to generate spatially and temporally fine-resolution imagery and to evaluate the performance of multiple STF algorithms and Consistent Adjustment of the Climatology to Actual Observations (CACAO) post-processing for parcel-level crop monitoring.
Sudanian savannas remain underexplored in terms of utilizing close-range photogrammetry (CRP) for assessing tree characteristics, leaving a gap in ecological research. This study evaluates the performance of automatic stem modeling techniques using CRP-generated point clouds for 30 trees from five savanna species. Two labeling methods, a machine learning-based approach (StemML) and a flatness/vertical structure-based method (StemFlat), were used to extract stem points. We applied three diameter estimation techniques: convex-hull line fitting (CHM), least squares circle fitting (LSM), and Ransac circle fitting (RANSAC), comparing their results against field measurements using root mean square error (RMSE), bias and the coefficient of determination R2. The combination of StemML and CHM yielded the best performance, with an RMSE of 2.1 cm (5.8%), R2 of 0.983 and a bias of −0.30 cm, accurately identifying 93% of stem segments. Diameter estimation accuracy varied with height, with optimal alignment between CRP-derived profiles and manual measurements occurring between 0.5 and 2.5 m. These findings demonstrate CRP’s potential for modeling savanna tree stems and highlight the importance of method selection in ensuring reliable measurements.
Why it matches plant phenotyping methods近距離写真測量による樹幹点群の抽出・モデル化と直径推定手法を開発・比較し、野外測定で性能検証しているため、植物形質計測手法が研究の中心である。
abstractThis study evaluates the performance of automatic stem modeling techniques using CRP-generated point clouds for 30 trees from five savanna species.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 5 Sept 2026
Eggplant (Solanum melongena L.) is a widely cultivated vegetable crop worldwide, occupying an important position in the agricultural industries of Asia, the Middle East, and Southern Europe. Its significance extends beyond agricultural economics to diverse dimensions such as dietary nutrition, rendering it of considerable research and application value. Traditional crop phenotyping methods suffer from low efficiency, substantial manual errors, and a tendency to damage tender seedlings, while existing three-dimensional phenotyping techniques face challenges including strong background interference and large data volumes. These dual constraints limit the accuracy and application feasibility of seedling phenotyping. To address these issues, this study proposes a non-destructive phenotyping method for eggplant seedlings, with the improvement of the PointNet++ architecture as its core and point cloud background purification as a key preprocessing step, aiming to enhance eggplant breeding efficiency and seedling screening accuracy. The raw point clouds first undergo background purification to actively remove seedling tray points, thereby improving point cloud purity and reducing data size. Concurrently, based on the PointNet++ model, we develop an improved point cloud segmentation model, EggplantPointNet++, by introducing multi-scale residual blocks, integrating channel attention mechanisms, incorporating a global context module, and refining the feature propagation layer. In conjunction with the DBSCAN clustering algorithm, this approach achieves semantic and instance segmentation of eggplant seedling point clouds, with certain improvements in segmentation accuracy and model efficiency under small-scale and occluded scenarios. To validate the technical effectiveness, multiple comparative experiments and ablation studies were conducted. The results demonstrate that EggplantPointNet++ outperforms the original model, background purification preprocessing provides positive gains, and each improved module contributes positively. The final model achieves improvements in core metrics including Recall and F1-score. Based on the segmented point cloud data, this study calculates core phenotypic parameters including plant height, stem diameter, cotyledon angle, and cotyledon area. Using the technical system established in this study, we completed the time-series measurement of three-dimensional morphological changes in eggplant seedlings during the cotyledon stage, providing quantitative references for seedling growth assessment and superior plant selection.
Why it matches plant phenotyping methodsナス幼苗の3D点群から形質を抽出する非破壊フェノタイピング手法を開発し、比較実験・アブレーションで技術性能を検証しているため、方法が中心的である。
abstractthis study proposes a non-destructive phenotyping method for eggplant seedlings
Soil-borne vascular pathogens pose serious threats to agriculture with complex invasion strategies that remain poorly characterized compared to foliar pathogens. While foliar pathogens like Magnaporthe oryzae employ specialized appressoria to penetrate plant surfaces through a combination of mechanical force and enzymatic degradation, the invasion mechanisms of vascular pathogens that lack classical appressoria have remained largely theoretical. The nanoscale processes governing root penetration and colonization by these pathogens are particularly challenging to visualize due to technical limitations of conventional microscopy. Here we show, using phase-contrast X-ray computed microtomography and advanced microscopy, that Fusarium oxysporum (Fo) employs distinct mitogen-activated protein kinase (MAPK) cascades to orchestrate root invasion through unprecedented morphological plasticity. We identify previously undocumented appressoria-like structures that facilitate physical penetration, while demonstrating that Fo exhibits remarkable cellular adaptability, reducing hyphal diameter by more than 20-fold (from 5 μm to 220 nm) to navigate confined plant spaces, a dramatic morphological transition previously thought impossible. By using cellulase-deficient mutants, we demonstrate that cellulolytic activity is dispensable for surface breach and submicrometric hyphal colonization, establishing that mechanical force generation rather than enzymatic degradation is the primary determinant of successful host penetration. Three-dimensional reconstruction reveals a quantitative correlation between fungal proliferation and progressive embolism formation, with distinct MAPK pathways differentially regulating penetration force generation (Fmk1), osmotic adaptation during apoplastic colonization (Hog1), and directional growth toward vascular tissues (Mpk1). These findings provide a mechanistic framework for vascular wilt pathogenesis and reveal potential targets for controlling these economically devastating plant diseases.
Why it matches plant phenotyping methods位相コントラストX線マイクロトモグラフィーと3次元再構成を中核に、根の侵入・菌糸形態・塞栓形成という植物の病態と形態を定量化しており、病理学的機構研究であるものの表現型取得法の適用が実質的です。
titlePhase-contrast microtomography unveils mechanisms of root colonization by a vascular fungal pathogen
This study evaluates the performance of LiDAR sensing and UAV photogrammetry for three-dimensional canopy reconstruction and structural parameter estimation in precision agriculture. Experiments were conducted in Sicily (Italy) on Moringa oleifera Lam. and Ficus macrophylla subsp. columnaris, representing contrasting canopy architectures. LiDAR and UAV data were used to generate canopy models and estimate canopy height, volume, and vegetation density. A voxel-based approach was applied to LiDAR point clouds to analyze internal canopy structure. LiDAR significantly outperformed UAV photogrammetry, achieving lower errors in canopy height estimation (RMSE = 0.19–0.21 m vs. 0.52–0.60 m) and canopy volume (3.5–4.2% vs. 13.7–16.1%). UAV photogrammetry provided reliable estimates of canopy surface but underestimated structural parameters in dense vegetation due to occlusion effects. Differences were more pronounced in Ficus macrophylla than in Moringa oleifera, highlighting the influence of canopy complexity. These findings demonstrate that LiDAR-derived structural metrics can improve canopy characterization and support precision agriculture applications such as biomass estimation, irrigation planning, and canopy management in Mediterranean cropping systems.
Why it matches plant phenotyping methodsLiDARとUAVフォトグラメトリによる樹冠の3次元再構成と、樹冠高・体積・密度推定を比較検証しており、植物形質取得手法が研究の中心です。
abstractThis study evaluates the performance of LiDAR sensing and UAV photogrammetry for three-dimensional canopy reconstruction and structural parameter estimation in precision agriculture.
In root nodule symbiosis, symbiosome compartments accommodate nitrogen-fixing rhizobia inside the plant cell. Differentiated into bacteroids, the rhizobia are surrounded by a peribacteroid space and a plant-derived peribacteroid membrane, which separates them from the plant cytoplasm but allows signal and nutrient exchange between host and microbe. The morphological features of symbiosomes are primarily determined by ultrastructural single focal plane imaging, with limited information about spatial details. This study combines 2D and 3D imaging, using transmission electron microscopy and focused ion beam scanning electron microscopy as complementary techniques to analyse the symbiosome ultrastructure and organisation in Lotus japonicus wild-type plants. The 3D model of a mature colonised root nodule cell region demonstrates a dense, puzzle-like arrangement of symbiosomes relative to one another and adjacent plant organelles. The symbiosome shape and size depends on the orientation and number of bacteroids within the compartment and features connective tubular structures. Furthermore, vesicular structures, some likely of bacterial origin, were present at the interface. The study presents a multi-angled analysis of symbiosome-related structures, highlighting their volumes, spatial distribution, and pronounced compactness. Interface associated vesicles, protrusions and connective structures hint towards a dynamic and flexible system that contributes to the plant-microbe crosstalk.
Why it matches plant phenotyping methods植物根粒内の共生体の形態・体積・空間分布を、2D/3D電子顕微鏡で解析することが研究の中心であり、植物組織の構造的表現型を取得する方法論的応用に該当する。
abstractThis study combines 2D and 3D imaging, using transmission electron microscopy and focused ion beam scanning electron microscopy as complementary techniques to analyse the symbiosome ultrastructure and organisation in Lotus japonicus wild-type plants.
Three-dimensional (3D) phenotyping of plants plays a crucial role for understanding plant growth, yield prediction, and disease control. We present a pipeline capable of generating high-quality 3D reconstructions of individual agricultural plants. To acquire data, a small commercially available Unmanned aerial vehicle (UAV) captures images of a selected plant. Apart from placing ArUco markers, the entire image acquisition process is fully autonomous, controlled by a self-developed Android application running on the drone's controller. The reconstruction task is particularly challenging due to environmental wind and downwash of the UAV. Our proposed pipeline supports the integration of arbitrary state-of-the-art 3D reconstruction methods. To mitigate errors caused by leaf motion during image capture, we use an iterative method that gradually adjusts the input images through deformation. Motion is estimated using optical flow between the original input images and intermediate 3D reconstructions rendered from the corresponding viewpoints. This alignment gradually reduces scene motion, resulting in a canonical representation. After a few iterations, our pipeline improves the reconstruction of state-of-the-art methods and enables the extraction of high-resolution 3D meshes.
Why it matches plant phenotyping methodsUAV画像から植物個体の高解像度3D形状を再構成する手法を開発しており、植物フェノタイピングの取得・抽出方法が研究の中心である。
abstractWe present a pipeline capable of generating high-quality 3D reconstructions of individual agricultural plants.
Land management and stewardship teams continue to lack the tools to capture 3D spatiotemporal insights of the ecosystems they oversee. For wildfire management at the wildland-urban interface, teams face challenges in capturing vegetation growth over time after a fuel reduction program and connecting seasonal changes to the vegetation distribution across the treated area. Current approaches rely on triangle meshes or point clouds generated from photogrammetry or LiDAR surveys on drones or hiked traverses. However, the difficulties in optimizing these meshes lead to large triangles that inadequately approximate the bulk vegetation shape, and the point cloud data is often too sparse for local plant-scale understanding. To address this gap, we extend recent machine learning-based computer graphics techniques in 3D Gaussian Splatting (3DGS) to reconstruct scenes at the wildland-urban interface from handheld imagery with sufficient detail to identify species, capture individual leaves, recover plant stature, and disambiguate overhanging plant individuals. We develop a new method to match the 3DGS reconstruction of these scenes across months, associating plant growth across seasons interactively in 3D. To achieve the centimeter-level matching, we adapt the Umeyama algorithm and the iterative closest point algorithm from point cloud maps to the 3DGS scene, leveraging the probabilistic interpretation of the 3D Gaussian data structure and robustly handling visual and geometric changes associated with vegetation phenology over time. We have applied our method to recent pile burns at Stanford’s Jasper Ridge ’Ootchamin ’Ooyakma Biological Preserve at monthly intervals. We demonstrate differences in ecological response where some piles featured the unexpected return of a rare and threatened bushmallow, and others remained more barren. This pile burn microcosm implicates the need for plant-level 3D spatiotemporal models to understand ecosystem recovery to fire mitigation practices. Please visit the project page for the spatiotemporal alignment video and more information: https://danineamati.github.io/burn-ecorecovery.github.io/ Please visit the Stanford Data Repository for the field data: https://doi.org/10.25740/ws901xs0162
Why it matches plant phenotyping methods植物の3D画像再構成と時系列位置合わせ手法を開発し、植物の個体・葉・草丈・成長を抽出することが中心であるため、植物フェノタイピング手法研究に該当します。
abstractwe extend recent machine learning-based computer graphics techniques in 3D Gaussian Splatting (3DGS) to reconstruct scenes at the wildland-urban interface from handheld imagery with sufficient detail to identify species, capture individual leaves, recover plant stature, and disambiguate overhanging plant individuals.
To address the issue of 3D reconstruction information loss caused by occlusion during single-view camera acquisition of crop phenotypic parameters, this study proposes a detection method for tomato plant phenotypic parameters based on multi-view 3D point cloud reconstruction. The Kinect 2.0 sensor was employed to acquire point cloud data of tomato plants from three different viewpoints. Background noise was effectively removed using a combination of Conditional Filtering and Statistical Outlier Removal methods. By extracting surface normal features and calculating Fast Point Feature Histograms (FPFH), the Sample Consensus Initial Alignment (SAC-IA) and Iterative Closest Point (ICP) algorithms were utilized to accomplish coarse and accurate registration of the point clouds, respectively, ultimately achieving 3D reconstruction. Experimental results demonstrated that the reconstructed 3D model of the tomato plant was clear in outline and complete in structure. For the phenotypic parameters of plant height, canopy width, and leaf angle, the coefficients of determination (R²) between the calculated and manually measured values were 0.98, 0.94, and 0.89, respectively, with Root Mean Square Errors (RMSE) of 0.75 cm, 1.10 cm, and 4.43 °. Compared to single-view measurements, the accuracy of plant height and maximum canopy width derived from multi-view reconstruction increased by 15.31% and 13.12%, respectively. This method provides technical support for the rapid and accurate extraction of phenotypic parameters in tomato plants.
Why it matches plant phenotyping methodsトマトの草丈、キャノピー幅、葉角を抽出するマルチビュー3D点群再構成法を開発し、手動測定との精度検証も行っており、植物表現型取得が研究の中心である。
abstractthis study proposes a detection method for tomato plant phenotypic parameters based on multi-view 3D point cloud reconstruction.
Accurate soybean field phenotyping is increasingly important for breeding. However, traditional measurement methods are labor-intensive and subjective, while UAV-based approaches are challenged by complex backgrounds and densely distributed small targets. This study first develops UAV-ZSAR to transform oblique UAV images into horizontal-view images and reconstruct plant geometry. A lightweight point-based model, Soy-MOPNet, is then proposed for fast and parallel detection of soybean seeds and stem nodes. The model incorporates the proposed SDConv, optimized hierarchical dilated convolution (HDC) principles, and PBOS to enhance adaptive feature fusion, receptive field design, and multi-branch training stability, respectively. Based on the detected keypoints, six phenotypic traits are extracted in parallel, providing comprehensive support for field phenotyping, breeding selection, and precision agricultural management.
Why it matches plant phenotyping methodsUAV画像の幾何再構成、軽量点検出モデル、複数器官からの6形質抽出を開発しており、植物表現型取得・抽出手法が研究の中心である。
abstractThis study first develops UAV-ZSAR to transform oblique UAV images into horizontal-view images and reconstruct plant geometry.
To address the low efficiency of manual inspection for corn sowing quality, which is labor-intensive and time-consuming, this study proposes an automatic seedling-stage plant-spacing measurement method based on three-dimensional machine vision. A high-clearance mobile platform equipped with a ZED 2i stereo camera and an industrial computer was developed to acquire RGB images and depth information of corn seedlings in the field in real time. Using the YOLOv11-Pose model, plant keypoints were detected and localized; combined with camera calibration and 3D reconstruction techniques, inter-plant distances were computed automatically. A sowing-quality evaluation framework was then established to enable automated analysis of the Quality of Feed Index (QFI), Multiple Index (MUL), Miss Index (MI), and coefficient of variation. Experimental results indicate that the system operates effectively under three preset plant spacings (15 cm, 20 cm, and 25 cm), achieving a keypoint-detection mAP@0.5 of 0.990 and an mAP@0.5:0.95 of 0.989. In sowing-quality evaluation, the qualified indices produced by the system were 77.83%, 80.36%, and 82.46%, respectively, and the Quality of Feed Index (QFI), Multiple Index (MUL), Miss Index (MI), and coefficient of variation showed trends consistent with manual measurements. The proposed method enables efficient, nondestructive detection of seedling-stage plant spacing and sowing quality, providing reliable technical support for precision sowing and field management.
Why it matches plant phenotyping methods3Dマシンビジョン、姿勢検出、校正、3D再構成を組み合わせ、トウモロコシ個体間距離を自動推定・検証する手法が研究の中心である。
abstractthis study proposes an automatic seedling-stage plant-spacing measurement method based on three-dimensional machine vision.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Introduction Static networks often exhibit limited generalization on few-shot data, particularly given the scarce samples and unstructured background noise inherent to precision agriculture. To address these limitations, an adaptive recognition network for few-shot plant diseases and pests based on homeostatic neuromodulation and meta-plasticity (HNeuroNet) is proposed. Methods This framework incorporates dynamic plasticity inspired by biological systems to mitigate the data dependency paradox. First, a Neuro Modulatory Generator (NMG) is constructed utilizing a hypernetwork architecture. Simulating neurotransmitter gating mechanisms, affine transformation parameters are dynamically generated for feature channels based on support set samples. Consequently, instantaneous weight reconstruction is enabled without expensive gradient fine-tuning, thereby overcoming structural rigidity and catastrophic forgetting during rapid adaptation. Second, a Homeostatic Suppression Mechanism (HSM) integrating visual perception is introduced. Leveraging Bienenstock-Cooper-Munro (BCM) theory, an adaptive activation function is employed to regulate neuron thresholds based on historical feature map statistics. High-frequency noise from complex environments is suppressed, significantly enhancing feature extraction and target saliency in low signal-to-noise ratios. Finally, an end-to-end Dynamic Meta-Plasticity (DMP) strategy is implemented. By coupling parameter generation and threshold regulation within a bi-level optimization framework, biological homeostatic adaptation is simulated to adjust perception strategies. Context-dependent feature interaction patterns are established to secure robust discriminative boundaries under extreme few-shot conditions. Results Experimental results demonstrate that HNeuroNet significantly outperforms state-of-the-art methods on IP102, PlantDoc, and Mini-ImageNet. Notably, 5-way 1-shot accuracy on the PlantDoc dataset surpasses the second-best baseline by 4.33%. Furthermore, a 1-shot accuracy of 71.36% is achieved on the cross-domain Mini-ImageNet task. Discussion These results confirm the potential of bio-inspired computing in addressing data scarcity.
Why it matches plant phenotyping methods植物画像から病害・害虫状態を認識する適応型深層学習手法を開発し、PlantDoc等で性能検証しており、病害状態の推定と手法自体が研究の中心である。
abstractan adaptive recognition network for few-shot plant diseases and pests based on homeostatic neuromodulation and meta-plasticity (HNeuroNet) is proposed.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 7 Sept 2026
Recent advancements in 3D reconstruction technologies have significantly transformed plant phenotyping, enabling precise, scalable, and automated trait extraction. Traditional manual phenotyping methods are increasingly being replaced by image-based approaches, such as photogrammetry, LiDAR, RGB-D sensing, and deep learning (DL)-based techniques. These tools allow for non-destructive, high-throughput measurements of plant morphology, structure, and physiological traits. This review synthesizes the state of the art in 3D reconstruction methods, including conventional geometric algorithms and emerging DL methods, and evaluates their application across diverse plant species. In addition, we discuss the sensing modalities, evaluation metrics, and crop-specific deployments. Although promising, current technologies still face challenges in terms of computational efficiency, scalability to outdoor environments, and generalizability across crop types. This review concludes by identifying research gaps and future directions for making real-time, field-deployable 3D phenotyping systems.
Why it matches plant phenotyping methods植物フェノタイピングにおける3D再構成技術と形質抽出を主題とする方法論レビューであり、評価指標やセンサー、応用を体系的に扱っている。
abstractThis review synthesizes the state of the art in 3D reconstruction methods
Accurately simulating shoot-scale light scattering in physically based radiative transfer models remains a key challenge for conifer ecosystems. This study evaluates the high-resolution three-dimensional (3D) radiative transfer capability of the Discrete Anisotropic Radiative Transfer (DART) model using laboratory reflectance measurements and detailed photogrammetric reconstructions of Norway spruce ( Picea abies (L.) H. Karst) shoots. Samples representing multiple age classes and crown positions were collected from temperate (Czech Republic) and hemiboreal (Estonia) Norway spruce stands. Their geometry was reconstructed with sub-millimetre accuracy using structured blue-light 3D scanning, while the optical properties of needles and twigs were measured using an integrating sphere. We measured shoot reflectance under controlled laboratory illumination and compared it to DART simulations based on the identical 3D structures and optical inputs. DART simulations accurately reproduced the measured spectral signatures (R 2 = 0.95; median spectral angle mapper = 4.8°), demonstrating the model's capacity to simulate shoot-scale reflectance across diverse viewing geometries. These results suggest that detailed 3D shoot representations can improve radiative transfer modelling accuracy, and that DART efficiently simulates shoot reflectance across diverse viewing geometries as an alternative to labour-intensive goniometer measurements. This work provides the first empirical evaluation of DART at the shoot-scale and establishes a transferable framework for integrating detailed 3D photogrammetry into radiative transfer modelling. This approach enables more accurate upscaling from the conifer needle to the canopy-level and can enhance future model intercomparison exercises, such as the Radiation Transfer Model Intercomparison benchmark. • First empirical validation of DART simulation of conifer shoots. • High-resolution blue-light photogrammetry captures realistic shoot architecture. • DART-simulated reflectance closely matches laboratory measurements. • Framework enables realistic needle-to-canopy upscaling in radiative transfer models.
Why it matches plant phenotyping methods針葉樹シュートの3D構造を高精度に取得し、反射率モデルを実測値で検証する技術研究であり、植物形質（シュート構造・反射特性）の取得とモデル評価が中心である。
abstractTheir geometry was reconstructed with sub-millimetre accuracy using structured blue-light 3D scanning
This study compared 3D point cloud data derived from Structure-from-Motion (SfM) in 2021 and lidar in 2022 acquired using remotely piloted aircraft systems (RPAS). The overall objective was to develop and compare optical and active point cloud methods for deriving vegetation structures commonly measured in the field to quantify wildfire fuel distribution. The outcomes of the modelling framework were then applied to examine the impacts of mountain pine beetle (MPB) on canopy fuel load volumes in Jasper National Park prior to a high intensity wildfire in 2024. Tree species were classified using geographic object-based image analysis (GEOBIA) with an overall accuracy of ∼ 90%, with higher performance in relatively open canopies with minimal shadow. Photogrammetric and lidar point clouds resolved accurate individual tree height (R 2 = 0.96; 0.99, respectively) when compared to field measurements. Crown base height derived using a windowed point density approach improved agreement with field data (R 2 = 0.76; 0.91, respectively) and improved relative to previously reported methods. Across sites with varying MPB-induced tree mortality, plots dominated by dead conifers showed a redistribution of canopy fuels towards the ground compared to plots of mostly live conifers. This structural shift suggests increased ladder fuel development, reduced canopy continuity, and a heightened likelihood of surface to crown fire transition. The results demonstrate that RPAS point clouds can effectively characterize tree structure and improve crown base height estimation, supporting more accurate assessment of canopy bulk density. These measurements provide a viable alternative to labour-intensive field surveys and can then be used as calibration and validation data for broad-area forest assessment fuel modelling using airborne and satellite remotely sensed data.
Why it matches plant phenotyping methodsRPASのSfMおよびLiDAR点群を用いて樹高、樹冠基部高、林冠燃料構造を抽出し、現地測定と比較・検証している。個体・林分の植物構造測定法が研究の中心である。
abstractThe overall objective was to develop and compare optical and active point cloud methods for deriving vegetation structures commonly measured in the field to quantify wildfire fuel distribution.
Abstract 3D Gaussian Splatting (3DGS) [KKLD23] has transformed novel‐view synthesis from RGB images, yet remains restricted to the visible spectrum. Many applications, including agricultural monitoring, rely on multi‐spectral imaging, where spectral camera alignment and scalability pose major challenges. We present MS‐Splatting—a multi‐spectral 3DGS framework enabling unified multi‐view consistent reconstruction and rendering across both visible and invisible spectra. Our key component is a neural color representation that encodes per‐primitive features shared across spectral bands, decoded through a shallow multi‐layer perceptron into spectrum‐specific radiance. By leveraging inter‐band correlations, this formulation enhances detail while reducing memory consumption compared to independent band modeling via per‐channel modeling with spherical harmonics. Our method enables accurate parallax‐free novel‐view vegetation index rendering for plant monitoring and enhances RGB novel view synthesis quality by exploiting details revealed through multi‐spectral bands. Our evaluation demonstrates that MS‐Splatting exceeds the current leading methods in both categories. In addition, we introduce a multi‐spectral dataset from aerial captures covering outdoor environments, specifically designed for evaluating these applications. We will release our code and dataset to facilitate further research. The project page is located at: https://meyerls.github.io/ms_splatting
Why it matches plant phenotyping methodsマルチスペクトル3D再構成法を開発し、植物モニタリング用の植生指数レンダリングを実現することが中心で、評価用データセットも提供している。
abstractWe present MS‐Splatting—a multi‐spectral 3DGS framework enabling unified multi‐view consistent reconstruction and rendering across both visible and invisible spectra.
Plant 3D reconstruction using optical imaging often suffers from incomplete point clouds due to viewpoint occlusion and sensor limitations. This incompleteness hinders accurate structural representation and subsequent feature extraction for plant analysis. To address these challenges, we propose a Multi-Scale Dual-Discriminator Generative Adversarial Network (MSDDG) for plant point cloud completion. A multi-scale point cloud generator (MSPG) that integrates local and global features from raw incomplete point clouds is used for MSDDG to reconstruct complete shapes. The dual-discriminators-a multi-view projected silhouette discriminator and a spatial distance discriminator-are designed to ensure geometric realism and spatial plausibility from multiple perspectives. To train MSDDG, we created the Plant4L dataset containing four plant species (sunflower, pumpkin, luffa, and eggplant) with high-quality 3D models augmented via 3D thin plate spline transformations and virtual occlusion simulation to generate incomplete point clouds and multi-view silhouettes. Experimental results on Plant4L demonstrate that MSDDG achieves superior completion performance, with Chamfer Distance (CD), Hausdorff Distance (HD), and Uniformity Chamfer Distance (UCD) all below 0.41. Comparative evaluations confirm MSDDG's superiority over previous point cloud completion methods. The application of MSDDG for 3D reconstruction from single view further validate its effectiveness in restoring occluded plant structures.
Why it matches plant phenotyping methods植物の不完全点群を補完して3D構造を再構成する手法を開発し、植物データセット上で比較評価・検証しており、表現型取得ワークフローが中心です。
abstractwe propose a Multi-Scale Dual-Discriminator Generative Adversarial Network (MSDDG) for plant point cloud completion.
Reproduction assets foundThe paper's data availability statement explicitly states that the source code and datasets (including the Plant4L point cloud completion dataset) are publicly available at the authors' GitHub repository.Code · publicThe source code and datasets used in this study are publicly available at https://github.com/Amuro-Aznable/MSCGPCN.git .Open asset ↗https://github.com/Amuro-Aznable/MSCGPCN.git · MSCGPCNlines:415-421Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 5 Sept 2026
Conventional approaches to measuring stomatal conductance (gs) and transpiration often rely on instruments that interfere with plant physiology. Porometers, for example, restrict natural leaf movement, apply pressure, and introduce dry airflow that can alter stomatal behaviour, thereby reducing the relevance of such measurements. Prior studies report discrepancies among devices attributable to such interferences (Toro et al. 2019). To minimise artefacts, transpiration should be estimated remotely without physical contact, which theoretically can be achieved via a thermal leaf energy-balance approach that infers gs from leaf temperature, radiative load, and boundary-layer terms. In this study, we combine 3D plant models, light interception models, and thermal imaging to solve the energy-balance equation of individual leaves, estimating transpiration entirely remotely. Approaches to estimate stomatal conductance based on the energy-balance equation were developed recently to aid phenotyping of plantss. Most methods either imposed rapid changes in air humidity to perturb transpiration and, consequently, leaf temperature (Driever et al. 2023), or relied on ‘dry’ and ‘wet’ reference surfaces (as in Leinonen et al. 2006) to compute stress indices (Vialet-Chabrand and Lawson 2020). These methods require reference materials to assess surface temperatures under maximum and zero transpiration, showing the effect of longwave radiation. However, reference-material methods were constrained by heterogeneity in light interception caused by variation in leaf angle and orientation, because reference surfaces could not reorient like real leaves (Zhang et al. 2025). In this study, we addressed this challenge by using thermal imaging and 3D photogrammetry to capture leaf temperature and geometry noninvasively, allowing parameter estimation for each leaf individually. Here, ρ is the density of air (kg m−3), cp is the specific heat capacity of air (J kg−1 K−1) and rHR is the parallel resistance to heat and radiative transfer on the leaf surface (s m−1), s is the slope of the curve relating saturating water vapour pressure to temperature (Pa °C−1). TL and TA are leaf and air temperatures (°C), respectively, δe is air vapour pressure deficit (Pa), γ is the psychrometric constant (Pa K−1) and rva is the boundary layer resistance to water vapour (s m−1) (Supporting Information S2: Equation S1). The net radiative energy Rn in the energy-balance term was obtained from the same 3D light interception model, which integrates measured direct and lateral scattered irradiance (W m−2) (Supplement Material and Methods, File S2). Stomatal conductance gs (m s−1) is the inverse of stomatal resistance rs (s m−1). To experimentally obtain a wide range of gs values, we grew eggplant (Solanum melongena L.) plants in hydroponic units in growth chambers under four sets of environmental conditions (Table 1). Thirty-day-old plants (4–5-leaf stage) were placed on balances (Supplementary Materials and Methods, File S2). Units were sealed with plastic film to minimise evaporation. Mass loss attributable to transpiration was logged automatically every 30 s. To induce short-term changes in stomatal conductance, we imposed an acute osmotic stress by delivering a saline NaCl solution with high electrical conductivity (60 mS cm−1) to the root zone, producing a steep drop in root osmotic potential. This created rapid physiological and morphological responses that altered incident irradiance at the leaves, leaf temperature, and consequently energy balance, stomatal conductance and transpiration. We chose this stressor for operational simplicity. Any perturbation that modifies transpiration dynamics and thus gas exchange could have served our purpose. The total transpiration of a leaf, Et (kg s−1), is the product of the total conductance to water vapour from the mesophyll to the atmosphere, gv (m s−1), calculated from the estimated stomatal resistance rs (s m−1) and the boundary layer conductance gva (m s−1), the difference between water vapour concentration inside the leaf Cvs (dimensionless), and in surrounding air Cva (dimensionless), the leaf area A (m2), and the density of water ρw (kg/m3) (Jones 1992). Estimated stomatal conductance was obtained from leaf energy balance calculation (Equation 1). Boundary-layer conductance was computed from measured wind speed and leaf dimensions (leaf area, length, width) extracted from structure-from-motion 3D reconstructions (Supporting Information S1: Equation S6; Grace et al. 1980). Transpiration was then calculated for each leaf at each thermal 3D imaging time point, and whole-plant transpiration for comparison with gravimetric logs was the sum of all per-leaf estimates. As a non-invasive approach, we evaluated the plausibility or our model derived stomatal conductance (Equation 2) indirectly by comparing calculated and measured whole plant transpiration. We emphasise that this is not a direct validation of gs. Rather, the close agreement between modelled and measured transpiration across the wide range of environmental treatments, both stressed and non-stressed, provides confidence that the inferred gs is realistic. RGB and thermal images acquired before, during, and after stress application enabled dynamic tracking of leaf position and temperature (Supplementary Material and Methods, File S2). As expected, osmotic stress application had immediate effects on morphology and physiology. While control leaves maintained an angle of around 110° throughout, osmotic shock induced immediate turgor loss and drooping in all environments except one (Figure 1A,B). Leaf angles recovered to pre-stress positions within 1 h, indicating adaptation to the osmotic shock and restoration of turgor. Only environment 4 (high light, low air temperature and low humidity) maintained turgor during stress. Angle shifts were most pronounced in older leaves, which drooped and reduced light interception; younger leaves better preserved structure and turgor (Supporting Information S1: Figure S2). These angle changes also altered incident irradiance at the leaf surface (Supporting Information S1: Figure S3). These morphological responses coincided with increases in leaf temperature, consistent with altered water fluxes and stomatal regulation after stress. Across environments, plants showed a uniform rise in leaf temperature following osmotic stress, regardless of initial temperature (Supporting Information S1: Figure S4). This response held across leaf ages, encompassing older (Figure 1C) and younger (Figure 1D) leaves. Stomatal conductance estimated with our method followed the same pattern, dropping rapidly after osmotic shock in both older (Figure 1E) and younger (Figure 1F) leaves (Supporting Information S1: Figure S5). We estimated no stomatal conductance recovery to pre-stress conditions over the time course of stress exposure. Model-estimated and gravimetrically measured transpiration showed identical time courses across all four environmental conditions (Figure 1G–J). Transpiration rates did not recover to the same extent as leaf turgor, indicating long-term effects of the osmotic shock. Across environments and time points, correlation between model estimated and measured whole-plant transpiration was high (Figure 1K). In this study, stomatal conductance (gs) is a model-derived quantity inferred from the same physically constrained framework and model (leaf temperature, boundary-layer conductance and vapour pressure deficit). Since we did not measure gs directly, we cannot validate gs directly. Instead, we used a non-invasive check via transpiration. Model predictions closely tracked measured transpiration across the four controlled environments. This agreement increases confidence that the inferred gs is realistic, while we acknowledge that transpiration agreement alone is not a rigorous validation and cannot fully rule out compensating errors. Our study demonstrated the potential of our approach to estimate transpiration accurately by combining 3D imaging and thermography with physiological modelling without the use of reference materials that imitate real leaves. This remote approach enables simultaneous assessment of morphological and physiological responses to stress, yielding a more integrated view on plant transpiration and gas exchange. In contrast to chamber and porometer measurements or IR methods requiring wet and dry references or calibration plates, our workflow is reference-free. Absorbed shortwave radiation is derived from measured irradiance and a 3D reconstruction of leaf geometry, with no external reference materials. Moreover, remote measurements avoid continuous pressure from clamp-on porometers, permitting long-term observation and capture of rapid stress responses without sustained damage or microclimate artifacts. Further, the approach is not limited by any clamp on sensors and as such enables multi-leaf tracking. Applied to crop canopies, this approach could improve understanding of canopy processes that influence productivity and enable remote estimation of canopy transpiration. Future research could further improve by replacing our strong saline solution stress by gradual soil drying to depict a more realistic and natural stress while testing the approach under long-term conditions. Recent studies indicate that, with rising atmospheric CO2 concentrations, breeding for reduced stomatal conductance could increases WUE without affecting photosynthetic capacity (Srivastava et al. 2024). As such, remote systems for high-throughput plant phenotyping (HTP) are required to scan vast quantities of plants. We see a potential use of our system for such purposes to quickly estimated whole plant and individual leaf transpiration, as initial image capturing is very fast. A large bottleneck in our work was 3D model generation speed and manual extraction of leaf parameters from these 3D models. Both could be streamlined with more automated software, possibly including neural network solutions. The authors have nothing to report. The authors declare no conflict of interest. The data that support the findings of this study are available from the corresponding author upon reasonable request. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
Why it matches plant phenotyping methods3D画像、熱画像、光遮断モデル、エネルギーバランスモデルを統合し、葉ごとの蒸散と気孔コンダクタンスを非侵襲的に推定する手法を開発・評価しており、植物表現型取得が研究の中心である。
abstractIn this study, we combine 3D plant models, light interception models, and thermal imaging to solve the energy-balance equation of individual leaves, estimating transpiration entirely remotely.
• Multisensor platform integrates RGB, depth, IR, and RTK-GPS data streams • Automated plant segmentation and 3D reconstruction extract plant traits in field conditions • System validation shows high correlation with manual and lab measurements • Public RGB-D lettuce dataset released to support reproducible AI research Accurate monitoring of leafy vegetable crops is essential to evaluate plant health, growth, yield, and quality, yet conventional methods based on manual measurements are labor-intensive and error-prone. This study proposes a data-driven framework for automated in-field monitoring of a lettuce crop based on multidimensional data acquired by a ground platform under various field conditions. Specifically, an advanced perception system is developed, including imaging and localization sensors to capture high-resolution visual, structural, and georeferenced information on the crop. An image processing pipeline is then proposed using zero-shot learning for plant segmentation, followed by 3D phenotyping techniques based upon computational geometry to automatically estimate plant biophysical traits, thus minimizing human input. An experimental trial conducted in a test field in Bari, Italy, between April and May 2025 validated the approach against manual and laboratory estimations. The results demonstrate strong correspondence between automated and reference measurements with a Pearson correlation coefficient r > 0.9 for key traits, confirming the potential of the framework. The influence of different nitrogen levels on the growing cycle is also evaluated, showing that the proposed system may provide a useful tool for decision support in lettuce crop monitoring and management.
Why it matches plant phenotyping methodsRGB・深度・IR等を統合したセンシング、植物セグメンテーション、3D形状解析による形質推定を開発し、手測定・実験室測定で検証しているため、フェノタイピング手法が中心である。
abstractAn image processing pipeline is then proposed using zero-shot learning for plant segmentation, followed by 3D phenotyping techniques based upon computational geometry to automatically estimate plant biophysical traits
Seeds are complex living systems that display rich diversity in morphology, physiology, biochemistry, and genetics. Yet phenotyping during seed dormancy remains hampered by limited imaging modalities, insufficient data integration, and underpowered intelligent analytics—constraints that impede the efficiency and accuracy of precision breeding and germplasm evaluation. In the AI-for-Science era, seed phenomics research urgently needs to establish an end-to-end “measure–compute–understand–apply” pipeline, spanning cross-scale multimodal data acquisition to insight. This article systematically reviews the technical evolution of dormancy-state seed phenotyping and delineates five stages—manual observation phenotypes, biochemical phenotypes, image-based phenotypes, digital seeds, and intelligent seeds—summarizing the defining features and principal limitations of each. The deep integration of advanced imaging with artificial intelligence offers new opportunities to overcome existing bottlenecks. Seed Imaging Omics has emerged to meet this need: leveraging multiscale, multidimensional imaging for comprehensive observation and multimodal data capture; coupling these data with multimodal fusion, foundation-model analysis, and virtual seed reconstruction to enable precise feature extraction and pattern discovery from large image corpora. These capabilities clarify complex traits, reveal morphology–function relationships, and advance systems-level understanding of seed biology, ultimately supporting precise germplasm management and evaluation, data-driven elucidation of biological mechanisms, and accelerated innovation in crop improvement. Looking ahead, continued progress in sensing and imaging, foundation models, and large-scale analytics will drive seed phenotyping toward “intelligent” systems capable of autonomous sensing, real-time analysis, and decision-making across the seed life cycle—transforming seeds from passive carriers of genetic and phenotypic information into smart units that integrate phenotypic logging, state monitoring, performance assessment, and management feedback.
Why it matches plant phenotyping methods種子休眠状態の表現型計測技術を体系的にレビューし、画像取得、マルチモーダル統合、特徴抽出、AI解析を中心に扱うため、植物フェノタイピング手法レビューとして含める。
abstractThis article systematically reviews the technical evolution of dormancy-state seed phenotyping
Reliable Aboveground Biomass (AGB) estimates for woody crops are essential for carbon accounting and for Measurement, Reporting and Verification (MRV) frameworks. However, it remains unclear how LiDAR modality and sampling geometry influence plot-scale and tree-scale AGB predictions in intensively managed Mediterranean orchards. In this study, we benchmarked four LiDAR modalities, namely open national airborne laser scanning from the Spanish National Aerial Orthophotography Plan (PNOA/ALS), a dedicated Riegl airborne laser scanner (ALS), unmanned laser scanning (ULS) and mobile laser scanning (MLS), across three woody-crop sites in Córdoba (southern Spain): IFAPA, Doña María, and Villaseca. Plot-level LiDAR metrics (mean height, 95th height percentile, maximum height, and canopy cover proxies) were extracted from normalised point clouds and related to field AGB using Random Forest and XGBoost regression models, together with an ensemble predictor, under an 80/20 train–test split. In parallel, TreeQSM-based Quantitative Structure Models (QSMs) were evaluated as an independent tree-level three-dimensional reconstruction approach. XGBoost achieved the lowest errors at IFAPA (RMSE = 0.400 Mg ha⁻¹; R² = 0.994) and Villaseca (RMSE = 0.872 Mg ha⁻¹; R² = 0.995), whereas PNOA/ALS was competitive at Doña María (RMSE = 0.725 Mg ha⁻¹; R² = 0.994). TreeQSM closely matched field inventory at the low-biomass IFAPA site but tended to overestimate biomass at Doña María and Villaseca, and only 28% of scanned trees yielded usable reconstructions. The results support the use of cross-platform LiDAR for orchard AGB and carbon mapping and identify the conditions under which open national LiDAR can enable scalable MRV of Mediterranean woody crops.
Why it matches plant phenotyping methods複数LiDARプラットフォームと3次元再構成・機械学習を比較検証し、樹木・圃場レベルの地上部バイオマスという植物形質を推定する手法が研究の中心である。
abstractwe benchmarked four LiDAR modalities, namely open national airborne laser scanning from the Spanish National Aerial Orthophotography Plan (PNOA/ALS), a dedicated Riegl airborne laser scanner (ALS), unmanned laser scanning (ULS) and mobile laser scanning (MLS)
Three-dimensional seed phenotyping requires imaging systems capable of achieving micron-level resolution across a centimeter-level field of view (FOV), a goal constrained by the resolution–FOV trade-off in conventional light field architectures. This paper presents a hardware–software co-optimized framework that integrates a reconfigurable optical system with computational imaging pipelines to address this limitation. At the hardware level, we develop a tunable-focus lens module that enables flexible adjustment of the effective focal length, combined with a custom-designed microlens array (MLA). A mathematical model is established to analyze the interdependencies among FOV, lateral resolution, depth of field (DOF), and system configuration, guiding the design of individual optical components. On the computational side, we propose a hybrid aberration correction strategy: first, a co-calibration of lens and MLA aberrations based on line-feature detection; second, a conditional generative adversarial network (cGAN) with attention-guided residual learning to enhance sub-aperture images, achieving a PSNR of 34.63 dB and an SSIM of 0.9570 on seed datasets. Experimentally, the system achieves a resolution of 6.2 lp/mm at MTF50 over a 2–3 cm FOV, representing a 307% improvement over the initial configuration (1.52 lp/mm). The reconstruction pipeline combines epipolar plane image (EPI) analysis with multi-view consistency constraints to generate dense 3D point clouds at a density of approximately 1.5 × 104 points/cm2 while preserving spectral and textural features. Validation on bitter melon and rice seeds demonstrates accurate 3D reconstruction and accurate extraction of morphological parameters across a large area. By integrating optical and computational design, this work establishes a reconfigurable imaging framework that overcomes the resolution–FOV limitations of conventional light field systems. The proposed architecture is also applicable to robotic vision and biomedical imaging.
Why it matches plant phenotyping methods種子の3D形態形質を取得する光学・計算イメージングシステムの開発と検証が研究の中心であり、フェノタイピング手法として明確に適格。
abstractThis paper presents a hardware–software co-optimized framework that integrates a reconfigurable optical system with computational imaging pipelines to address this limitation.
Automated quantification of plant-level development from multi-plant greenhouse scenes requires separating individual plants from shared scene-level reconstructions and quantifying organ-level development, a challenge that single-plant acquisition workflows do not directly address. This study presents an end-to-end phenotyping pipeline built on 3D Gaussian Splatting (3DGS) and a post-reconstruction extraction framework, LCR-GS, designed to isolate plant instances from full greenhouse scenes without scene-specific model retraining. LCR-GS integrates zero-shot 2D cues with multi-view lifting, geometric clustering, and chromatic refinement to convert large scene-level reconstructions (~2M Gaussians) into compact per-plant subsets (~16K Gaussians). Experiments on greenhouse-grown muskmelon at the early vegetative stage demonstrate high plant-extraction precision (0.933) and strong organ-level instance segmentation (mean AP50 = 0.924). Plant height and leaf count are validated against manual measurements (height R² = 0.98, RMSE = 1.88 cm; leaf count R² = 0.86), whereas additional morphological traits, including leaf area, leaf area index, mean internode length, and stem node count, are reported as pipeline-derived descriptors for within-cohort comparison. By decoupling semantic inference from reconstruction, the pipeline reduces scene-scale data by over 99% and provides a practical route to derive compact per-plant 3D representations from multi-plant greenhouse imagery for downstream organ-level analysis.
Why it matches plant phenotyping methods3DGS画像から個体・器官を抽出し、植物形質を定量化するフェノタイピング手法の開発と検証が中心である。
abstractThis study presents an end-to-end phenotyping pipeline built on 3D Gaussian Splatting (3DGS) and a post-reconstruction extraction framework, LCR-GS, designed to isolate plant instances from full greenhouse scenes without scene-specific model retraining.
Reproduction assets foundThe paper's Data Availability Statement points to a public GitHub repository containing the muskmelon 3DGS phenotyping dataset (scenes, Gaussian-level plant/background annotations, and point-level organ labels) used in this study.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://github.com/bblabNTU/3dgs-muskmelon-phenotyping-dataset.Open asset ↗bblabNTU/3dgs-muskmelon-phenotyping-datasethtml-lines:485-547Plant phenotyping relevance match · UnverifiedEurope PMC · bioRxiv · checked 5 Sept 2026
Imaging carbon movements in the rhizosphere is fundamentally limited by high soil heterogeneity, low signal levels, and lack of methodology. We present Rhizo-PET, a dedicated positron emission tomography (PET) imaging and analysis framework designed to characterize the 4D spatiotemporal patterns of tracer distribution in intact plant–soil systems. The system achieved a global energy resolution of 11.93 ± 0.02% FWHM at 511 keV and maintained stable performance over 8 h of continuous acquisition, with a coincidence rate variation of only 0.7%. Spatial resolution reached 1.06 mm near the center of the field of view, establishing a high-fidelity region for root-scale analysis. Dynamic datasets were acquired from live Phaseolus vulgaris plants ( N = 3) over 180 min following 11 CO 2 pulse labeling and reconstructed into 3 min temporal frames. Quantitative analysis across 243 independent regions of interest (ROI) revealed that cumulative tracer accumulation decreases monotonically with radial distance from the root axis, while axial transport delays increase systematically in lower root segments ( p < 0.001). Hierarchical variability analysis showed that within-plant spatial organization ( CV TTP = 0.03) is significantly more stable than inter-plant variation ( CV TTP = 0.14), proving that the observed heterogeneity reflects biological spatial organization rather than experimental instability. These results establish Rhizo-PET as a robust, reproducible platform for the non-invasive, time-resolved analysis of carbon dynamics in the rhizosphere under realistic soil conditions.
Why it matches plant phenotyping methods植物根圏における炭素動態を非侵襲・時系列で測定する専用PETシステムを開発し、性能・再現性・空間解析能力を検証した研究であり、植物状態の取得方法が中心である。
abstractWe present Rhizo-PET, a dedicated positron emission tomography (PET) imaging and analysis framework designed to characterize the 4D spatiotemporal patterns of tracer distribution in intact plant–soil systems.
Summary (1) Rationale Quantifying and predicting plant morphology is central to understanding development and evolution, yet many plant forms lack homologous features required for traditional morphometrics. We apply the Euler Characteristic Transform (ECT), an injective descriptor from topological data analysis, to encode 2D plant shapes. The ECT converts contours into image-like representations that preserve shape information while enabling deep learning. (2) Methods We computed ECTs for large datasets of leaf and pavement cell shapes and used convolutional neural networks (CNNs) for classification. We also trained CNNs to approximate the inverse mapping, predicting leaf shape masks from radial ECTs. (3) Key results ECT-based models achieved high classification accuracy, surpassing previous approaches on millions of herbarium-derived leaves. Notably, grapevine leaf venation was predicted from blade geometry alone, demonstrating that vascular structure is encoded in the outline. (4) Main conclusion The ECT provides a compact, information-preserving representation of biological shape that integrates naturally with deep learning. It enables both accurate classification and predictive reconstruction, revealing latent morphological information and offering new opportunities to study plant form across scales.
Why it matches plant phenotyping methods植物形状を定量化・分類し、葉形状や葉脈を推定するECTベースの計算手法を中心に開発・評価しているため、植物フェノタイピング手法研究に該当する。
abstractQuantifying and predicting plant morphology is central to understanding development and evolution
Introduction High-throughput field phenotyping (HTFP) holds great potential for elucidating the relationship between genomes and phenotypes. However, obtaining high-quality three-dimensional point cloud data of field populations and achieving single-plant phenotypic analysis remain challenging. Methods This study develops an integrated framework for field crop reconstruction based on 3D Gaussian splatting, incorporating a geometry-aware dynamic constraint algorithm to achieve instance segmentation and extract key phenotypic traits of individual plants. Using 3D Gaussian splatting technology, field-scale cotton population modeling is accomplished, generating dense 3D point clouds for regions of interest. Furthermore, the concept of a crop localization domain is proposed, establishing a longitudinal mapping that associates plant positional coordinates with long-term phenotypic attributes. Finally, through a dynamic spatial constraint mechanism, the accuracy and computational efficiency of instance segmentation for crop population point clouds are significantly improved, enabling rapid extraction of individual plant traits such as cotyledon node height, plant height, and leaf area. Results The results demonstrate that PhenotypeAI successfully reconstructed nine cotton populations with PSNR exceeding 30.0 dB. It successfully extracted regions of interest from 403 cotton plants, achieving an average F-score of 91.32% for instance segmentation and an average accuracy of 91.35%. The extracted traits—cotyledon node height, plant height, and leaf area—exhibited strong correlations with manual measurements, with coefficients of determination ( R 2 ) of 0.90, 0.91, and 0.91, respectively. Discussion The proposed method provides a low-cost solution for high-throughput field phenotypic analysis of field cotton and improves the efficiency of cotton breeding.
Why it matches plant phenotyping methods3D再構成と動的空間制約による個体セグメンテーションおよび形質抽出が研究の中心で、綿花の草丈・葉面積などを検証しているため。
abstractThis study develops an integrated framework for field crop reconstruction based on 3D Gaussian splatting, incorporating a geometry-aware dynamic constraint algorithm to achieve instance segmentation and extract key phenotypic traits of individual plants.
Abstract Maize（Zea mays L.） is an important crop, and improving its productivity is required even under challenging conditions such as labor shortages and uncertain climate fluctuations. One approach to enhancing yield is utilizing crop data for cultivation management and yield prediction. However, efficient acquisition of such data remains constrained by various limitations. In this study, we developed a non-contact and labor-efficient method for crop data acquisition by generating 3D models of maize at the ripening stage using Neural Radiance Fields (NeRF). Segmentation was performed on the obtained point clouds to estimate plant height, leaf area, and leaf angle. The coefficients of determination (R2) were 0.903, 0.954, and -0.521, respectively, demonstrating high accuracy for plant height and leaf area even at the ripening stage, while reducing the time required for data acquisition by 93% compared to manual measurements. Nevertheless, some manual operations−such as removing kernels and separating overlapping leaves−were still necessary, and full automation was not achieved. The main sources of error were identified as reconstruction errors in the base during scale adjustment, excessive removal of leaf sheaths, and the curvature of individual plants. Furthermore, we examined how measurement accuracy was influenced by factors such as the time of day and cultivar. The proposed method is expected to contribute to the practical implementation of a labor -saving 3D measurement technique that supports yield prediction and growth diagnosis in maize.
Why it matches plant phenotyping methodsNeRFによる3D再構成と点群セグメンテーションを開発し、トウモロコシの草丈・葉面積・葉角度を推定して精度と誤差要因を検証しており、表現型取得法が研究の中心である。
abstractwe developed a non-contact and labor-efficient method for crop data acquisition by generating 3D models of maize at the ripening stage using Neural Radiance Fields (NeRF).
Abstract Accurate estimation of individual tree above‐ground biomass (AGB) and its component‐wise allocation is crucial for advancing ecological research and forest management. However, current biomass estimation methods, such as destructive sampling and allometric equation–based approaches, face limitations in both operational efficiency and cost‐effectiveness, and only destructive sampling can provide component‐wise biomass measurements, which is impractical for large‐scale studies or repeated measurements. In this study, we present a terrestrial laser scanning (TLS)‐based workflow integrating wood–leaf separation, voxel‐based foliage estimation and detailed 3D reconstruction of tree architecture to achieve accurate estimation of individual tree AGB and its component‐wise allocation. A total of 68 trees were scanned to obtain high‐resolution TLS data and subsequently destructively harvested to acquire field reference measurements for validation. The results demonstrate that the workflow achieved high accuracy in predicting AGB at the individual tree level (coefficient of determination/ R 2 = 0.88, root mean squared error/RMSE = 16.83 kg, mean absolute error/MAE = 12.18 kg), significantly outperforming estimates derived from locally calibrated allometric equations ( R 2 = 0.61, RMSE = 29.86 kg, MAE = 24.52 kg). Furthermore, this study provides evidence of the strong capability of TLS in estimating branch‐level biomass, with high accuracy achieved across branch orders ( R 2 ranging from 0.66 to 0.91, RMSE from 3.55 to 380 g and MAE from 2.97 to 290 g). By providing precise, non‐destructive estimates of biomass distribution across branches and leaves, this workflow demonstrates strong potential for improving the accuracy of tree biomass quantification, supporting investigations of resource allocation strategies, and enhancing forest carbon monitoring.
Why it matches plant phenotyping methodsTLSによる樹木の地上部・器官別バイオマスを推定するワークフローを開発し、破壊的実測で精度検証しており、植物形質取得法が研究の中心である。
abstractwe present a terrestrial laser scanning (TLS)‐based workflow integrating wood–leaf separation, voxel‐based foliage estimation and detailed 3D reconstruction of tree architecture to achieve accurate estimation of individual tree AGB and its component‐wise allocation.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 5 Sept 2026
Abstract Monitoring crop health is a pivotal pillar of sustainable agricultural production, requiring non-invasive sensing methods to prevent biotic and abiotic stress. Although multispectral and hyperspectral imaging has enabled advances in crop phenomics, its large-scale adoption remains limited by high costs, restricted spatial resolution, and strong sensitivity to lighting conditions. This work analyzes how varying illumination affects generative artificial intelligence models designed to reconstruct the near-infrared (NIR) band for agricultural and forestry applications. Two deep learning architectures were developed and evaluated: Convolutional Variational Autoencoders (CNN-VAEs) and Conditional Generative Adversarial Networks (cGANs). Each model was trained on aerial datasets collected over two forest sites under four shadow scenarios (without, minimal, short, and moderate) to assess their robustness across heterogeneous lighting conditions. The performance model was analyzed using SSIM, PSNR, and NRMSE to enable a standardized comparison between architectures. The experimental findings revealed that the multi-dataset VAE achieved the highest performance, reaching 0.96 SSIM, 35.06 dB PSNR, and an NRMSE of 0.018 under moderate shadows. These results demonstrate that VAE-based architectures provide more stable and reliable multispectral reconstructions than GANs under diverse illumination conditions.
Why it matches plant phenotyping methods植物の健康状態モニタリングに用いるNIR画像再構成法を開発し、異なる照明条件でCNN-VAEとcGANを比較・評価しており、画像取得・抽出手法が中心である。
abstractThis work analyzes how varying illumination affects generative artificial intelligence models designed to reconstruct the near-infrared (NIR) band for agricultural and forestry applications.
How evolutionary and developmental processes interact to determine axes of neural variation that produce behavioural diversity has been debated for many decades, with alternative hypotheses giving differential emphasis to functional coupling, which favours co-evolution, and developmental constraint, which enforces it. A critical omission is data on the genetic architecture of brain size and structure, which more closely illuminates the shared developmental dependencies between components of an integrated system. Here, we exploit ecological divergence between Astatotilapia calliptera and Aulonocara stuartgranti, two closely related cichlid species from Lake Malawi, to explore the genetic architecture of brain evolution. Using computer vision and machine learning techniques to extract volumetric data from micro-tomographic images, we first demonstrate significant divergence in brain composition between these species. Genomic and micro-tomographic imaging data from a population of hybrids generated between the two species were used to investigate genetic factors shaping this differentiation. We show that the majority of brain components are integrated phenotypically in hybrids, but genetic correlations between them are generally weaker. We further show that variation in multiple brain components is associated with variation in largely structure-specific quantitative trait loci, rather than determined by genetic factors with broad effects across the entire brain. These results suggest a genetic architecture that can facilitate modular changes in brain structure, and imply that individual components are independently evolvable.
Why it matches plant phenotyping methodsマイクロCT画像からコンピュータビジョンと機械学習で脳各部の体積を抽出する手法が、脳形態の遺伝的解析における中心的な表現型取得方法として明示されています。
abstractUsing computer vision and machine learning techniques to extract volumetric data from micro-tomographic images
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Field / plotPhotogrammetry / SfM / MVSRootWhole plant / canopy / plot / fieldCalibration / preprocessing2D/3D reconstructionRoot system architecture
Accurate 3D reconstruction is essential for high-throughput plant phenotyping, particularly for studying complex structures such as root systems. While photogrammetry and Structure from Motion (SfM) techniques have become widely used for 3D root imaging, the camera settings used are often underreported in studies, and the impact of camera calibration on model accuracyccu remains largely underexplored in plant science. In this study, we systematically evaluate the effects of focus, aperture, exposure time, and gain settings on the quality of 3D root models made with a multi-camera scanning system. We show through a series of experiments that calibration significantly improves model quality, with focus misalignment and shallow depth of field (DoF) being the most important factors affecting reconstruction accuracy. Our results further show that proper calibration has a greater effect on reducing noise than filtering it during post-processing, emphasizing the importance of optimizing image acquisition rather than relying solely on computational corrections. This work improves the repeatability and accuracy of 3D root imaging for phenotyping pipelines by giving useful calibration guidelines. This leads to better trait quantification for use in crop research and plant breeding in downstream analysis.
Why it matches plant phenotyping methods根の3D画像フェノタイピングにおけるカメラ校正・撮像条件を体系的に評価し、再構成精度と再現性を改善する技術指針を提示しており、フェノタイプ取得手法が研究の中心である。
abstractThis work improves the repeatability and accuracy of 3D root imaging for phenotyping pipelines by giving useful calibration guidelines.
Abstract Plant phenotyping attempts to objectively measure a plant’s reaction to its environment as encoded by its genotype. It has become an essential tool for deepening our understanding of plant responses to environmental stimuli. Understanding the plant’s reaction to, for example, a warmer climate is crucial to ensure food production for future generations. Breeders and researchers rely on automated high-throughput phenotyping for optimizing crops. Ideally, above- and below-ground traits are observed simultaneously. The newly established controlled environment facility at the Technology Center for Phenotyping of the Fraunhofer IIS in Merkendorf provides several climate chambers with individually controllable conditions for up to 400 individual plants to allow simulation of even extreme climatic conditions all year around. Comprehensive measurement of plant structures using X-ray as well as optical cameras provide highly detailed 2D and 3D information to researchers and breeders worldwide. In combination with automated data pipelines, distinct plant traits can be extracted from the sensor data. By bridging above- and below-ground phenotyping, this facility not only advances plant science but also contributes to the breeding of more resilient and productive crops. Collaborators are welcome to unlock the transformative potential of these unique phenotyping capabilities, exploring traits such as root length, leaf area, biomass, and more.
Why it matches plant phenotyping methodsX線・光学カメラと自動データパイプラインによる地上部・地下部形質の高スループット取得を中核とするフェノタイピング施設・プラットフォームの紹介であり、方法と測定基盤が中心。
abstractComprehensive measurement of plant structures using X-ray as well as optical cameras provide highly detailed 2D and 3D information to researchers and breeders worldwide.
ABSTRACT 3D reconstruction has matured into a robust technology. However, small, flexible objects such as conifer seedlings remain challenging due to their fine‐scale structures, and susceptibility to movement. This study investigates and evaluates methods for reconstructing spruce ( Picea abies ) and pine ( Pinus sylvestris ) seedlings, with the aim of establishing a workflow capable of capturing geometry and texture for applications in machine learning and virtual testing environments. Two acquisition approaches were tested: photogrammetry using a RGB camera and a 3D scanner, both mounted on a robotic arm. While the scanner produced incomplete results, the photogrammetry approach successfully generated point clouds (pcl) with color information. Three different photogrammetry software were tested before relying on Agisoft Metashape and Meshroom for image processing and dense pcl generation, followed by pcl filtering in CloudCompare and meshing in Blender. Six seedlings were reconstructed to textured meshes and quantitatively evaluated using the metrics precision, recall, F1‐score, mask intersection‐over‐union (IoU), and boundary IoU. Results showed an average mask IoU of 75.7% and F1‐score of 86.1%. Pine seedlings yielded higher recall and F1‐scores, whereas spruce reconstructions demonstrated higher precision. The proposed semi‐automated workflow demonstrates the feasibility of reconstructing small and slender structured flexible objects, specifically conifer seedlings.
Why it matches plant phenotyping methods針葉樹苗の形状・テクスチャを取得する3D画像再構成ワークフローを開発・比較・定量評価しており、植物フェノタイピング手法が中心である。
abstractThis study investigates and evaluates methods for reconstructing spruce ( Picea abies ) and pine ( Pinus sylvestris ) seedlings, with the aim of establishing a workflow capable of capturing geometry and texture for applications in machine learning and virtual testing environments.
Reproduction assets foundThe paper's Data Availability Statement states that the raw seedling image data and finalized textured meshes (the paper's phenotyping/3D reconstruction inputs and outputs) are freely available on Zenodo under DOI 10.5281/zenodo.19823955, which appears in the allowed URL list.Dataset · publicand without adjusting the scanning parameters, while also re-
Data Availability Statement
taining texture and color. In contrast to prior approaches that
require manual intervention or do not preserve visual informa- Raw image data and finalized textured meshes are freely available at
Zenodo.org with https://doi.org/10.5281/zenodo.19823955.
tion, the proposed workflow enables a semi-automated recon-
struction process suitable for dataset generation. As shown, the
methodology is effective for the digital reconstruction of small References
and slender structured flexible objects and holds potential for
Abbood, S. A., H. A. Ajjah, A. H. H. Alboabidallah, M. U. MohaOpen asset ↗Zenodopdf-layout-page:14 lines:50-74Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Apr 2026Computers and Electronics in Agriculture.
Given the substantial agronomic and economic significance of maize, the development of real-time and high-precision disease detection methodologies is essential for ensuring yield stability. While hyperspectral imaging excels at capturing fine-grained spectral signatures of infection, its higher detection precision comes with considerable high hardware and temporal costs compared to RGB imaging, posing significant challenges for scalable field applications. To bridge this gap, this article proposes a fusion perception unfolding network (FPUF-Net) for high-fidelity maize spectral reconstruction from RGB images. Distinct from conventional deep learning models, FPUF-Net unfolds the optimization problem via a half-quadratic splitting algorithm, solving the data subproblem and prior subproblem alternately during iterations. Specifically, a fusion feature learning network and a spectral-spatial joint attention network are designed within the data subproblem to explicitly exploit RGB spatial priors and mitigate spatial smoothing. Moreover, a spectral-spatial Transformer is utilized as the denoiser to capture long-range spectral dependencies in the prior subproblem. Experiments performed on a maize spectral recovery dataset comprehensively demonstrate that FPUF-Net can effectively reconstruct maize hyperspectral images with superior precision. The structural characteristics enable the network to perceive long-term spectral-spatial fusion features, significantly reducing reconstruction errors, particularly in the biologically critical red-edge region (620-700 nm). In downstream disease detection tasks, the overall accuracy of reconstructed HSIs improves over RGB by margins of 0.51% to 8.1% across different scenarios, while the average accuracy increases by 2.45% to 18.86%. These results indicate that the proposed model offers a viable, cost-effective solution for applying hyperspectral imaging in field settings, enabling its scalable use in agricultural robots.
Why it matches plant phenotyping methodsRGB画像からトウモロコシのハイパースペクトル情報を再構成する手法を開発し、データセットで性能評価している。植物のスペクトル状態および病害検出に直接関わる手法が研究の中心である。
abstractthis article proposes a fusion perception unfolding network (FPUF-Net) for high-fidelity maize spectral reconstruction from RGB images.
施設園芸における環境制御では，温度や湿度の空間変動など環境の不均一性を考慮せずに，平均化された指標に基づいた制御が行われており，作物の生育にばらつきが生じる問題があった．これらを解決するためには，主に日照や温度・湿度などの不均一の原因となっている作物群落のキャノピー構造を可視化することが重要である．本研究では，低コストの2D LiDAR（Light Detection and Ranging）計測により，作物のキャノピー構造の可視化を試み，薄い葉や細い茎によるレーザー反射の有効性と適切なスキャン条件を検証した．機器を設置した台車を移動プラットフォームとして，圃場の畝に沿って移動させることで，畝に沿った作物のキャノピー構造を把握する．2つのLiDARの走査面を変えて用いることとし，水平スキャンにより，台車進行方向の作物及び障害物の2Dマッピング，垂直スキャンにより作物の高さ方向のスキャンを時系列的に重ねることで，3Dマッピングを行う．結果として，水平スキャンのデータは，台車の走行制御のための状況把握としては十分な精度で利用可能である．垂直スキャンのデータは，作物の高さ方向の構造を把握できることが確認された．2つのLiDARを搭載したシステムを用いて，圃場で定期的に移動計測を行うことで，作物のキャノピー構造を把握することができ，環境の不均一の要因として利用可能となることが期待される．
Why it matches plant phenotyping methods低コスト2D LiDARによる作物キャノピー構造の可視化手法を開発し、反射の有効性と走査条件を検証しているため、植物表現型取得が研究の中心である。
abstract本研究では，低コストの2D LiDAR（Light Detection and Ranging）計測により，作物のキャノピー構造の可視化を試み，薄い葉や細い茎によるレーザー反射の有効性と適切なスキャン条件を検証した．
Accurate and spatially resolved yield estimation is a critical requirement for precision agriculture and orchard management. This paper presents a geometrically consistent, orchard-scale apple yield estimation framework that integrates GNSS–visual-inertial odometry (VIO) fusion, deep learning-based object detection, multi-frame tracking, three-dimensional triangulation, and incremental factor-graph optimization. Camera poses are obtained using ZED GNSS–VIO fusion and subsequently refined using an iSAM2-based nonlinear smoothing approach that incorporates strong relative-motion constraints and soft global ENU (East-North-Up) translation priors. Apples are detected using a YOLO-based model and associated across frames via CoTracker3, enabling robust multi-view landmark reconstruction. Reprojection factors and landmark priors are incorporated into a unified nonlinear factor graph to jointly optimize camera trajectories and 3D apple positions. The reconstructed apples are spatially aggregated into a grid-based mass map, where individual fruit volumes are estimated assuming spherical geometry and converted to mass using density models. The resulting ENU-referenced yield plot provides a structured representation of orchard production variability. Experimental results demonstrate significant reductions in reprojection error after optimization and improved global consistency of the trajectory, leading to stable and spatially coherent 3D reconstructions. The proposed pipeline bridges perception, geometry, and optimization, providing a scalable solution for orchard-scale yield mapping and decision support in precision agriculture.
Why it matches plant phenotyping methods果実の三次元再構成から体積・質量・収量を推定する画像・計算パイプラインが研究の中心であり、植物器官の形態および収量形質を技術的に抽出している。
abstractThis paper presents a geometrically consistent, orchard-scale apple yield estimation framework that integrates GNSS–visual-inertial odometry (VIO) fusion, deep learning-based object detection, multi-frame tracking, three-dimensional triangulation, and incremental factor-graph optimization.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 5 Sept 2026
3D plant phenotyping has garnered significant interest for its ability to quantify key structural traits such as plant volume and canopy architecture. However, standard monocular 3D reconstruction techniques suffer from inherent scale ambiguity, requiring an additional step to recover the true metric scale of the plants. Existing scale recovery methods, whether based on precisely fabricated 3D objects or planar patterns such as checkerboards, have been successfully applied in controlled environments but face practical constraints in certain real-world scenarios: some require costly fabrication or pre-reconstruction calibration, which can limit throughput in dynamic field environments. Here, we present MagicRing, a novel, affordable, and physically reliable post-reconstruction scale recovery approach that addresses these specific constraints and provides a complementary solution for high-throughput, mobile, and field-based phenotyping. MagicRing features a simple red ring printed on A4 paper with a known diameter. By leveraging color-based segmentation and geometric curve fitting, our approach automatically detects the ring within 3D point clouds, recovers the metric scale, and establishes a standardized world coordinate system without the need for pre-calibration. Its planar, isotropic design ensures robustness even under significant occlusion. We demonstrate the utility of MagicRing through MobilePheno3D, an integrated smartphone-based pipeline that performs fully automated 3D reconstruction, scale recovery, and phenotypic extraction from video sequences. This system, which was validated across multiple plant species, including vegetables, wheat, rice, and maize in both indoor and field settings, reliably reconstructs aboveground and root structures and supports continuous growth monitoring. MagicRing decouples data collection from data analysis, enabling a workflow transition from conventional step-by-step, scene-specific calibration toward more scalable, high-throughput 3D plant phenotyping.
Why it matches plant phenotyping methods植物の3D形態形質を抽出するためのスケール復元法とスマートフォン型フェノタイピング・パイプラインを開発し、複数植物種・環境で検証しており、手法が研究の中心である。
abstractHere, we present MagicRing, a novel, affordable, and physically reliable post-reconstruction scale recovery approach
Abstract Accurate estimation of aboveground biomass (AGB) is essential for forest monitoring and carbon stock assessment. Airborne laser scanning (ALS) is widely used for large-scale AGB estimation, yet acquiring reference biomass from field measurements for training biomass regression models remains time-consuming and labour-intensive. Here we explore the potential of synthetic ALS data to enhance forest biomass estimation. Two virtual forest plots were generated using a voxel-based forest reconstruction approach to simulate ALS data. We compared the model performances under varying amount and proportion of simulated and real samples in the training set. We find that models trained exclusively on simulated samples underperform models trained solely on real samples. When real samples are scare, incorporation of synthetic samples substantially improves the model performance, with coefficient of determination (R²) increased by 0.001–0.73 and the root mean square error (RMSE) decreased by 0.07–2.26 Mg ha–1. When sufficient real samples are available, adding a small number of simulated samples further improves model performance, with RMSE decreased by 0.12–1.46 Mg ha–1. The optimal performance (R² = 0.852, RMSE = 33.47 Mg ha–1) is obtained when real samples comprise about 83% of the training samples. These findings demonstrate that synthetic ALS data can effectively complement real datasets in AGB modelling, improving accuracy under diverse data availability conditions.
Why it matches plant phenotyping methods森林プロットの地上部バイオマスという植物形質を、合成ALSデータとボクセル再構成で推定する手法を開発・比較評価しており、形質取得・推定法が研究の中心である。
abstractTwo virtual forest plots were generated using a voxel-based forest reconstruction approach to simulate ALS data.
Low spatial resolution (LR) remote sensing data is widely adopted because of its lower cost, although its limited analytical precision constrains its full use in precision agriculture. By contrast, the acquisition of high spatial resolution (HR) data often requires substantial expense. To address this limitation, this study proposes an unsupervised degradation-aware multi-channel super-resolution network (UDAMSR) to enhance LR spectral images without requiring paired HR–LR training data. The main contributions are as follows: ① the original framework is extended with dedicated queue and reconstruction layers to process multispectral and hyperspectral image (HIS) cubes, and a contrast-learning-based degradation-aware module is integrated to address unknown real-world degradation; ② comprehensive evaluation is conducted using image quality metrics, spectral consistency analysis, and performance in crop remote sensing tasks, such as chlorophyll content estimation; ③ the generalization capability of the model is assessed using data from three imaging devices, two spatial scales (near-ground and unmanned aerial vehicle (UAV)), and two geographic regions. The results show that the proposed method achieves the best overall performance in the comprehensive evaluation, with a mean peak signal-to-noise ratio ( P S N R ¯ ) of 32.78, a mean root mean squared error ( R M S E ¯ ) of 6.93, a mean structural similarity index ( S S I M ¯ ) of 0.89, and a mean spectral angle mapper ( S A M ¯ ) of 0.131. The method effectively reduces the degradation in chlorophyll detection accuracy caused by spatial resolution reduction. The evaluation of generalization capability further shows that the proposed method demonstrates strong generalization across different spatial scales, geographic regions, devices, and data types. These results indicate that UDAMSR provides a robust, efficient, and cost-effective software solution that can compensate for hardware limitations and support high-quality crop phenotyping detection in diverse application scenarios.
Why it matches plant phenotyping methods作物スペクトル画像の空間分解能向上ネットワークを開発し、画質・スペクトル整合性・クロロフィル推定性能を多装置・多地域・多スケールで検証しており、植物表現型取得・推定手法が中心である。
abstractthis study proposes an unsupervised degradation-aware multi-channel super-resolution network (UDAMSR) to enhance LR spectral images
Abstract Root system architecture plays a critical role in water and nutrient acquisition, particularly in semi‐arid environments where drought stress limits crop productivity. Despite advances in three‐dimensional (3D) root phenotyping, no dedicated low‐cost imaging platform currently exists for sorghum ( Sorghum bicolor (L.) Moench) in the United States. The objective of this study was to design and construct an affordable laboratory‐based 3D imaging system for sorghum root phenotyping modeled after the digital imaging of root traits (3D) framework. The system consists of a rotating aluminum frame equipped with eight high‐resolution digital cameras controlled by Raspberry Pi microcomputers, uniform LED lighting, and background reference markers to ensure accurate image alignment. Approximately 2000–3000 overlapping images are captured in under 5 min and processed using structure‐from‐motion algorithms to generate colorized 3D point clouds. The total system cost was approximately $6000, substantially lower than commercial imaging technologies such as computed tomography or magnetic resonance imaging. Initial assembly demonstrated strong geometric alignment and minimal distortion, enabling measurement of key root traits including volume, nodal root angle, and whorl spacing. This platform provides a reproducible and scalable approach for sorghum root phenotyping and addresses a critical gap in crop research tools for semi‐arid production systems. The system also offers educational value by integrating engineering design, programming, and plant science, supporting interdisciplinary training and future genotype‐phenotype studies aimed at improving drought resilience.
Why it matches plant phenotyping methodsソルガム根の形態形質を取得する低コスト3D画像プラットフォームの設計・構築が研究の中心であり、根体積や根角度などの測定法を提供している。
abstractThe objective of this study was to design and construct an affordable laboratory‐based 3D imaging system for sorghum root phenotyping modeled after the digital imaging of root traits (3D) framework.
Severe foliage occlusion and dynamically changing lighting conditions in complex orchard environments pose significant challenges for visual perception systems in automated apple harvesting, including low detection accuracy, poor robustness, and insufficient real-time performance. To address these issues, this study proposes an improved lightweight detection network based on YOLOv11, named YOLO-WBL, along with a precise yield estimation algorithm based on 3D point clouds, termed CLV. The YOLO-WBL network is optimized in three aspects: (1) A C3K2_WT module integrating wavelet transform is introduced into the backbone network to enhance multi-scale feature extraction capability; (2) A weighted bidirectional feature pyramid network (BiFPN) is adopted in the neck network to improve the efficiency of multi-scale feature fusion; (3) A lightweight shared convolution separated batch normalization detection head (Detect-SCGN) is designed to significantly reduce the parameter count while maintaining accuracy. Based on this detection model, the CLV algorithm deeply integrates depth camera point cloud information through 3D coordinate mapping, irregular point cloud reconstruction, and convex hull volume calculation to achieve accurate estimation of individual fruit volume and total yield. Experimental results demonstrate that: (1) The YOLO-WBL model achieves a precision of 93.8%, recall of 79.3%, and mean average precision (mAP@0.5) of 87.2% on the apple test set; (2) The model size is only 3.72 MB, a reduction of 28.87% compared to the baseline model; (3) When deployed on an NVIDIA Jetson Xavier NX edge device, its inference speed reaches 8.7 FPS, meeting real-time requirements; (4) In scenarios with an occlusion rate below 40%, the mean absolute percentage error (MAPE) of yield estimation can be controlled within 8%. Experimental validation was conducted using apple images selected from the dataset under varying lighting intensities and fruit occlusion conditions. The results demonstrate that the CLV algorithm significantly outperforms traditional average-weight-based estimation methods. This study provides an efficient, accurate, and deployable visual solution for intelligent apple harvesting and yield estimation in complex orchard environments, offering practical reference value for advancing smart orchard production.
Why it matches plant phenotyping methodsリンゴ果実の検出と3D点群による個別果実体積・総収量推定を開発し、精度・速度・遮蔽条件下で検証しており、植物形質取得が中心的な方法論的貢献である。
abstractthis study proposes an improved lightweight detection network based on YOLOv11, named YOLO-WBL, along with a precise yield estimation algorithm based on 3D point clouds, termed CLV.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 5 Sept 2026
WheatNeRF / 3D Gaussian SplattingLiDAR / point cloudWhole plant / canopy / plot / fieldPose / keypoint estimation2D/3D reconstruction
Precise reconstruction of plant phenotypes is crucial for smart agriculture. Conventional methods struggle with low efficiency and strong dependency on high-quality data, especially for low-texture and structurally complex crops like wheat. We propose a novel 3D reconstruction framework—Plant3R—that fuses deep feature learning with 3D Gaussian Splatting (3DGS). It innovatively uses the Matching and Stereo 3D Reconstruction (MASt3R) model for sparse point cloud reconstruction and camera pose estimation via its 3D feature matching capabilities, which substantially improve image matching rates and the quality of sparse point clouds. Subsequently, 3DGS is employed for rendering and optimization, enabling end-to-end, high-fidelity, and high-robust 3D reconstruction of wheat plants. Validated on potted wheat at multiple growth stages using handheld images, our experimental results demonstrate that Plant3R performs well in feature extraction and matching, and the reconstructed point cloud provides a good geometric prior for the subsequent rendering stage. In most scenes, its key rendering metrics—Peak Signal-to-Noise Ratio (PSNR) > 34, Structural Similarity Index Measure (SSIM) of 0.94, and Learned Perceptual Image Patch Similarity (LPIPS) 0.94), confirming its utility for accurate and quantitative phenotype analysis. Overall, Plant3R not only improves the rendering quality and geometric precision of 3D modeling, but also provides a reliable tool for accurate phenotypic parameter extraction and high-throughput crop phenotyping in precision agriculture.
Why it matches plant phenotyping methods小麦植物の3D再構成と表現型パラメータ抽出を目的とする画像解析手法を開発・検証しており、植物フェノタイピング手法が研究の中心である。
abstractWe propose a novel 3D reconstruction framework—Plant3R—that fuses deep feature learning with 3D Gaussian Splatting (3DGS).
MilletPeaField / plotGreenhouseNeRF / 3D Gaussian SplattingWhole plant / canopy / plot / fieldPose / keypoint estimation2D/3D reconstructionVisualization / data managementGrowth / development / phenology
Plant phenotyping in precision agriculture increasingly requires high-fidelity three-dimensional reconstruction and accessible visualization methods. This study presents an integrated pipeline combining Neural Radiance Fields (NeRF), 3D Gaussian Splatting (G-Splat), and Virtual Reality (VR) visualization for comprehensive plant analysis across developmental stages. We collected multi-view imagery of finger millet, proso millet, mungbean, and field pea under controlled greenhouse conditions, aligning data acquisition with standardized BBCH phenological scales. Camera pose estimation was performed using GLOMAP, followed by reconstruction via both Nerfacto and G-Splat implementations. Quantitative evaluation using PSNR, SSIM, and LPIPS metrics revealed complementary strengths of the two approaches: G-Splat achieved superior structural fidelity, while NeRF provided enhanced perceptual realism. Both reconstruction methods were successfully integrated into an immersive VR greenhouse environment deployed on Meta Quest headsets, maintaining consistently high framerates. This framework establishes a practical foundation for incorporating neural reconstruction and immersive technologies into agricultural phenotyping workflows, supporting both research applications and educational engagement.
Why it matches plant phenotyping methods植物の多視点画像からNeRFと3D Gaussian Splattingで3D形状を再構成し、画質指標で比較評価する統合フェノタイピング基盤の開発・検証が中心である。
abstractThis study presents an integrated pipeline combining Neural Radiance Fields (NeRF), 3D Gaussian Splatting (G-Splat), and Virtual Reality (VR) visualization for comprehensive plant analysis across developmental stages.
High-density planting is an effective strategy to increase maize yield but imposes greater demands on plant architectural adaptability. To elucidate the structural response mechanisms of maize under varying planting densities, we developed a high-throughput 3D phenotyping system tailored to complex field conditions. High-precision point clouds of field-sampled plants were obtained via multi-view 3D reconstruction. Using a deep learning network, stem and leaf organs were semantically segmented (95.6% accuracy), while leaves were individually separated via clustering (94.8% accuracy). From these data, 31 plant architectural traits and 14 ear-leaf traits were extracted, establishing a hierarchical trait characterization system. Results showed that increased planting density significantly influenced plant architecture reshaping and structural coordination, leading to more compact plant forms and ear height position centralization. Ear leaves exhibited heightened sensitivity to density variation, particularly in leaf area, vertical distribution, and leaf inclination angle, suggesting an early-response role. Principal component analysis and clustering further revealed patterns of structural differentiation and key traits driving these changes under density treatments. The integrated workflow-comprising data acquisition, modeling, segmentation, clustering, trait extraction, and analysis-offers a robust approach for structural phenotyping and intelligent breeding selection in maize and other tall crops. This pipeline provides valuable technical support and data resources for optimizing dense planting strategies and advancing digital agriculture.
Why it matches plant phenotyping methods高スループット3D表現型システムを開発し、点群再構成・器官分割・クラスタリングから多数の植物構造形質を抽出することが中心であるため。
abstractwe developed a high-throughput 3D phenotyping system tailored to complex field conditions
Reproduction assets foundThe paper's authors provide a public GitHub repository for the study's source code (segmentation/trait-extraction pipeline). The phenotype point-cloud dataset itself is only available on request from the corresponding author, so it is not a public asset.Code · publicThe code of this study will be made publicly available upon publication. The source code is available at https://github.com/CSC-csc426/3D-Point-Cloud-Driven-Organ-Semantic-Segmentation-to-Assess-Maize-Structural-Responses .Open asset ↗CSC-csc426/3D-Point-Cloud-Driven-Organ-Semantic-Segmentation-to-Assess-Maize-Structural-Responseslines:330-415Code / dataset availability confirmedOpenAlex · Crossref · checked 13 Sept 2026
Abstract Background: Next-generation models of fire behavior and smoke production rely on gridded, 3D inputs of wildland fuel complexes. We used a hierarchically scaled sampling design to characterize canopy and surface fuels that are common to prescribed burning programs in the southeastern and western US. Sampling included airborne laser scanning, terrestrial laser scanning, close-range photogrammetry, and destructive field sampling. The objective of this study was to use a combination of airborne laser scanning (ALS), terrestrial laser scanning (TLS), structure-from-motion photogrammetry (SfM), and field observations to create co-located 3D datasets of live and dead understory fuels for use in wildland fuel mapping and prescribed burn decision support Results: Using our integrated, co-located methods, we produced hierarchically-scaled datasets detailing the structure and composition of canopy and surface fuels across 9 southeastern pine sites, 5 western pine sites, and 4 western grassland sites. These are now publicly available at within the Wildland Fire Science Initiative data repository (https://doi.org/10.60594/W4859C). In this paper, we detail methods and the repository structure. Conclusions: The study was designed to evaluate and advance methods for 3D fuel characterization and to provide consistently scaled and labelled datasets for model training and evaluation. More specifically, machine learning models can be used to parse 3D point clouds collected from ALS, TLS, and structure-from-motion photogrammetry into fuel objects and metrics. Calibration with field plots will allow our hierarchically-scaled datasets to be used as the foundation for synthetic fuelbed mapping, starting with fine-scale objects such as individual shrubs or downed wood and scaling to vegetation patches and operational burn units.
Why it matches plant phenotyping methodsALS、TLS、SfMと現地観測を統合して植物群落の3D構造・燃料特性を取得し、手法の評価・改良と公開データセット構築を主目的としているため、植物形質計測法が中心である。
abstractThe objective of this study was to use a combination of airborne laser scanning (ALS), terrestrial laser scanning (TLS), structure-from-motion photogrammetry (SfM), and field observations to create co-located 3D datasets of live and dead understory fuels for use in wildland fuel mapping and prescribed burn decision support
Reproduction assets foundThe paper's hierarchically scaled ALS/TLS/SfM point clouds, field fuel measurements, and analysis scripts are explicitly stated to be open source and archived in the Wildland Fire Science Initiative data repository (DOI 10.60594/W4859C), a paper-specific public asset directly reproducing this study's phenotyping/fuel-3Dataset · publicThe datasets and analysis scripts for this study are open source and are being archived with the Wildland Fire Science Initiative data repository (doi.org/10.60594/W4859C), including project metadata, methods documentation and data libraries (Prichard and Rowell 2025).Open asset ↗Wildland Fire Science Initiative data repository · 10.60594/W4859Clines:384-403Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Transmission electron microscopy (TEM) and energy-dispersive X-ray spectroscopy (EDS), with complementary light and scanning electron microscopies observations, are employed to reveal novel information regarding the foliar cuticle fine-structure of Araucaria grandifolia (Araucariaceae). Well-preserved foliar compressions of this taxon were collected from the Punta del Barco Formation (Baqueró Group, Aptian, Patagonia, Argentina). TEM sections revealed six types of cell cuticles: two representing the ordinary epidermal cells (OEC) of the upper and lower cuticle, and four related to the stomatal apparatus and associated cells: subsidiary and guard cell cuticles, and inner and outer associated OEC cuticles. Cuticles comprise either a granular A2 layer (cuticle proper) and a spongy-fibrilous B1 layer (cuticular layer), or solely a B1 spongy layer, which is similar to that of Nothopehuen brevis and Brachyphyllum garciarum , two Cretaceous Araucariaceae from Patagonia. The statistical evaluation of quantitative measurements revealed the relationships and hierarchies between cell cuticle types and ultrastructural layers, revealing for the first time the precise identity of Araucariaceae cuticles. TEM-EDS revealed a significant presence of phosphorus (P) and chlorine (Cl), highlighting the potential taxonomic and paleoenvironmental relevance of the P/Cl ratio. Additionally, the six cell cuticle types found in A. grandifolia are shown in a dichotomous key, and a cuticle three-dimensional reconstruction is provided. Finally, the paleoenvironment conditions under which the A. grandifolia plant lived during the Aptian in Patagonia are also inferred.
Why it matches plant phenotyping methods葉のクチクラ微細構造・元素組成という植物器官形質を、TEM、EDS、各種顕微鏡、定量解析、3D再構成で体系的に取得・解析しており、観察が分類・古生態の補助的な routine 測定に留まらず、方法に基づく形質記載の中心となっている。
abstractTransmission electron microscopy (TEM) and energy-dispersive X-ray spectroscopy (EDS), with complementary light and scanning electron microscopies observations, are employed to reveal novel information regarding the foliar cuticle fine-structure of Araucaria grandifolia (Araucariaceae).
Efficient large-scale 3D reconstruction of orchard environments is essential for robotic inspection and precision agriculture, yet existing methods struggle with unstructured scenes, variable illumination, and computational bottlenecks. We propose InspectGaussian, a coarse-to-fine Gaussian reconstruction framework tailored for orchard inspection robots. The pipeline integrates an RGB-D-based data acquisition strategy using ORB-SLAM3, which is enhanced by a dense mapping module for robust large-scale pose estimation and point cloud generation. A divide-and-conquer strategy is then employed: individual plant views are extracted via a YOLO-World-based detection and 3D matching algorithm, followed by plant-specific reconstruction using an improved 3D Gaussian Splatting (3DGS) method incorporating depth regularization and region-aware refinement. Experimental results in citrus orchards demonstrate that InspectGaussian achieves 96% average precision and 93% recall in plant view extraction, while surpassing state-of-the-art methods in reconstruction fidelity (31.226 PSNR, 0.915 SSIM, 0.067 LPIPS) and point cloud accuracy (7 mm error). These results confirm its effectiveness in capturing fine structural and textural details while maintaining scalability and efficiency. This framework provides a practical solution for high-throughput, in-field plant phenotyping and lays the foundation for intelligent orchard monitoring and management.
Why it matches plant phenotyping methods植物個体の3D再構成とRGB-D・検出・Gaussian Splattingを統合した手法開発であり、植物の構造的形質取得を目的とするため、フェノタイピング手法が中心である。
abstractWe propose InspectGaussian, a coarse-to-fine Gaussian reconstruction framework tailored for orchard inspection robots.
Reproduction assets foundThe paper's authors explicitly state their analysis code is publicly available on GitHub. Phenotype datasets (RGB-D orchard image sequences, LiDAR point clouds, manual trait measurements) are only available upon request, so they do not qualify as public assets.Code · publicOur code are available at https://github.com/zlhzau/InspectGaussian.git .Open asset ↗zlhzau/InspectGaussianlines:489-515Plant phenotyping relevance match · UnverifiedarXiv · OpenAlex · checked 13 Sept 2026
Dense ground-truth disparity maps are practically unobtainable in forestry environments, where thin overlapping branches and complex canopy geometry defeat conventional depth sensors -- a critical bottleneck for training supervised stereo matching networks for autonomous UAV-based pruning. We present UE5-Forest, a photorealistic synthetic stereo dataset built entirely in Unreal Engine 5 (UE5). One hundred and fifteen photogrammetry-scanned trees from the Quixel Megascans library are placed in virtual scenes and captured by a simulated stereo rig whose intrinsics -- 63 mm baseline, 2.8 mm focal length, 3.84 mm sensor width -- replicate the ZED Mini camera mounted on our drone. Orbiting each tree at up to 2 m across three elevation bands (horizontal, +45 degrees, -45 degrees) yields 5,520 rectified 1920 x 1080 stereo pairs with pixel-perfect disparity labels. We provide a statistical characterisation of the dataset -- covering disparity distributions, scene diversity, and visual fidelity -- and a qualitative comparison with real-world Canterbury Tree Branches imagery that confirms the photorealistic quality and geometric plausibility of the rendered data. The dataset will be publicly released to provide the community with a ready-to-use benchmark and training resource for stereo-based forestry depth estimation.
Why it matches plant phenotyping methods樹木の枝・樹冠形状を対象とするステレオ深度推定データセットを開発し、画素単位の視差ラベルと実画像との比較検証を提供しており、植物構造の取得方法が中心である。
abstractWe present UE5-Forest, a photorealistic synthetic stereo dataset built entirely in Unreal Engine 5 (UE5).
Branching forms are ubiquitous in nature and have evolved repeatedly across scales and species. An important goal of developmental biology remains to identify similarities and differences in the regulatory mechanisms underlying branching. Here, we investigate the branching filaments that form upon spore germination in mosses, using Physcomitrium patens as a model species. To identify the macroscopic rules governing filament patterning, we developed a pipeline to acquire high-resolution 3D images of whole sporelings, reconstruct filament architecture at single-cell resolution, and formalize cell organization using mathematical tree representations. Our quantitative analysis reveals that branch patterning in moss filaments can be captured by a simple probabilistic model in which subapical cells have a near-equal probability of producing – or not producing – a side-branch between successive apical cell divisions. This framework provides a quantitative basis for comparing the developmental rules driving branching morphogenesis within and beyond the plant kingdom.
Why it matches plant phenotyping methodsコケ植物のフィラメント形態を対象に、高解像度3D画像取得、単一細胞レベルの構造再構築、数学的表現による定量化パイプラインを開発しており、植物表現型の取得・抽出手法が研究の中心です。
abstractwe developed a pipeline to acquire high-resolution 3D images of whole sporelings, reconstruct filament architecture at single-cell resolution, and formalize cell organization using mathematical tree representations
Aerial remote sensing efficiently surveys large areas, but accurate direct object-level measurement remains difficult in complex natural scenes. Advancements in 3D computer vision, particularly radiance field representations such as NeRF and 3D Gaussian splatting, can improve reconstruction fidelity from posed imagery. Nevertheless, direct aerial measurement of important attributes like tree diameter at breast height (DBH) remains challenging. Trunks in aerial forest scans are distant and sparsely observed in image views; at typical operating altitudes, stems may span only a few pixels. With these constraints, conventional reconstruction methods have inaccurate breast-height trunk geometry. TreeDGS is an aerial image reconstruction method that uses 3D Gaussian splatting as a continuous scene representation for trunk measurement. After SfM–MVS initialization and Gaussian optimization, we extract a dense point set from the Gaussian field using RaDe-GS’s depth-aware cumulative-opacity integration and associate each sample with a multi-view opacity reliability score. Then, we isolate trunk points and estimate DBH using opacity-weighted solid-circle fitting. Evaluated on 10 plots with field-measured DBH, TreeDGS reaches 4.79 cm RMSE (about 2.6 pixels at this GSD) and outperforms a LiDAR baseline (7.66 cm RMSE). This shows that TreeDGS can enable accurate, low-cost aerial DBH measurement.
Why it matches plant phenotyping methods樹木の胸高直径（DBH）という植物形態形質を、航空画像から3D再構成と信頼度重み付き推定で測定する手法を開発・検証しており、フェノタイピング手法が中心である。
abstractTreeDGS is an aerial image reconstruction method that uses 3D Gaussian splatting as a continuous scene representation for trunk measurement.
ArabidopsisChlorophyll fluorescenceMicroscopyLiDAR / point cloudCell / cellular structureRootMorphology / geometry measurement2D/3D reconstructionGrowth / time-series analysisGrowth / development / phenology
Embryogenesis in the model plant Arabidopsis thaliana provides a framework for understanding how cell polarity and patterning coordinate with hormonal signalling to establish the plant body plan. Following fertilisation, the zygote divides asymmetrically to generate apical and basal lineages, establishing the apical-basal axis that defines future shoot and root poles. Genetic and molecular analyses of classical mutants including gnom, monopteros (mp), bodenlos (bdl) and topless revealed that localised auxin biosynthesis, directional transport and downstream transcriptional responses are central to apical-basal axis establishment and organ initiation. The main components of this regulation are polarly localised PIN auxin transporters and downstream modules involving MONOPTEROS and WUSCHEL-RELATED HOMEOBOX transcription factors. Advances in microscopy have transformed the study of Arabidopsis embryogenesis: fluorescence-compatible clearing reagents and three-dimensional reconstructions now permit quantitative analyses of cell geometry, division orientation, and cytoskeletal dynamics. Live ovule imaging setups with confocal laser scanning and multiphoton microscopes enable real-time observation of embryo development, while laser-assisted cell ablation can be used to probe cell-to-cell communication and fate plasticity. Together, these methodological breakthroughs position Arabidopsis embryos as a prime model for dissecting the chemical and biophysical cues that shape plant development.
Why it matches plant phenotyping methods胚発生を解析するための蛍光イメージング、三次元再構成、ライブ撮像などの方法論と定量的な細胞形態・分裂方向測定が中心であり、植物フェノタイピング手法に該当する。
abstractAdvances in microscopy have transformed the study of Arabidopsis embryogenesis: fluorescence-compatible clearing reagents and three-dimensional reconstructions now permit quantitative analyses of cell geometry, division orientation, and cytoskeletal dynamics.
Abstract To address the issues of detail loss and matching difficulties in fruit tree 3D reconstruction caused by complex branch–leaf morphology, fruit occlusion, and illumination variations, this paper proposes an end-to-end cross-scale collaborative attention multi-view stereo network, termed MSA-MVSNet, for high-quality 3D reconstruction of orchard trees, while integrating semantic segmentation for fruit counting. A multi-scale feature enhancement module is designed to adaptively fuse deep semantic features and shallow fine-grained details through a spatial–channel collaborative attention mechanism, thereby enhancing the network’s capability to represent multi-scale structures such as trunks, branches, and leaves. Multi-branch dilated convolutions are introduced to enlarge the receptive field, and deformable convolutions are incorporated to adaptively capture the irregular geometric shapes of fruits, improving modeling robustness. In addition, a feature matching transformer is introduced to strengthen long-range global contextual correlations within and across images via intra-attention and inter-attention mechanisms, thereby improving matching stability in low-texture and repetitive-texture regions.To validate the effectiveness of the proposed method, experiments are conducted on self-collected real orchard dataset and public benchmark datasets. The results demonstrate that MSA-MVSNet outperforms baseline models by 8.2% in terms of 3D reconstruction quality. Finally, by combining depth filtering with the semantic segmentation results of YOLOv11-Seg, a semantic-guided fruit reconstruction and counting framework is constructed. This framework achieves an overall counting F1-score of 92.8% on the self-collected dataset with varying scene sparsity and 93.5% on the public Fuji-sfm dataset, demonstrating its effectiveness and generalization capability.
Why it matches plant phenotyping methods果樹の3D再構成と果実カウントという植物形質取得を目的に、マルチビュー再構成ネットワークとセグメンテーション統合手法を開発・検証しており、フェノタイピング手法が中心である。
abstractthis paper proposes an end-to-end cross-scale collaborative attention multi-view stereo network, termed MSA-MVSNet, for high-quality 3D reconstruction of orchard trees, while integrating semantic segmentation for fruit counting.
Reliable aboveground biomass (AGB) estimates for woody crops are required for carbon accounting and MRV; however, it remains unclear how LiDAR modality and sampling geometry influence plot-scale and tree-scale AGB predictions in intensively managed orchards. We benchmarked four LiDAR modalities across three Mediterranean woody-crop sites in Córdoba (Spain), IFAPA, Doña María, and Villaseca using open national airborne laser scanning (PNOA/ALS), Riegl ALS, unmanned laser scanning (ULS), and mobile laser scanning (MLS). The field inventory used 58 fixed-area plots (20×50 m; 0.1 ha) collected in December 2024-January 2025 (1,867 trees) and species-specific allometries based on D2r to derive tree and plot AGB; carbon was computed using wood carbon fractions (0.445 olive; 0.457 almond) and CO2e via IPCC conversion. Plot-level LiDAR metrics (e.g., mean height, p95, maximum height, and cover proxies) were extracted from normalized point clouds and modeled with Random Forest, XGBoost, and an ensemble under an 80/20 train-test split. Mean field AGB differed among sites (33.89, 30.94 and 12.76 Mg ha−1 for Villaseca, Doña María, and IFAPA). In the provided summaries, XGBoost achieved the lowest errors at IFAPA (RMSE = 0.400 Mg ha−1; R2 = 0.994) and Villaseca (RMSE = 0.872 Mg ha−1; R2 = 0.995), whereas PNOA was competitive at Doña María (RMSE = 0.725 Mg ha−1; R2 = 0.994). The results support cross-platform LiDAR for orchard AGB mapping and identify conditions under which open national LiDAR can enable scalable MRV. In addition, we evaluated TreeQSM-based quantitative structure models (QSMs) as an independent tree-level 3D reconstruction approach and examined their site-dependent agreement with field inventory estimates.
Why it matches plant phenotyping methods複数のLiDARモダリティと解析手法を比較・ベンチマークし、樹木およびプロットの地上部バイオマスを推定する技術評価が研究の中心であるため。
abstractWe benchmarked four LiDAR modalities across three Mediterranean woody-crop sites
TeaField / plotNeRF / 3D Gaussian SplattingLiDAR / point cloudLeafWhole plant / canopy / plot / fieldCountingObject detection2D/3D reconstructionSegmentation
Accurate perception of tea buds is a fundamental prerequisite for intelligent and precise tea harvesting planning. However, in real tea plantation environments, reliable harvesting-oriented perception at the planning level remains highly challenging due to the small size of tea buds, severe occlusion, complex background clutter, and the lack of accurate three-dimensional spatial information. To address these challenges, we propose TeaNeRF, an integrated three-dimensional visual perception pipeline designed for harvesting-oriented tea bud analysis. Instead of treating detection, segmentation, and spatial analysis as independent tasks, TeaNeRF integrates sequential two-dimensional recognition, monocular depth estimation, and neural radiance field reconstruction into a coherent perception pipeline, allowing accurate spatial understanding of tea buds in complex natural scenes. It should be noted that the proposed integration is conducted at the perception-output level, where multiple modular components are connected through fixed interfaces, rather than through joint optimization or an end-to-end trainable formulation. The proposed framework combines an enhanced YOLO-based detector, prompt-guided segmentation, and monocular depth priors to guide NeRF-based three-dimensional reconstruction. By incorporating depth supervision and semantic-aware neural fields, TeaNeRF generates dense and geometrically consistent point clouds with reliable semantic separation. Quantitative evaluations show consistent improvements in reconstruction fidelity, as reflected by increased PSNR and reduced LPIPS across multiple tea tree scenes. Based on the reconstructed semantic point cloud, a three-dimensional clustering and geometric fitting strategy is further developed to enable tea bud counting and harvesting-oriented candidate point estimation at the perception level. Experiments conducted on a real-world dataset of 4,700 tea plantation images demonstrate that TeaNeRF improves detection accuracy (mAP@50 = 91.7%), segmentation quality (IoU = 0.640), and overall three-dimensional perception performance. Case-level counting results on representative tea trees indicate that the proposed 3D semantic point cloud-based approach can provide feasible tea bud counting behavior and consistent spatial guidance cues for downstream harvesting planning. By providing structured three-dimensional spatial information, including tea bud locations, counts, and harvesting-oriented candidate points, TeaNeRF offers practical perception-level outputs for downstream planning in automated tea harvesting systems.
Why it matches plant phenotyping methods茶芽の検出・セグメンテーション・3D再構成を統合し、茶芽の計数と3D位置推定を行う知覚パイプラインが研究の中心であり、単なる収穫対象の局在化を超えた器官形質の抽出を含む。
abstractwe propose TeaNeRF, an integrated three-dimensional visual perception pipeline designed for harvesting-oriented tea bud analysis.
NeRF / 3D Gaussian SplattingLiDAR / point cloudRGB / grayscaleWhole plant / canopy / plot / fieldOrgan identification2D/3D reconstructionSegmentation
Organ segmentation of plant point clouds is a prerequisite for the high-resolution and accurate extraction of organ-level phenotypic traits. Although the fast development of deep learning has boosted much research on segmentation of plant point clouds, the existing techniques for organ segmentation still face limitations in resolution, segmentation accuracy, and generalizability across various plant species. In this study, we proposed a novel approach called plant segmentation neural radiance fields (PlantSegNeRF), aiming to directly generate high-precision instance point clouds from multi-view RGB image sequences for a wide range of plant species. PlantSegNeRF performed two-dimensional (2D) instance segmentation on the multi-view images to generate instance masks for each organ with a corresponding instance identification (ID). The multi-view instance IDs corresponding to the same plant organ were then matched and refined using a specially designed instance matching (IM) module. The instance NeRF was developed to render an implicit scene containing color, density, semantic and instance information, which was ultimately converted into high-precision plant instance point clouds based on volume density. The results proved that in semantic segmentation of point clouds, PlantSegNeRF outperformed the commonly used methods, demonstrating an average improvement of 16.1 %, 18.3 %, 17.8 %, and 24.2 % in precision, recall, F1-score, and intersection over union (IoU) compared to the second-best results on structurally complex datasets. More importantly, PlantSegNeRF exhibited significant advantages in instance segmentation. Across all plant datasets, it achieved average improvements of 11.7 %, 38.2 %, 32.2 % and 25.3 % in mean precision (mPrec), mean recall (mRec), mean coverage (mCov), and mean weighted coverage (mWCov), respectively. Furthermore, PlantSegNeRF demonstrates superior few-shot, cross-species performance, requiring only multi-view images of few plants to train models applicable to specific or similar varieties. This study extends organ-level plant phenotyping and provides a high-throughput way to supply high-quality 3D data for developing large-scale artificial intelligence (AI) models in plant science. • A comprehensive dataset of well-labeled two-dimensional (2D) images and point clouds dataset of plants was established, including various varieties and growth stages. 50 plant samples were collected for each type. • A novel multi-view image instance matching (IM) module was proposed to align plant organ instance identifications (IDs) across different viewpoints, serving as the foundation for organ-level instance segmentation. • A multi-channel instance neural radiance fields (NeRF) module with encoding color, semantic, and instance information was developed to achieve high-precision mapping of 2D image colors, semantics, and aligned instances into 3D space, enabling point cloud background removal and fine-grained segmentation of plant organs.
Why it matches plant phenotyping methods植物器官の3D点群再構成・インスタンス分割を開発し、セグメンテーション性能を検証する手法研究であり、器官レベル表現型抽出を直接支援するため。
abstractwe proposed a novel approach called plant segmentation neural radiance fields (PlantSegNeRF), aiming to directly generate high-precision instance point clouds from multi-view RGB image sequences for a wide range of plant species.
Reconstruction of crop three-dimensional (3D) point clouds is essential for monitoring phenotypic parameters, like plant height and leaf area index (LAI), which is a critical phenotype predictor for smart crop breeding. The main 3D reconstruction technologies include image-based approaches, laser scanning, and depth camera methods. Among these methods, image-based structure-from-motion (SfM) is widely used due to its low cost and high accuracy. However, field crop canopy image data for high-resolution point cloud construction are often large-scale, unordered, and uncalibrated. Conventional SfM methods struggle with 3D reconstruction due to high computational costs and long processing times, delaying phenotypic analysis. To address this issue, we developed an improved global SfM algorithm, which increases the point cloud reconstruction speed by an average of 1.39 times compared to traditional incremental SfM methods and by more than 10 % on average compared to two mainstream global SfM algorithms. In addition, we integrated three types of predictors, point cloud features, color indices and texture features, through multi-feature data fusion and machine learning. A random forest algorithm for the prediction of LAI for a combined data set of four different crops, and using all three categories of predictors, achieved higher monitoring accuracy compared to using a single feature category (R²=0.78 vs R²=0.71–0.74). This new method, which includes an improved global SfM algorithm and a three-predictor fusion-based LAI monitoring approach, offers an efficient and reliable solution for precise crop phenotyping and continuous growth monitoring in complex field environments, enabling accurate assessment of crop morphology and developmental dynamics.
Why it matches plant phenotyping methods改良型SfMによる3D再構成と、特徴量融合・機械学習によるLAI推定を開発しており、植物表現型取得手法が研究の中心である。
abstractwe developed an improved global SfM algorithm
Field / plotLaboratory / benchtopNeRF / 3D Gaussian SplattingPhotogrammetry / SfM / MVSLiDAR / point cloudRGB-D / ToFWhole plant / canopy / plot / field2D/3D reconstruction
Three-dimensional point cloud (3DPC) data capture detailed geometric and structural plant traits beyond the capability of 2D imaging. When combined with artificial intelligence (AI), it offers a powerful, non-invasive tool for plant phenotyping, which is crucial for driving advancements in plant breeding and agriculture. However, challenges related to data complexity, limited datasets, and model generalization hinder 3DPC’s widespread adoption. To provide a comprehensive overview and guide future research in this area, we conducted a systematic literature review (SLR) following Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines by analysing 381 papers published between January 2017 and October 2025 from major databases. Our review examines the advantages, current status, limitations, and future directions of AI applications in 3DPC-based plant phenotyping. Our findings indicate a rapid increase in publications since 2022, with deep learning (DL) methods, especially pointwise MLP-based networks, driving much of this growth, with a notable recent surge in Transformer-based, Graph-based, and particularly Hybrid models that combine their strengths. Furthermore, novel methods like Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) are emerging as powerful tools for 3D reconstruction and scene synthesis. Time-of-Flight (ToF) and Structure from Motion and Multi-View Stereo (SfM-MVS) technologies remain the predominant 3DPC data acquisition techniques. Research in this area focuses on trees/shrubs and cereals, typically involving single-species studies. Although the overall use of public datasets remains low (18.9%), their adoption has significantly increased since 2020. Key limitations identified include: (1) a lack of standardized data collection and formats, (2) insufficient model robustness and generalization, especially from lab to field, (3) high computational demands, and (4) a reliance on species-specific models. The future of AI-driven 3DPC phenotyping hinges on overcoming these bottlenecks. Priority should be given to: developing field-deployable, computationally efficient models; exploring the potential of the foundation model; establishing diverse and standardized public datasets; and strengthening the integration of 3D phenomics with genomics to bridge the genotype-to-phenotype gap. This review provides a foundational roadmap to guide research in plant phenomics, crop breeding, and plant science.
Why it matches plant phenotyping methods3D点群とAIによる植物形質取得・解析を中心に扱う体系的レビューであり、植物フェノタイピング手法のレビューとして明確に適格。
titleAI-driven 3D point cloud analysis in plant phenotyping: A Systematic Review
Quantitative characterization of complete canopy architecture is essential for accurate evaluation of crop photosynthesis and yield potential, thereby supporting crop ideotype design. Although various sensing technologies enable three-dimensional (3D) reconstruction of individual plants and canopies, they often fail to describe canopy architecture accurately because of severe occlusion in dense populations. To address this limitation, we developed an effective framework for the 3D reconstruction of complex and dynamic population-scale canopy architecture in rapeseed using unmanned aerial vehicle multi-view imagery combined with a novel point cloud completion model. A complete point cloud generation pipeline was first established to enable automated training data annotation, allowing discrimination between surface points and occluded points within the canopy. The proposed crop population point cloud completion network (CP-PCN) integrates a multi-resolution dynamic graph convolutional encoder, a point pyramid decoder, a dynamic graph convolutional feature extractor, and a generative adversarial network-based loss function to predict occluded canopy points. CP-PCN achieved chamfer distance values of 3.35 to 4.51 cm across four growth stages, outperforming the state-of-the-art transformer-based method PoinTr. Ablation analyses confirmed that each of the four modules contributes to overall model accuracy. In addition, validation experiments showed that the improved architectural completeness achieved by CP-PCN resulted in more accurate yield estimation compared with incomplete and PoinTr-completed point clouds. CP-PCN also demonstrated strong cross-crop generalizability by successfully reconstructing mature rice canopies. Overall, this framework provides a scalable approach for quantitative analysis of complex canopy architectures in field-grown crops.
Why it matches plant phenotyping methodsUAVマルチビュー画像から遮蔽点を補完し、作物群落の3Dキャノピー構造を再構成する手法を開発・検証しており、植物表現型取得が研究の中心です。
abstractwe developed an effective framework for the 3D reconstruction of complex and dynamic population-scale canopy architecture in rapeseed using unmanned aerial vehicle multi-view imagery combined with a novel point cloud completion model
Reproduction assets foundThe paper's Data and code availability statement explicitly deposits all source code and test data for the CP-PCN phenotyping pipeline on a public GitHub repository, matching an allowed URL.Code · publicAll source code and test data used in this study are publicly available on GitHub ( https://github.com/Ziyue-Guo/CP-PCN.git ).Open asset ↗https://github.com/Ziyue-Guo/CP-PCN.git · CP-PCNlines:133-158Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 15 Sept 2026
Reconstruction of crop three-dimensional (3D) point clouds is essential for monitoring phenotypic parameters, like plant height and leaf area index (LAI), which is a critical phenotype predictor for smart crop breeding. The main 3D reconstruction technologies include image-based approaches, laser scanning, and depth camera methods. Among these methods, image-based structure-from-motion (SfM) is widely used due to its low cost and high accuracy. However, field crop canopy image data for high-resolution point cloud construction are often large-scale, unordered, and uncalibrated. Conventional SfM methods struggle with 3D reconstruction due to high computational costs and long processing times, delaying phenotypic analysis. To address this issue, we developed an improved global SfM algorithm, which increases the point cloud reconstruction speed by an average of 1.39 times compared to traditional incremental SfM methods and by more than 10 % on average compared to two mainstream global SfM algorithms. In addition, we integrated three types of predictors, point cloud features, color indices and texture features, through multi-feature data fusion and machine learning. A random forest algorithm for the prediction of LAI for a combined data set of four different crops, and using all three categories of predictors, achieved higher monitoring accuracy compared to using a single feature category (R²=0.78 vs R²=0.71–0.74). This new method, which includes an improved global SfM algorithm and a three-predictor fusion-based LAI monitoring approach, offers an efficient and reliable solution for precise crop phenotyping and continuous growth monitoring in complex field environments, enabling accurate assessment of crop morphology and developmental dynamics.
Why it matches plant phenotyping methods改良したSfMによる3D再構成と、特徴量融合・機械学習によるLAI推定を開発・評価しており、植物表現型取得手法が研究の中心である。
abstractwe developed an improved global SfM algorithm
Robotic apple harvesting requires precise instance segmentation and 3D localization, especially for multi-colored apples under complex orchard conditions with occlusions and variable lighting. Current deep learning methods lack robustness and accuracy for such scenarios, limiting automation. This study proposes improved YOLOv8-based models to enhance segmentation of multi-colored apples, combined with a high-precision 3D localization pipeline to advance practical robotic harvesting. To address these issues, this study collected apple images in three colors from two locations, creating a dataset of 5171 images. Four enhanced YOLOv8-based models—RA-YOLO, GA-YOLO, YA-YOLO, and MCA-YOLO—were proposed for segmenting red, green, yellow, and mixed multi-colored apples. RA-YOLO integrates the GD mechanism and EMBConv structure based on EfficientNet's MBConv. GA-YOLO replaces standard convolutions with dynamic serpentine convolution and adds the P6 layer for large object detection. YA-YOLO utilizes deformable convolution (DCNv2) and introduces the new attention mechanism MPCA. MCA-YOLO combines the P6 layer, DCNv2, and EMBConv structure, merging the strengths of other models. RA-YOLO, GA-YOLO, and YA-YOLO achieved mAP values of 95.2 %, 96.4 %, and 95.4 %, respectively, for single-colored apple instance segmentation, surpassing baseline models and those in existing literature. MCA-YOLO achieved mAP values of 95.6 %, 96.6 %, and 94.6 % for single-colored apples and 95.6 % for mixed multi-colored apples. Ablation experiments validated the necessity of each module. Finally, a high-precision 3D localization and shaping pipeline was developed, achieving an average localization error of 2.636 mm and a shaping error of 0.768 mm, enabling millimeter-level localization and sub-millimeter-level shaping for apple harvesting optimization.
Why it matches plant phenotyping methodsリンゴ果実のインスタンス分割、3D位置推定、形状推定を開発・検証しており、収穫対象の単なる検出を超えて果実形状という植物器官形質を定量化する手法が中心である。
abstractThis study proposes improved YOLOv8-based models to enhance segmentation of multi-colored apples, combined with a high-precision 3D localization pipeline
Accurate and temporally consistent multispectral observations are essential for monitoring alfalfa yield and quality, given its frequent harvest cycles and rapid regrowth. However, optical satellite imagery is often constrained by cloud cover, revisit intervals, and sensor availability. To overcome these limitations, we propose a novel Alfalfa Multimodal Generative Adversarial Network (AMGAN) designed for near-daily multispectral image reconstruction. Unlike conventional image-to-image or spatiotemporal fusion methods that overlook crop-specific characteristics, are restricted to observed timestamps, or depend heavily on dense temporal series, AMGAN leverages multisource (Landsat-8/9, Sentinel-1, PlanetScope) and multimodal (climate, geographic, temporal) information within an adversarial learning paradigm. This enables high-quality image generation from minimal inputs. Extensive experiments across five major alfalfa-producing states in the United States (2022-2024) show that AMGAN consistently surpasses four state-of-the-art (SOTA) deep learning baselines. It achieves higher reconstruction accuracy across all spectral bands, with pronounced gains in red-edge and near-infrared (NIR) regions critical for vegetation assessment. Multisource integration and multimodal cues enhance robustness, ensuring reliable performance under diverse observation scenarios. The reconstructed imagery was subsequently evaluated in alfalfa yield and quality prediction tasks. Results demonstrated high predictive accuracy for dry matter yield (DM) in the cross validation (CV) experiment with a coefficient of determination (R²) of 0.80, and moderate correlations for selected quality traits such as crude protein (CP), non-fiber carbohydrates (NFC), and minerals, while nutritive value traits tied to complex biochemical processes remained more challenging. Overall, this study underscores the potential of multimodal adversarial learning to bridge observational gaps in alfalfa monitoring. The proposed framework provides a scalable, crop-specific approach for generating temporally dense imagery, supporting precision management for biomass-related and proximate quality traits, while performance for digestibility traits remains limited.
Why it matches plant phenotyping methodsアルファルファの収量・品質形質推定を支えるマルチスペクトル画像再構成手法を開発し、複数手法との比較検証と形質予測評価を行っており、フェノタイピング手法が中心である。
abstractwe propose a novel Alfalfa Multimodal Generative Adversarial Network (AMGAN) designed for near-daily multispectral image reconstruction.
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.
Accurate per-branch 3D reconstruction is a prerequisite for autonomous UAV-based tree pruning; however, dense disparity maps from modern stereo matchers often remain too noisy for individual branch analysis in complex forest canopies. This paper introduces a progressive pipeline integrating DEFOM-Stereo foundation-model disparity estimation, SAM3 instance segmentation, and multi-stage depth optimization to deliver robust per-branch point clouds. Starting from a naive baseline, we systematically identify and resolve three error families through successive refinements. Mask boundary contamination is first addressed through morphological erosion and subsequently refined via a skeleton-preserving variant to safeguard thin-branch topology. Segmentation inaccuracy is then mitigated using LAB-space Mahalanobis color validation coupled with cross-branch overlap arbitration. Finally, depth noise - the most persistent error source - is initially reduced by outlier removal and median filtering, before being superseded by a robust five-stage scheme comprising MAD global detection, spatial density consensus, local MAD filtering, RGB-guided filtering, and adaptive bilateral filtering. Evaluated on 1920x1080 stereo imagery of Radiata pine (Pinus radiata) acquired with a ZED Mini camera (63 mm baseline) from a UAV in Canterbury, New Zealand, the proposed pipeline reduces the average per-branch depth standard deviation by 82% while retaining edge fidelity. The result is geometrically coherent 3D point clouds suitable for autonomous pruning tool positioning. All code and processed data are publicly released to facilitate further UAV forestry research.
Why it matches plant phenotyping methods樹木の枝を対象に、ステレオ深度推定・セグメンテーション・深度最適化による枝単位の3D形状抽出手法を開発・評価しており、植物器官の表現型取得が中心である。
abstractThis paper introduces a progressive pipeline integrating DEFOM-Stereo foundation-model disparity estimation, SAM3 instance segmentation, and multi-stage depth optimization to deliver robust per-branch point clouds.
Lignin plays a central role in the formation and function of secondary cell walls in vascular plants. However, the structural consequences of lignin modification for cell wall properties and cellular function in grasses remain poorly understood. Here, we investigated how cinnamyl alcohol dehydrogenase (CAD) deficiency alters vascular cell architecture in Sorghum bicolor, using the brown midrib-6 (bmr6) mutant as a model system. Biochemical and histochemical analyses confirmed altered lignin chemistry in bmr6, including increased incorporation of hydroxycinnamaldehyde residues and reduced tricin levels. We applied ptychographic X-ray computed tomography (PXCT) to quantify the cell wall geometry, in three dimensions, at nanometer-scale resolution. PXCT enabled measurements of wall thickness distribution and lumen shape along tracheary elements. Analyses revealed no significant differences in wall thickness between wild-type and bmr6 plants. However, three-dimensional morphometric descriptors indicated reduced lumen convexity in bmr6, suggesting localized modifications not detectable by conventional two-dimensional imaging. Water flow numerical simulations through PXCT-derived images indicated reduced vessel permeability and simulated hydraulic conductivity in bmr6, suggesting that subtle geometric changes may influence performance. These findings highlight the value of three-dimensional imaging for resolving cell wall organization and provide new insight into the architectural resilience of grass xylem in response to targeted lignin modification. HighlightThree-dimensional X-ray nano-imaging reveals alterations in the cell wall architecture that affect simulated hydraulic performance under reduced CAD activity in sorghum.
Why it matches plant phenotyping methods植物の木部細胞壁形状をナノスケール3D画像から定量化するPXCT手法が研究の中心であり、壁厚・内腔形状・形態記述子を抽出しているため、植物表現型計測の実質的な適用に該当する。
abstractWe applied ptychographic X-ray computed tomography (PXCT) to quantify the cell wall geometry, in three dimensions, at nanometer-scale resolution.
• Novel pipeline simplifying pose annotation • Novel method to quantify the occlusion rate was developed • 99.6% reduction in the amount of manual annotations • Training with an occlusion rate ≤ 95% for the labels lead to the best performance • Improved fruit detection and similar pose estimation as state of the art Automating tasks in orchards is challenging because of the large amount of variation in the environment and occlusions. One of the challenges is apple pose estimation, where key points, such as the calyx, are often occluded. Recently developed pose estimation methods no longer rely on these key points, but still require them for annotations, making annotating challenging and time-consuming. Due to the abovementioned occlusions, there can be conflicting and missing annotations of the same fruit between different images. Novel 3D reconstruction methods can be used to simplify annotating and enlarge datasets. We propose a novel pipeline consisting of 3D Gaussian Splatting to reconstruct an orchard scene, simplified annotations, automated projection of the annotations to images, and the training and evaluation of a pose estimation method. Using our pipeline, 105 manual annotations were required to obtain 28,191 training labels, a reduction of 99.6%. Experimental results indicated that training with labels of fruits that are ≤ 95% occluded resulted in the best performance, with a neutral F1 score of 0.927 on the original images and 0.970 on the rendered images. Adjusting the size of the training dataset had small effects on the model performance in terms of F1 score and pose estimation accuracy. It was found that the least occluded fruits had the best position estimation, which worsened as the fruits became more occluded. It was also found that the tested pose estimation method was unable to correctly learn the orientation estimation of apples.
Why it matches plant phenotyping methods3D Gaussian Splattingによる再構成、アノテーション投影、リンゴの姿勢推定を統合した新規パイプラインが研究の中心であり、果実の位置・向きという植物器官形質を抽出・評価している。
abstractWe propose a novel pipeline consisting of 3D Gaussian Splatting to reconstruct an orchard scene, simplified annotations, automated projection of the annotations to images, and the training and evaluation of a pose estimation method.
This data article describes a curated RGB-Depth image dataset captured using an Intel RealSense D435 stereo depth camera mounted on an autonomous mobile platform during field deployments at commercial baby broccoli farms in Victoria, Australia. The dataset comprises 1759 paired RGB images (640 × 480 pixels) and corresponding 16-bit depth frames acquired under both daytime (natural sunlight) and night-time (LED illumination) conditions, designed to support research in agricultural computer vision and robotic harvesting. Images were selected from 39,765 raw acquisitions through a reproducible Python curation pipeline applying quality filtering (blur detection, brightness thresholds, corruption detection), perceptual hash-based duplicate removal, and manual review. The final dataset includes 924 daytime and 835 night-time image pairs containing baby broccoli plants at various growth stages. The dataset provides RGB camera intrinsic parameters and pixel-aligned depth maps to enable 3D point cloud reconstruction. Potential applications include developing deep learning models for crop detection and segmentation, validating depth-based size estimation methods, and benchmarking illumination-robust vision systems. All data and curation code are publicly available under a CC BY 4.0 license.
Why it matches plant phenotyping methodsRGB-Depth画像データセットの構築と再現可能なキュレーションを中心とし、作物検出に加えてサイズ推定という植物形質の評価・ベンチマークに利用できるため。
titleA field-acquired RGB-Depth image dataset for computer vision-based baby broccoli detection and size estimation under varying illumination conditions.
Reproduction assets foundThe paper is a data article describing a public Mendeley Data repository containing the authors' field-acquired RGB-D baby broccoli image dataset (1759 image pairs, ground truth diameter annotations, camera intrinsics, and curation/annotation code), directly reproducing the paper's phenotyping measurements and analysisDataset · publicRepository name: Mendeley Data
Data identification number: 10.17632/px5p6zdk6k.3
Direct URL to data: https://data.mendeley.com/datasets/px5p6zdk6k/3Open asset ↗Mendeley Data · 10.17632/px5p6zdk6k.3html-lines:95-155Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Cuscuta spp. are stem holoparasitic plants that use haustoria to draw water, photosynthates, and nutrients from host plant vascular systems. Cuscuta has served as a model plant for understanding plant-plant interactions and haustoria development of stem parasitic plants; however, studies of the three-dimensional (3D) internal host-parasite interface and interconnections are limited due to their unique structures developed inside host stems. This study investigates laser ablation tomography (LATscan) technology, which generates 3D reconstructions from stacked high-resolution 2D cross-sectional images. LATscan imaging of Cuscuta invading Arabidopsis (Arabidopsis thaliana) and beet (Beta vulgaris) stems yielded 3D renderings and detailed images of the anatomy of Cuscuta-host tissue interactions, including Cuscuta searching hyphae penetrating the host vasculature. Laser-tissue interactions generated color contrast and facilitated easy differentiation between Cuscuta and host tissues in 3D renderings and 2D images, demonstrating that LATscan technology can be an efficient tool to investigate the development and function of host-parasitic plant interactions.
Why it matches plant phenotyping methodsレーザーアブレーション断層撮影による植物組織の3D画像化・再構成が研究の中心であり、宿主—寄生植物組織の形態・構造状態を取得する手法を実証している。
abstractThis study investigates laser ablation tomography (LATscan) technology, which generates 3D reconstructions from stacked high-resolution 2D cross-sectional images.
Abstract An understanding of spike shape will be of great benefit for improving wheat yields. Traditional manual measurements of spike traits are slow and prone to human error, preventing large-scale phenotyping. Employing imaging techniques will allow researchers to measure multiple morphometric parameters simultaneously. While 2D imaging provides a rapid screening method, 3D imaging offer a more comprehensive understanding of spike shape, revealing complex external structures. This study addresses the challenge of developing a high-resolution 3D surface-scanning pipeline to accurately quantify wheat spike morphology across diverse genotypes. Using a 3D surface-scanner, sharp point clouds of individual spikes were reconstructed and automatically aligned and analysed to extract key morphological features including spike length, volume, and thickness profile. New shape descriptors based on thickness profiles, local extremes, statistical curve fitting, segmentation of spikes into zones of aborted spikelets, base and apical segments as well as the extraction of spike/spikelets branching and endpoints of components were introduced to capture detailed structural variation between genotypes. Correlations between the 3D-derived traits and traditional metrics such as spike weight, spikelet number and seed weight confirmed the biological relevance of the extracted parameters. The method distinguished morphological differences among twelve wheat genotypes, revealing distinct shape types such as long, short, compact, and awned spikes. By combining precise 3D imaging with computational analysis, this approach provides a non-destructive framework for spike phenotyping. These findings demonstrate that 3D surface-scanning can deliver accurate and reproducible measurements of wheat spike architecture, offering new opportunities for linking morphology with genetics and yield potential in modern breeding programs.
Why it matches plant phenotyping methods小麦穂の形態形質を3D画像から抽出するパイプラインを開発し、形質の相関・遺伝子型間比較で検証しており、表現型取得法が研究の中心である。
abstractThis study addresses the challenge of developing a high-resolution 3D surface-scanning pipeline to accurately quantify wheat spike morphology across diverse genotypes.
Reproduction assets foundThe preprint explicitly shares sample 3D spike scan data and the trait-extraction analysis code in the authors' public GitHub repository, with separate Data and code availability statements.Dataset · public1003/1)
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1. Sanchez-Bragado R, Molero G, Araus JL, and Slafer GA. Awned versus awnless wheat spikes:
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does it matter? Trends in plantOpen asset ↗LatifaGreche/3D-WheatSpikeMorphologyExtractionpdf-raw-page:26 lines:1-57Code · public1003/1)
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does it matter? Trends in plantOpen asset ↗LatifaGreche/3D-WheatSpikeMorphologyExtractionpdf-raw-page:26 lines:1-57Plant phenotyping relevance match · UnverifiedOpenAlex · checked 13 Sept 2026
Replicability of Digital Terrain Models (DTMs) and Canopy Height Models (CHMs) derived from drone photogrammetry is important to understand the extent to which time-series are exposed to methodological noise and conceal real environmental changes. Root mean square error (RMSE) distribution metrics (median/IQR) were used as indicators of replicability across seven drone survey setups, three dense matching scales, and 13 ground point filters in a challenging shrubland environment (total of 273 DTMs and CHMs). We conclude that methodological effects have considerable potential to negatively affect replicability. A power-law relationship between point cloud density and dense matching resolution suggested that important dense matching resolution thresholds exist beyond which replicability degrades considerably. For our Arctic study area, replicability of DTMs (median ± 0.1 m RMSE Vegetated Vertical Accuracy) and CHMs (within ±0.05 m of true site-level heights) is most likely when source imagery is collected with ≤1.5 cm spatial resolution and side-lap of >80%, and if classified point clouds are generated using full-scale dense matching and Triangular Irregular Network filtering. Negative biases for maximum shrub height estimates increased from 4–9% to 14–50% with coarser imagery. We advocate for increased attention to drone-derived model replicability to separate real environmental changes from noise during a period of rapid ecological and geomorphic change.
Why it matches plant phenotyping methodsドローン写真測量によるDTM/CHMの再現性を比較・検証し、低木の樹高推定精度に影響する撮影・点群処理条件を評価しているため、植物形質取得手法が中心である。
abstractReplicability of Digital Terrain Models (DTMs) and Canopy Height Models (CHMs) derived from drone photogrammetry is important to understand the extent to which time-series are exposed to methodological noise
Analyzing morphological parameters and growth at the cell level is crucial for a better understanding of organ development. The most popular approach for quantitative analyses of development relies on confocal imaging of an organ expressing a fluorescent membrane marker over several time points, and analyzing the confocal dataset to quantify changes in morphological parameters and growth rate. These analyses are commonly done on the surface, with the assumption that changes in the surface of a cell reflect faithfully changes of the whole, volumetric cell. However, this assumption has not yet been systematically and explicitly tested. It is also not clear how the correlation between areal and volumetric measurements would change over time. Here, we combined time-series live imaging and three-dimensional reconstruction to compare surface and volumetric size and growth of cotyledon and sepal epidermal cells in Arabidopsis thaliana. We found that on average, surface area is tightly correlated with volume in both cell size and growth, supporting the use of surface area as a good proxy for volume at the population level. However, cells with similar surface areas or surface growth can display substantial differences in volume and volumetric growth. This happens due to variation in cell thickness, which in turn is controlled by microtubules. These findings highlight limitations of surface-based metrics, and call for volumetric analyses if a more accurate assessment of cell parameters is needed.
Why it matches plant phenotyping methods植物細胞の表面積・体積・成長をライブイメージングと3次元再構成で比較し、表面積を体積の代理指標として検証することが中心である。
abstractHere, we combined time-series live imaging and three-dimensional reconstruction to compare surface and volumetric size and growth of cotyledon and sepal epidermal cells in Arabidopsis thaliana.
Monitoring multi-temporal forest vertical structure in anthropogenically disturbed and topographically complex landscapes remains a major challenge, particularly when low-cost remote sensing technologies are used. This study aims to quantify forest vertical structure change and to determine whether these changes are systematically regulated by geomorphometric controls rather than occurring randomly. A multi-temporal unmanned aerial vehicle (UAV) photogrammetry workflow based on Structure from Motion (SfM) was applied to generate annual Canopy Height Models (CHMs) for 2023, 2024, and 2025. To ensure temporal robustness, the 95th percentile of canopy height (P95) was adopted as the primary structural metric, and vertical change was quantified using a difference-based indicator (ΔP95). Random Forest (RF) regression was used to model the relationship between canopy height change and terrain-derived predictors, including slope, aspect, and Topographic Wetness Index (TWI). The results reveal a consistent vertical growth signal across the study area, with a mean ΔP95 increase of 0.65 m over the monitoring period, clearly exceeding the photogrammetric vertical error (RMSE = 0.082 m). Positive canopy height changes are concentrated on moisture-favored, moderately sloping and north-facing terrain, whereas negative changes (down to −1.20 m) are mainly associated with mining-disturbed and steep surfaces. The RF model achieved high explanatory performance (training R2 = 0.919) and identified aspect (20%), slope (18%), and TWI (18%) as the dominant controls on forest vertical dynamics. These findings demonstrate that forest vertical structure evolution in disturbed landscapes is not stochastic but is systematically governed by terrain-driven hydro-morphological and microclimatic conditions. The main contribution of this study is the development of an interpretable, change-focused UAV–machine learning framework that moves beyond single-epoch canopy height estimation and enables process-oriented analysis of terrain–vegetation interactions. The proposed approach provides a cost-effective and transferable tool for forest monitoring and post-mining restoration planning in complex terrain settings.
Why it matches plant phenotyping methodsUAV-SfMによる樹冠高モデルと機械学習を組み合わせ、森林の垂直構造変化という植物形質を定量化する再利用可能な手法を開発・適用しており、フェノタイピング手法が中心である。
abstractA multi-temporal unmanned aerial vehicle (UAV) photogrammetry workflow based on Structure from Motion (SfM) was applied to generate annual Canopy Height Models (CHMs) for 2023, 2024, and 2025.
Timely acquisition of crop chlorophyll contents is essential for effective field management decisions and comprehensive crop nutritional monitoring. Unmanned Aerial Vehicle (UAV) has been extensively utilized for canopy chlorophyll content monitoring. However, prior studies predominantly concentrated on the horizontal variability of SPAD, with few researches dedicated to monitoring the vertical distribution of SPAD. This study aimed to develop a high-resolution vertical SPAD distribution model for winter wheat by integrating UAV-based Cross-Circle Oblique (CCO) photography with the Structure from Motion (SFM) and Multi-View Stereo (MVS) methods. The canopy was divided into upper, middle, and lower layers based on plant height. Nadir and CCO photography were used to capture images, with Nadir photography constructing the upper canopy SPAD model and CCO data used for the vertical distribution model (including upper, middle and lower layers). Shapley values were applied to evaluate feature importance in different machine learning models (GBR, gradient boosting regression; RF, random forest; SVM, support vector machine; RR, ridge regression). Finally, a vertical SPAD distribution model for winter wheat was created with a 0.1-meter gradient. The results demonstrated that the accuracy of SPAD vertical distribution inversion using single machine learning algorithms (KNN: k-nearest neighbor, RR: ridge regression) was lower than that achieved by ensemble learning methods (stacking, RF: random forest). Ensemble learning enhanced R 2 and RMSE by 0.13 and 1.23 for the training set, and by 0.09 and 0.56 for the test set, respectively. CCO photography exhibited high accuracy in capturing the SPAD spatiotemporal distribution of winter wheat, with R 2 values for the training sets across all three growth stages surpassing 0.8, and R 2 values for the test sets ranging from 0.6 to 0.81. The highest accuracy in SPAD vertical distribution inversion was achieved during the heading stage, with R 2 and RMSE values for the training and test sets of 0.89, 2.58, and 0.80, 3.34, respectively. Therefore, UAV-based CCO photography combined with the SFM-MVS algorithm demonstrates preliminary potential for vertical SPAD phenotyping and precision nutrient management; however, further validation across different growing seasons and wheat varieties is necessary to confirm its broad generalizability.
Why it matches plant phenotyping methodsUAV斜視画像とSfM-MVS、機械学習を統合し、コムギの垂直SPAD分布を推定する方法の開発・検証が研究の中心である。
abstractThis study aimed to develop a high-resolution vertical SPAD distribution model for winter wheat by integrating UAV-based Cross-Circle Oblique (CCO) photography with the Structure from Motion (SFM) and Multi-View Stereo (MVS) methods.
Published12 Feb 2026The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciencesCited by 0 · OpenAlex ↗
Abstract. The article presents an approach for multisensors integrating data, namely terrestrial laser scanning (TLS; Leica RTC360), the MandEye mobile SLAM system equipped with a Livox MID-360 LiDAR sensor, and multi-temporal RGB and colour-infrared (CIR) aerial imagery supported by Airborne Laser Scanning (ALS) point clouds, for the detailed inventory of historical gardens and monitoring the process of rebuilding the bosquets in the Lower Gardens of the Royal Castle in Warsaw. The Castle Gardens constitute a unique cultural landscape of exceptional historical and symbolic value, where fragments of pre-war hornbeam bosquets have survived and now form the basis for contemporary restoration efforts. The study demonstrates how the integration of complementary active and passive sensing techniques enables a multi-scale, three-dimensional documentation of vegetation structure, capturing both fine-scale geometric details and broader spatial context. TLS data provide high-precision representations of tree geometry and hedge structure, while mobile SLAM measurements allow rapid mapping of garden interiors and hard-to-access areas. These ground-based datasets are complemented by ALS and photogrammetric point clouds derived from archival and contemporary aerial imagery, enabling the analysis of canopy structure and long-term vegetation growth. Additionally, CIR images were utilised to derive vegetation indices, supporting the assessment of plant vitality and temporal changes in biological condition. The results demonstrate that the proposed multi-source integration framework allows effective monitoring of spatial development, height growth, and health condition of reconstructed bosquets. The approach provides a robust methodological basis for heritage greenery inventory and long-term conservation monitoring, supporting informed decision-making in the management of historic gardens.
Why it matches plant phenotyping methods複数の3D・航空画像・CIRセンサーを統合し、植生構造、樹高成長、植物活力・健康状態を抽出・監視する方法論が研究の中心であるため。
abstractThe study demonstrates how the integration of complementary active and passive sensing techniques enables a multi-scale, three-dimensional documentation of vegetation structure, capturing both fine-scale geometric details and broader spatial context.
Modeling the time-varying 3D appearance of plants during growth poses unique challenges: unlike most dynamic scenes, plants continuously generate new geometry as they expand, branch, and differentiate. Existing dynamic scene representations are ill-suited to this setting: deformation fields provide insufficient constraints to yield physically plausible scene dynamics, and 4D Gaussian splatting represents the same physical structures with different Gaussian primitives at different times, breaking temporal consistency. We introduce GrowFlow, a dynamic representation that couples 3D Gaussian primitives with a neural ordinary differential equation to model plant growth as a continuous flow field over geometric parameters (position, scale, and orientation). Our representation enables consistent appearance rendering and models nonlinear, continuous-time growth dynamics with full temporal correspondences for every primitive. To initialize a sufficient set of Gaussian primitives, we first reconstruct the mature plant and then learn a reverse-growth process, effectively simulating the plant's developmental history in reverse. GrowFlow achieves superior image quality and geometric coherence compared to prior methods on a new, multi-view timelapse dataset of plant growth, and provides the first temporally coherent representation for appearance modeling of growing 3D structures.
Why it matches plant phenotyping methods植物の成長を対象に、4D再構成と連続的な成長表現を開発し、幾何学的整合性と画像品質を既存手法・新規データセットで比較評価しているため、植物フェノタイピング手法が中心です。
abstractWe introduce GrowFlow, a dynamic representation that couples 3D Gaussian primitives with a neural ordinary differential equation to model plant growth as a continuous flow field over geometric parameters (position, scale, and orientation).
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract Accurate three-dimensional (3D) nuclear instance segmentation is a prerequisite for quantitative phenotyping in volumetric microscopy, yet remains challenging in densely packed tissues, irregular nuclear morphologies, and across heterogeneous imaging modalities. Here we present NucVerse3D, a deep-learning framework for generalized 3D nuclei instance segmentation that combines a residual attention 3D U-Net architecture with a reversible gradient-field representation for robust centroid-aware instance reconstruction. NucVerse3D is trained end to end in 3D using modality-agnostic preprocessing and isotropic scale normalization, enabling deployment across confocal microscopy, two-photon microscopy, light-sheet microscopy, micro–computed tomography, and scanning electron microscopy volumes. We benchmarked NucVerse3D on seven volumetric datasets spanning multiple species and tissues, comprising more than forty thousand manually annotated nuclei, including newly released ground-truth datasets of mouse liver tissue (control and hepatocellular carcinoma) and Drosophila brain glial nuclei. Across datasets, NucVerse3D achieved consistently high precision, recall, F1-score, and average precision, and outperformed the state-of-the-art methods particularly in dense and irregular settings, while remaining competitive on simpler cases. A single generalized model trained on pooled data matched the performance of dataset-specific models, and ablation experiments demonstrated that preprocessing and scale normalization substantially contribute to performance under strict intersection-over-union criteria. To demonstrate the biomedical utility of NucVerse3D, we applied it to three-dimensional liver images from a mouse model of hepatocellular carcinoma (HCC). High-fidelity, nucleus-by-nucleus segmentation enabled the quantification of the Nuclear Decoupling Score (NDS), which captures deviations in nuclear DNA–volume coupling at the single-nucleus level. NDS analysis revealed a progressive increase in nuclear abnormalities within tumor regions, forming spatially coherent domains of dysregulated nuclei and highlighting NDS as a potential quantitative biomarker of dysplastic and tumor tissue. Together, NucVerse3D provides a robust and generalizable solution for 3D nuclear instance segmentation and enables quantitative nuclear phenotyping across imaging modalities. Highlights - NucVerse3D provides accurate 3D nuclear instance segmentation across modalities - Residual attention and gradient fields enable robust separation of dense nuclei - New 3D annotated datasets of mouse liver and Drosophila brain are released - A generalized model achieves performance comparable to dataset-specific training - 3D nuclear phenotyping reveals spatially organized nuclear abnormalities in HCC
Why it matches plant phenotyping methods3D核インスタンスセグメンテーション手法を開発し、多数のデータセットでベンチマークするとともに、核形態状態の定量的フェノタイピングへ応用しているため。
abstractHere we present NucVerse3D, a deep-learning framework for generalized 3D nuclei instance segmentation
Analyzing three-dimensional (3D) phenotypic parameters of maize seedlings is of significant importance for maize cultivation and selection. However, existing methods often struggle to balance cost, efficiency, and accuracy, particularly when capturing the complex morphology of seedlings characterized by slender stems. To address these issues, this study proposes a novel end-to-end automated framework for extracting phenotypes using only consumer-grade RGB cameras. The pipeline initiates with Instant-NGP to rapidly reconstruct dense point clouds, establishing the 3D data foundation for phenotypic extraction. Subsequently, we formulate a directed topological graph-based mechanism. By mathematically defining bifurcation constraints via vector analysis, this mechanism guides a depth-first traversal strategy to explicitly disentangle stem and leaf skeletons. Building upon these decoupled skeletons, organ-level point cloud segmentation is achieved through constraint-based expansion, followed by density-based spatial clustering (DBSCAN) to detect individual leaves. Algorithms combining point cloud geometry with 3D Euclidean distance are also implemented to calculate key phenotypes including plant height and stem width. Finally, single-leaf skeleton fitting is used to estimate leaf length, and principal component analysis (PCA) is adopted to determine the stem–leaf angle, realizing the comprehensive automatic extraction of maize seedling phenotypes. Experiments show that the proposed method achieves high accuracy in extracting key phenotypic parameters. The mean relative errors for plant height, stem width, leaf length, stem-leaf angle, and leaf area are 0.76%, 2.93%, 1.26%, 2.13%, and 3.33%, respectively. Compared with existing methods as far as we know, the proposed method significantly improves extraction efficiency by reducing the processing time per plant to within 5 min while maintaining such high accuracy.
Why it matches plant phenotyping methodsRGBカメラとNeRF・点群処理・骨格解析を統合し、トウモロコシ幼苗の形質を自動抽出する手法を開発・精度評価した研究であり、フェノタイピング手法が中心である。
abstractthis study proposes a novel end-to-end automated framework for extracting phenotypes using only consumer-grade RGB cameras.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
BACKGROUND: Precise, non-destructive phenotyping of saffron during vegetative growth is critical for optimizing corm yield and accelerating breeding programs, yet systematic 3D measurements have remained elusive due to extreme morphological challenges: ultra-narrow leaves, severe mutual occlusion, and prostrate growth architecture. Traditional single-view imaging systems fail to resolve individual leaves under such conditions, limiting phenotypic analysis to whole-canopy descriptors. Here, we developed a specialized organ-level 3D phenotyping workflow specifically designed for narrow, overlapping leaves using a low-cost dual-camera rotary acquisition system integrated with open-source Structure-from-Motion Multi-View Stereo (SfM-MVS) reconstruction. RESULTS: > 0.94, MAPE < 6%), achieving accuracy benchmarks established for broad-leaved crops using commercial-grade hardware at 100 × lower cost. Systematic voxel sensitivity analysis across nine scales identified optimal preprocessing parameters (2 cm voxel size) balancing measurement precision with computational efficiency, addressing a critical reproducibility gap in plant phenotyping. Exploratory longitudinal tracking revealed that above-ground biomass was correlated with final corm yield (r = 0.68, P < 0.001), with mid-vegetative canopy volume also showing strong correlation (r = 0.52, P < 0.01), suggesting potential resource allocation trade-offs between vegetative expansion and storage organ development. CONCLUSIONS: This work demonstrates that organ-level 3D phenotyping of narrow, overlapping leaves is achievable using low-cost imaging hardware and transparent methodological workflows. Complete documentation of algorithmic parameters and hardware specifications enables direct replication and adaptation to other narrow-leaved crops (wheat, rice, onion, leek), democratizing access to high-throughput phenotyping in resource-limited settings. The workflow advances plant phenomics by demonstrating that methodological transparency and cost-effectiveness need not compromise measurement precision, opening new avenues for phenotype-to-genotype mapping and predictive breeding in underutilized crops.
Why it matches plant phenotyping methods低コストの双眼カメラとSfM-MVSによるサフラン葉の器官レベル3D形質取得ワークフローを開発し、精度検証、再現性、パラメータ最適化まで扱っており、植物フェノタイピング手法が研究の中心である。
abstractHere, we developed a specialized organ-level 3D phenotyping workflow specifically designed for narrow, overlapping leaves using a low-cost dual-camera rotary acquisition system integrated with open-source Structure-from-Motion Multi-View Stereo (SfM-MVS) reconstruction.
Fine-grained 3D phenotypic analysis of rice plays a vital role in rice breeding and yield estimation. However, a comprehensive rice data acquisition and segmentation pipeline is still lacking. While Neural Radiance Fields (NeRF) have shown impressive results in crop-level 3D reconstruction, their high sensitivity to data volume and camera viewpoints often leads to reconstruction failures for rice. In addition, the large-scale rice point clouds, coupled with heavy occlusion and visual similarity among grains, pose significant challenges for fine-grained trait extraction. To address the challenge of reconstructing rice point clouds under low-quality data conditions, we propose a novel method named Multi-Scale NeRF(MSNeRF). This method incorporates a structure-detail collaborative reconstruction mechanism and a dynamic initialization density scheduling strategy. Furthermore, we introduce a multimodal and multitask rice dataset (MMR) as a benchmark resource for future research. For rice point cloud segmentation, we develop Vision Rice Knowledge Graph Network(VRKGNet), which comprises an image segmentation module, a projection module, and a point cloud segmentation module enhanced with a Transformer to enlarge the receptive field. VRKGNet performs standalone point cloud segmentation and integrates image segmentation results from multiple viewpoints as prior knowledge to enhance semantic and instance-level segmentation. Extensive experiments demonstrate that MSNeRF achieves high-fidelity point cloud reconstruction with as few as 10 viewpoints. VRKGNet achieves superior rice plant segmentation with a semantic segmentation mIoU of 88.79% and an instance segmentation AP 25 of 84.55%, outperforming mainstream algorithms.
Why it matches plant phenotyping methods米の3D形質取得・再構成・分割を中核とする手法開発であり、データセット／ベンチマークも提供しているため、植物フェノタイピング手法文献に該当する。
abstractwe propose a novel method named Multi-Scale NeRF(MSNeRF)
Reproduction assets foundThe paper's authors explicitly state that the source code for MSNeRF and VRKGNet is publicly available on GitHub with testing scripts and test cases to reproduce the main results. The MMR dataset itself is only available upon request from the corresponding author, so it does not qualify as a public asset.Code · publicof Hefei Artificial Intelligence Breeding Accelerator Co. Ltd. ( NB2024005-02 ).
Data availability
The source code for the proposed methods, MSNeRF and VRKGNet, is publicly available on GitHub. The released repositories contain testing scripts and test cases used to reproduce the main results presented in this paper:
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MSNeRF : https://github.com/qfwysw/MSNeRF.git
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VRKGNet : https://github.com/qfwysw/VRKGNet.git
The datasets used in the experiments are available from the corresponding author upon reasonable request. For access or further inquiries, please contact the corresponding author.
Declaration of competing interest
The authors declare that they have no known competing financial iOpen asset ↗https://github.com/qfwysw/MSNeRF.gitlines:620-663Code · publicator Co. Ltd. ( NB2024005-02 ).
Data availability
The source code for the proposed methods, MSNeRF and VRKGNet, is publicly available on GitHub. The released repositories contain testing scripts and test cases used to reproduce the main results presented in this paper:
•
MSNeRF : https://github.com/qfwysw/MSNeRF.git
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VRKGNet : https://github.com/qfwysw/VRKGNet.git
The datasets used in the experiments are available from the corresponding author upon reasonable request. For access or further inquiries, please contact the corresponding author.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could haveOpen asset ↗https://github.com/qfwysw/VRKGNet.gitlines:620-663Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
3D phenotyping refers to the quantitative characterization of a plant's structural and morphological traits in three-dimensional space, allowing for a detailed analysis of plant architecture and growth patterns. In recent years, rapid advancements in non-destructive, high-throughput 3D imaging technologies have enabled the precise measurement of these traits. Initially focused on single-plant traits under controlled conditions, the field has now expanded towards robust applications in real-world field environments, enabling large-scale analyses of plant canopies and complex structures. This study focuses on the recent advancements in 3D crop phenotyping using point cloud technologies. It compares sensor technology and its application in controlled environments (Chamber-Crop Phenotyping, CCP) and field conditions (Field-Crop Phenotyping, FCP). Technologies such as Multiview stereo (MVS) reconstruction, LiDAR, and laser triangulation have enhanced plant phenomics by enabling high-throughput, non-destructive measurements of key traits such as canopy structure, leaf area, and stem diameter. This review highlights the strengths of the CCP, where environmental variables and flexibility are tightly controlled, facilitating precise trait measurement, and contrasts it with the challenges of the FCP, where unpredictable factors, such as occlusion, wind, light variability, and terrain complexity, complicate data acquisition. Various sensor platforms, including ground-based robotic systems and unmanned aerial vehicles (UAVs), have been discussed regarding their ability to overcome occlusion and limited sensor range in real-world conditions. The need to transition these technologies from laboratory environments to real-world agricultural applications is emphasized, highlighting their potential to improve crop management and plant breeding through accurate phenotypic trait extraction. Finally, current research gaps and future directions for integrating advanced sensor platforms and analytical techniques in both CCP and FCP settings are identified, emphasizing the need to enhance the scalability and robustness of 3D phenotyping for field applications.
Why it matches plant phenotyping methods3D作物フェノタイピングのセンサー技術、点群処理、対象形質、検証上の課題を中心に扱う方法論レビューであり、植物形質の取得手法が明確に中心である。
abstractThis study focuses on the recent advancements in 3D crop phenotyping using point cloud technologies.
Poplars are essential to China's forestry, contributing to timber production, ecological restoration, and shelterbelt construction. Branch architecture critically influences tree growth, demanding scalable solutions beyond manual methods to assess phenotypic variation in large-scale poplar breeding programs. Unmanned aerial vehicle light detection and ranging (UAV LiDAR) provides an efficient alternative; however, existing methods focus on conifers, leaving a gap in approaches for the more complex morphology of poplar branches. This study proposes a poplar branch reconstruction algorithm utilizing material transport flux and object-level geometric features from low-cost UAV LiDAR data. First, a voxel-based near-centroid method is used to extract skeleton points from tree point clouds. Next, a material transport flux model identifies individual branches, and geometric features of transport paths, including path length and curvature, are used to reconstruct each branch. Finally, branch parameters are estimated based on reconstructed branches. Data from a 5-ha plot were collected using the DJI Zenmuse L1 UAV LiDAR at the Shishou National Poplar Breeding Station, Hubei Province, China. Results demonstrate the proposed algorithm achieves high accuracy in first-order branch identification (F1-score = 1), with second-order branches having an average F1-score of 0.69. Branch length estimation demonstrates an RMSE of 0.47 m, while branch angles show an RMSE of 7.06°. The study also reveals structural variability in branch traits, with the highest variability observed in the second-order branch length (coefficient of variation = 29.68%), and a moderate positive correlation between first- and second-order branch lengths (correlation coefficient = 0.34), providing insights into tree growth patterns. This approach offers a framework for high-throughput phenotyping, which provides an efficient solution towrads advanced tree breeding using UAV LiDAR.
Why it matches plant phenotyping methodsUAV LiDARによるポプラの枝構造再構成と枝長・枝角度などの形質推定アルゴリズムを開発し、精度検証まで行っており、フェノタイピング手法が研究の中心である。
abstractThis study proposes a poplar branch reconstruction algorithm utilizing material transport flux and object-level geometric features from low-cost UAV LiDAR data.
Reproduction assets foundThe paper's Data availability statement provides a public URL to the supporting UAV LiDAR point cloud data (the paper-specific phenotyping measurements) hosted on forestdata.cn, with a DOI. No author analysis code or trained models are mentioned.Dataset · publicThe data that support this study are available from https://www.forestdata.cn/dataDetail.html?id=6f6934f4-680e-4e18-b4e3-1e7a60b85b55 . The DOI is 10.12459.14.0320260116001.0000.V1.Open asset ↗forestdata.cn · 10.12459.14.0320260116001.0000.V1lines:192-218Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
3D phenotyping of seedlings is crucial to tomato cultivation in greenhouse facilities. Current studies focus on high-quality point cloud reconstruction and artificial intelligence (AI) 3D segmentation to derive phenotypic traits like plant height and crown width, which heavily rely on manual annotation and possess high complexity in deployment. This study proposes a novel AI framework from pixels to points, for efficient 3D plant phenotyping of tomato seedlings. Through the integration of 2D-3D coordinate mapping and AI vision language models, the proposed method enables accurate reconstruction and analysis of 3D phenotypic traits from single-view data. Top-down RGB images and corresponding point clouds with spatial alignment are captured using a binocular camera. Vision language models are employed with the text prompt “plant” to automatically generate bounding boxes and masks, thereby minimizing manual annotation. These outputs are further transferred to a lightweight YOLO11-segment model. The core innovation is established in our 2D-3D mapping strategy, through which plant-specific 3D points are efficiently extracted using only 2D masks. Non-plant points within initial masks are repurposed to determine ground height for improved plant height estimation, while masks are refined using the Excess Green Index to enhance crown width measurement. An mAP₅₀ of 96.0% is achieved by the YOLO11-segment model. Concerning sparse canopy, highly accurate results are yielded by our phenotyping approach, with RMSE values of 1.7 cm for plant height and 1.0 cm for crown width, and R 2 values of 0.93 and 0.95 against manual measurements. For dense canopy, the usage of a reference chessboard improves the performance (RMSE was reduced from 9.57 cm to 2.07 cm). Annotation dependency is significantly reduced, computational complexity is decreased, edge deployment is supported, and efficient technology transfer is enabled by the presented method. Considerable potential is offered for high-throughput screening of elite tomato varieties with desirable agronomic traits. • Real-time low-cost 3D phenotyping of tomato plants is proposed. • Weak labels simplify the 3D plant segmentation. • Segment the 3D point cloud using 2D pixel-masks with spatial alignment. • Vision language models and knowledge transfer further simplify the AI application.
Why it matches plant phenotyping methodsトマト苗の3D表現型を抽出する画像・点群・AI統合手法を開発し、手動測定との精度検証も行っており、表現型取得法が研究の中心である。
abstractThis study proposes a novel AI framework from pixels to points, for efficient 3D plant phenotyping of tomato seedlings.
Sugarcane is an important economic crop, and key phenotypic traits such as plant height and leaf area play a crucial role in yield potential assessment and breeding selection. However, the quantification of these traits currently relies mainly on inefficient and destructive manual measurements, making it difficult to achieve continuous monitoring of plant growth. To address this limitation, this study integrates a YOLOv8x-seg instance segmentation model with 3D Gaussian Splatting (3DGS) and proposes a non-contact, high-precision 3D phenotyping method based on low-cost data acquisition using a smartphone. Multi-view RGB images are first processed using YOLOv8x-seg to extract plant foreground masks, which are then used as inputs for 3DGS-based reconstruction to generate 3D models. Plant height is automatically measured from the reconstructed models, while leaf area extraction involves a semi-automatic workflow combining image processing and manual steps. Experimental results demonstrate that the proposed approach enables accurate trait estimation, achieving a coefficient of determination (R2) of 0.9644 for plant height estimation (evaluated on a subset of 15 plants, with a mean absolute percentage error of approximately 1.5%) and an R2 of 0.8551 for leaf area estimation (validated on 10 plants). Ground-truth plant height was measured using a telescopic measuring rod, and leaf area was determined through destructive measurement with a leaf area meter (LI-COR Model LI-3000A). Ground-truth plant height values were obtained using a telescopic measuring rod, and leaf area was determined through destructive measurement with a leaf area meter (LI-COR Model LI-3000A). This method demonstrates the feasibility of using consumer-grade devices for high-fidelity 3D phenotyping and offers an effective approach for high-throughput sugarcane breeding applications.
Why it matches plant phenotyping methodsスマートフォン画像、インスタンスセグメンテーション、3D再構成を統合し、サトウキビの草高・葉面積を自動／半自動推定するフェノタイピング手法を開発・検証しており、方法が研究の中心である。
abstractproposes a non-contact, high-precision 3D phenotyping method based on low-cost data acquisition using a smartphone
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Accurate, spatially explicit quantification of the fraction of absorbed photosynthetically active radiation (fPAR) in tall conifer plantations is essential for productivity modelling and breeding, yet standard nadir-view optical UAV imagery yields only two-dimensional surface estimates. We developed an unmanned aerial workflow that fuses centimeter resolution LiDAR point clouds with five band multispectral imagery to produce a three-dimensional voxelized canopy structure in which top-of-canopy multispectral reflectance values are propagated downward within each vertical column. Ground measurements of fPAR and chlorophyll fluorescence were collected contemporaneously and used to calibrate Random Forest, XGBoost, Support Vector Machine (SVM), and Partial Least Squares Regression models built from 14 spectral indices. Random Forest explained 84 % of fPAR variance (RMSE = 0.12), outperforming alternative algorithms. Application of the trained Random Forest model to the voxelized canopy (0.01 m × 0.01 m × 2 m) across 28 ha generated three-dimensional fPAR maps that revealed a 26 ± 4 % increase from lower to upper crowns and a seasonal shift of up to 9 %. Compared with conventional plot-level inversion, the workflow significantly reduced field labour and improved prediction accuracy. The fusion pipeline provides a species-specific tool for high-throughput phenotyping, precision silviculture, and genomic selection in slash pine plantations under clear-sky conditions (solar zenith angle 20-30°); transferability to other sites, species, or illumination conditions requires further validation.
Why it matches plant phenotyping methodsLiDAR・マルチスペクトル融合と機械学習により、樹冠内fPARを3次元推定する高スループット植物フェノタイピング手法を開発・検証しており、方法が中心である。
abstractWe developed an unmanned aerial workflow that fuses centimeter resolution LiDAR point clouds with five band multispectral imagery to produce a three-dimensional voxelized canopy structure
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 15 Sept 2026
Hyperspectral imaging (HSI) has emerged as a powerful tool for precision agriculture, enabling the non-destructive monitoring of crop biochemical and physiological traits. However, HSI alone lacks structural context, which limits its ability to accurately capture complex canopy architectures and organ-level traits. Integrating HSI with depth-sensing modalities such as Light Detection and Ranging (LiDAR), Red, Green, Blue, and Depth (RGB-D) cameras, and computational reconstruction technique such as photogrammetry enables the generation of three-dimensional hyperspectral point clouds, combining spectral richness with geometric fidelity. This multi-modal fusion enhances crop trait estimation, including biomass, leaf chlorophyll content, canopy height, leaf area, and stress indicators, while improving the robustness of phenotyping under occlusions, shadows, and varying illumination. Dimensionality reduction, feature selection, and machine learning approaches, including deep learning and explainable AI, are useful for handling high-dimensional hyperspectral data and extracting actionable agronomic insights. Moreover, the integration of thermal, radar, and Global Navigation Satellite System (GNSS) data further expands the capabilities of multi-modal sensing, enabling continuous, all-weather crop monitoring and accurate spatial referencing. Despite these advances, most studies to date focus on controlled environments, highlighting the need for field-based validation to ensure the reliability and scalability of HSI-depth fusion techniques. This review consolidates current knowledge on multi-modal hyperspectral and 3D crop reconstruction, highlighting methods, applications, and challenges, and outlines future directions for implementing high-throughput, real-time phenotyping and precision agriculture solutions.
Why it matches plant phenotyping methods植物形質推定のためのハイパースペクトル・深度センシング融合と3D再構成を中心に扱うレビューであり、フェノタイピング手法の方法論的整理が主題。
abstractThis review consolidates current knowledge on multi-modal hyperspectral and 3D crop reconstruction, highlighting methods, applications, and challenges
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 15 Sept 2026
Accurate estimation of crop water status is essential for monitoring plant senescence and enabling intelligent agricultural management. This study proposes a pixel-aligned co-registration and DSM-grid fusion framework that integrates high-resolution point clouds, multispectral (MS) images, and thermal imagery acquired by Unmanned Aerial Vehicles (UAVs) to enable three-dimensional prediction and visualization of cotton canopy leaf water content (LWC) and equivalent water thickness (LEWT). To address the low spatial resolution of thermal imagery, a downsampling–upsampling simulation framework was developed to evaluate interpolation errors. This framework quantitatively compares three common interpolation methods—nearest neighbor, bilinear, and bicubic interpolation—using RMSE and PSNR metrics. Results show that bicubic interpolation performs best in preserving spatial details and minimizing errors, and is therefore adopted in the subsequent image fusion process. A 3D grid was constructed based on the digital surface model (DSM), enabling grid-cell (pixel-aligned) spectral and thermal features to be mapped onto point-cloud units. Vegetation and thermal indices extracted from the mapped features were used as input variables. Combined with recursive feature elimination (RFE) and random forest (RF) models, the prediction of LEWT and LWC achieved R² values of 0.792 and 0.752, and rRMSE values of 13.84% and 9.68%, respectively. These results significantly outperformed those of partial least squares regression (PLSR), support vector machine (SVM), and extreme learning machine (ELM) models. By integrating the predicted results with the point cloud data, a 3D representation of canopy water parameters was constructed, revealing a typical top-down gradient of water loss. The experiment also revealed that nitrogen treatment significantly influenced the vertical distribution of water content. High-nitrogen application delayed water loss in the middle and lower canopy layers, highlighting the coupled regulation between nitrogen and water. Parameter comparisons showed that LEWT exhibited higher sensitivity than LWC across both temporal and spatial scales, making it a more robust indicator for canopy water monitoring. Additionally, point clouds generated from Cross-circling oblique (CCO) photogrammetry outperformed UAV LiDAR systems in terms of point density, structural completeness, and image fusion potential. In summary, this study validated the feasibility and effectiveness of integrating point cloud, MS, and thermal imagery via the proposed pixel-aligned co-registration and DSM-grid fusion framework for 3D crop water monitoring. The proposed method provides a reliable technical foundation for drought detection, irrigation management, and yield prediction in precision agriculture.
Why it matches plant phenotyping methodsUAVマルチスペクトル・熱画像・点群を融合し、綿花の葉水分状態を3D推定・可視化する手法の開発と検証が研究の中心である。
abstractThis study proposes a pixel-aligned co-registration and DSM-grid fusion framework that integrates high-resolution point clouds, multispectral (MS) images, and thermal imagery acquired by Unmanned Aerial Vehicles (UAVs) to enable three-dimensional prediction and visualization of cotton canopy leaf water content (LWC) and equivalent water thickness (LEWT).
Hyperspectral imaging (HSI) has emerged as a powerful tool for precision agriculture, enabling the non-destructive monitoring of crop biochemical and physiological traits. However, HSI alone lacks structural context, which limits its ability to accurately capture complex canopy architectures and organ-level traits. Integrating HSI with depth-sensing modalities such as Light Detection and Ranging (LiDAR), Red, Green, Blue, and Depth (RGB-D) cameras, and computational reconstruction technique such as photogrammetry enables the generation of three-dimensional hyperspectral point clouds, combining spectral richness with geometric fidelity. This multi-modal fusion enhances crop trait estimation, including biomass, leaf chlorophyll content, canopy height, leaf area, and stress indicators, while improving the robustness of phenotyping under occlusions, shadows, and varying illumination. Dimensionality reduction, feature selection, and machine learning approaches, including deep learning and explainable AI, are useful for handling high-dimensional hyperspectral data and extracting actionable agronomic insights. Moreover, the integration of thermal, radar, and Global Navigation Satellite System (GNSS) data further expands the capabilities of multi-modal sensing, enabling continuous, all-weather crop monitoring and accurate spatial referencing. Despite these advances, most studies to date focus on controlled environments, highlighting the need for field-based validation to ensure the reliability and scalability of HSI-depth fusion techniques. This review consolidates current knowledge on multi-modal hyperspectral and 3D crop reconstruction, highlighting methods, applications, and challenges, and outlines future directions for implementing high-throughput, real-time phenotyping and precision agriculture solutions.
Why it matches plant phenotyping methods植物形質推定のためのハイパースペクトル・深度センシング・3D再構成手法を中心に扱うレビューであり、フェノタイピング手法レビューに該当する。
abstractThis review consolidates current knowledge on multi-modal hyperspectral and 3D crop reconstruction, highlighting methods, applications, and challenges
Vertical farming offers a promising solution to global food security and urbanization challenges, yet its widespread adoption is hindered by high costs, particularly for lighting. Addressing this requires enhancing light use efficiency (LUE) through intelligent control strategies. While numerous studies have investigated the effects of light intensity on lettuce growth, relatively few have explored the potential benefits of stage-specific light regulation. In this study, we first developed an automated 3D phenotyping pipeline based on multi-view reconstruction to quantify canopy morphology and light interception. Utilizing this quantitative framework, we conducted a dynamic light experiment with lettuce in a commercial plant factory to evaluate four dynamic light-intensity strategies. The proposed 3D phenotyping pipeline demonstrated promising performance for canopy information extraction, with RMSEs for plant height, canopy diameter, and projected leaf area of 0.79 cm, 1.05 cm, and 44.3 cm², respectively. The “high-low-high” dynamic lighting strategy, applying higher light intensity during the early and late growth stages and lower intensity during the mid-growth stage, successfully optimized canopy morphology for better light capture. This treatment significantly increased shoot fresh and dry weights by 28 % and 65 %, respectively, compared to constant lighting. Furthermore, it enhanced LUE based on incident and intercepted light integrals by 67 % and 19 %, while reducing electricity consumption per unit of fresh weight by 24 %. Nutritional quality analysis showed the treatment increased soluble sugars and starch contents. By integrating advanced 3D phenotyping with dynamic light intensity control, this study demonstrates a prototype for intelligent decision-making to enhance yield and energy use efficiency in practical vertical farming.
Why it matches plant phenotyping methods自動3Dフェノタイピングパイプラインを開発し、マルチビュー再構成で植物体形態と光遮断を定量化、精度評価も実施しており、フェノタイピング手法が研究の中心である。
abstractwe first developed an automated 3D phenotyping pipeline based on multi-view reconstruction to quantify canopy morphology and light interception.
CottonAerial / UAVLiDAR / point cloudMultispectral / hyperspectralThermalLeafPhysiological trait estimation2D/3D reconstructionImage / point-cloud registrationWater status / transpiration
Accurate estimation of crop water status is essential for monitoring plant senescence and enabling intelligent agricultural management. This study proposes a pixel-aligned co-registration and DSM-grid fusion framework that integrates high-resolution point clouds, multispectral (MS) images, and thermal imagery acquired by Unmanned Aerial Vehicles (UAVs) to enable three-dimensional prediction and visualization of cotton canopy leaf water content (LWC) and equivalent water thickness (LEWT). To address the low spatial resolution of thermal imagery, a downsampling–upsampling simulation framework was developed to evaluate interpolation errors. This framework quantitatively compares three common interpolation methods—nearest neighbor, bilinear, and bicubic interpolation—using RMSE and PSNR metrics. Results show that bicubic interpolation performs best in preserving spatial details and minimizing errors, and is therefore adopted in the subsequent image fusion process. A 3D grid was constructed based on the digital surface model (DSM), enabling grid-cell (pixel-aligned) spectral and thermal features to be mapped onto point-cloud units. Vegetation and thermal indices extracted from the mapped features were used as input variables. Combined with recursive feature elimination (RFE) and random forest (RF) models, the prediction of LEWT and LWC achieved R² values of 0.792 and 0.752, and rRMSE values of 13.84% and 9.68%, respectively. These results significantly outperformed those of partial least squares regression (PLSR), support vector machine (SVM), and extreme learning machine (ELM) models. By integrating the predicted results with the point cloud data, a 3D representation of canopy water parameters was constructed, revealing a typical top-down gradient of water loss. The experiment also revealed that nitrogen treatment significantly influenced the vertical distribution of water content. High-nitrogen application delayed water loss in the middle and lower canopy layers, highlighting the coupled regulation between nitrogen and water. Parameter comparisons showed that LEWT exhibited higher sensitivity than LWC across both temporal and spatial scales, making it a more robust indicator for canopy water monitoring. Additionally, point clouds generated from Cross-circling oblique (CCO) photogrammetry outperformed UAV LiDAR systems in terms of point density, structural completeness, and image fusion potential. In summary, this study validated the feasibility and effectiveness of integrating point cloud, MS, and thermal imagery via the proposed pixel-aligned co-registration and DSM-grid fusion framework for 3D crop water monitoring. The proposed method provides a reliable technical foundation for drought detection, irrigation management, and yield prediction in precision agriculture.
Why it matches plant phenotyping methodsUAVマルチスペクトル・熱画像・点群を統合し、綿花キャノピーの水分形質を3D推定・可視化する手法の開発と検証が中心である。
abstractThis study proposes a pixel-aligned co-registration and DSM-grid fusion framework that integrates high-resolution point clouds, multispectral (MS) images, and thermal imagery acquired by Unmanned Aerial Vehicles (UAVs) to enable three-dimensional prediction and visualization of cotton canopy leaf water content (LWC) and equivalent water thickness (LEWT).
AppleField / plotLiDAR / point cloudRGB-D / ToFFruitObject detection2D/3D reconstructionSegmentation
One of the key challenges in orchard robots is accurately localizing occluded fruits in complex environments, especially when the fruit targets are split into multiple isolated regions within images. Traditional single-task network models exhibit limited capability in discerning fragmented targets that belong to the same fruit but are segmented into multiple spatially isolated regions within images. In addition, fruit localization largely relies on high-cost sensors or additional 3-D localization algorithms. To address this issue, we propose a fruit detection and centroid localization method based on a Multi-Task Wavelet-Enhanced YOLO (MT-WavYOLO) to enhance the success rate of robotic operations on occluded fruit targets. Initially, a lightweight semantic segmentation branch was integrated into the YOLOv8 backbone network to precisely segment exposed fruits, while retaining the original object detection branch to fully identify occluded fruits. To address the diminished sensitivity of conventional models to geometric profiles of heavily occluded fruits, a novel feature fusion module, C2f_WTConv, was designed by incorporating wavelet transform convolution, leveraging the multi-frequency robustness of wavelet representations to enhance the model’s feature extraction capabilities under complex orchard occlusions. Subsequently, a 3D frustum-based point cloud processing method was proposed, combining the detection results from MT-WavYOLO with the semantic segmentation masks to accurately localize occluded fruits. MT-WavYOLO demonstrated a 2%, 1.5%, and 2.2% improvement in Precision, Recall, and mAP50, respectively, on our custom-built dataset compared to the latest YOLOv10s model. Semantic segmentation performance, measured by Intersection over Union (IoU) and Accuracy, was improved by 5.2% and 3.8%, respectively, over the state-of-the-art Deeplabv3+ network. Compared to the adapted multi-task network YOLOP, MT-WavYOLO achieved a 3.4% increase in mAP50 and a 2.7% improvement in IoU. In addition, MT-WavYOLO has a compact footprint of 10.2 M parameters and achieves approximately 27 FPS in real-time inference, thereby meeting the requirements of robotic harvesting operations. The proposed localization method was evaluated through 600 fruit localization tests using six different RGB-D cameras in an orchard environment. The average experimental results demonstrated that the centroid localization and radius estimation errors were reduced by 42.5%, 73.7%, 16.17%, and 11.25%, respectively, compared to traditional 3D bounding box methods and our previous approaches. These results indicate that the MT-WavYOLO combined with the frustum-based method significantly enhances the accuracy of apple localization under complex orchard conditions using consumer-grade sensors, providing a strong practical foundation for non-destructive robotic harvesting.
Why it matches plant phenotyping methods果実の検出・3D重心定位という植物器官の形態的状態を、画像分割・深層学習・点群処理で推定する手法を開発し、データセットおよび複数カメラで性能評価しているため、方法が中心的である。
abstractwe propose a fruit detection and centroid localization method based on a Multi-Task Wavelet-Enhanced YOLO (MT-WavYOLO)
With rapid advancements in breeding technologies, phenomics, and artificial intelligence, crop breeding is progressively entering an era of greater precision and efficiency. In this context, three-dimensional (3D) phenotyping techniques-leveraging multidimensional spatial resolution capabilities-have overcome the limitations of two-dimensional (2D) phenotyping in breeding analyses, enabling precise characterization of crop spatial interactions, spatial distribution of plant architecture, and complex 3D structural traits. Recent breakthroughs in computer technology for 3D reconstruction and 3D segmentation have provided robust technical support for crop 3D phenotypic analysis. Furthermore, effective integration of extracted 3D phenotypic data with genotypic data serves as a powerful tool for future research on crop gene function and genomics-assisted breeding. This review systematically examines major advances in 3D phenotyping techniques and their representative applications, with particular emphasis on innovations in 3D phenotyping and analytical methodologies. In parallel, we describe the latest interdisciplinary advances in 3D phenotyping within crop functional genomics research and genomics-assisted breeding. We objectively evaluate the advantages and limitations of 3D phenotyping compared with 2D approaches to assist breeders in selecting appropriate technologies. Finally, we propose future perspectives to promote deeper integration of phenomics and breeding technologies. Despite existing conceptual and technical challenges, it is foreseeable that cross-disciplinary integration of phenomics and genomics will offer promising prospects for crop breeding.
Why it matches plant phenotyping methods3D植物フェノタイピング技術と解析手法の進展を体系的にレビューしており、植物形態・構造形質の取得方法が中心である。
abstractThis review systematically examines major advances in 3D phenotyping techniques and their representative applications, with particular emphasis on innovations in 3D phenotyping and analytical methodologies.
Abstract Visible and near‐infrared (VIS‐NIR) spectral imaging is vital for agriculture, food safety, and biomedical applications. Conventional spectral imaging relies on precision optical components with limited environmental adaptability, whereas computational spectral imaging employs advanced processing algorithms to simplify hardware architecture while improving system flexibility. However, developing compact, high‐performance, low‐cost snapshot systems remains challenging, especially in mask fabrication and real‐time imaging algorithms. In this work, a low‐cost snapshot VIS‐NIR spectral imager based on an on‐chip all‐dielectric weak‐confined Fabry–Pérot filter array and a deep learning‐based reconstruction approach is presented. Using single‐exposure patterning and the Cumulative Attention Transformer with Random Mask (CATRM) algorithm, the manufacturing process is streamlined while the reconstruction accuracy is enhanced. The system achieves high spatial resolution (100.17 lp mm −1 ) and maintains isotropic imaging fidelity, while reconstructing full‐field (2048 × 2048 × 61) hyperspectral data at 12.35 fps. The spectral accuracy of the imager is confirmed by spectral imaging of two traditional Chinese medicinal herbs, Astragalus membranaceus and Coptis chinensis , which shows over 99.1% cosine similarity between the system and a commercial scanning hyperspectral imager. Moreover, the broad application prospects are validated by non‐destructive sugar content prediction in mangoes. The integrated imager design enables compact, cost‐effective VIS‐NIR spectral imaging for diverse application scenarios.
Why it matches plant phenotyping methodsVIS-NIRスナップショット型ハイパースペクトル撮像装置と再構成手法の開発・性能検証が中心で、植物試料のスペクトル測定およびマンゴー糖度という植物器官形質の非破壊推定に応用している。
abstractThe spectral accuracy of the imager is confirmed by spectral imaging of two traditional Chinese medicinal herbs, Astragalus membranaceus and Coptis chinensis
Field / plotNeRF / 3D Gaussian SplattingPhotogrammetry / SfM / MVSLiDAR / point cloudLeafWhole plant / canopy / plot / field2D/3D reconstructionImage / point-cloud registrationGrowth / time-series analysisTracking
Land management and stewardship teams continue to lack the tools to capture 3D spatiotemporal insights of the ecosystems they oversee. For wildfire management at the wildland-urban interface, teams face challenges in capturing vegetation growth over time after a fuel reduction program and connecting seasonal changes to the vegetation distribution across the treated area. Current approaches rely on triangle meshes or point clouds generated from photogrammetry or LiDAR surveys on drones or hiked traverses. However, the difficulties in optimizing these meshes lead to large triangles that inadequately approximate the bulk vegetation shape, and the point cloud data is often too sparse for local plant-scale understanding. To address this gap, we extend recent machine learning-based computer graphics techniques in 3D Gaussian Splatting (3DGS) to reconstruct scenes at the wildland-urban interface from handheld imagery with sufficient detail to identify species, capture individual leaves, recover plant stature, and disambiguate overhanging plant individuals. We develop a new method to match the 3DGS reconstruction of these scenes across months, associating plant growth across seasons interactively in 3D. To achieve the centimeter-level matching, we adapt the Umeyama algorithm and the iterative closest point algorithm from point cloud maps to the 3DGS scene, leveraging the probabilistic interpretation of the 3D Gaussian data structure and robustly handling visual and geometric changes associated with vegetation phenology over time. We have applied our method to recent pile burns at Stanford’s Jasper Ridge ’Ootchamin ’Ooyakma Biological Preserve at monthly intervals. We demonstrate differences in ecological response where some piles featured the unexpected return of a rare and threatened bushmallow, and others remained more barren. This pile burn microcosm implicates the need for plant-level 3D spatiotemporal models to understand ecosystem recovery to fire mitigation practices. Please visit the project page for the spatiotemporal alignment video and more information: https://danineamati.github.io/burn-ecorecovery.github.io/
Why it matches plant phenotyping methods植物の3D画像再構成と時系列位置合わせを開発し、植物の個体・葉・草丈・成長を抽出することが中心であるため、植物フェノタイピング手法として含める。
abstractwe extend recent machine learning-based computer graphics techniques in 3D Gaussian Splatting (3DGS) to reconstruct scenes at the wildland-urban interface from handheld imagery with sufficient detail to identify species, capture individual leaves, recover plant stature, and disambiguate overhanging plant individuals.
Accurate measurement of plant height in leafy vegetables is challenging due to their short stature, high planting density, and severe canopy occlusion during later growth stages. These factors often limit the reliability of single-plant monitoring across the full growth cycle in open-field environments. To address this, we propose a multi-temporal point cloud alignment method for accurate plant height measurement, focusing on Choy Sum (Brassica rapa var. parachinensis). The method estimates plant height by calculating the vertical distance between the canopy and the ground. Multi-temporal point cloud maps are reconstructed using an enhanced Oriented FAST and Rotated BRIEF–Simultaneous Localization and Mapping (ORB-SLAM3) algorithm. A fixed checkerboard calibration board, leveled using a spirit level, ensures proper vertical alignment of the Z-axis and unifies coordinate systems across growth stages. Ground and plant points are separated using the Excess Green (ExG) index. During early growth stages, when the soil is minimally occluded, ground point clouds are extracted and used to construct a high-precision reference ground model through Cloth Simulation Filtering (CSF) and Kriging interpolation, compensating for canopy occlusion and noise. In later growth stages, plant point cloud data are spatially aligned with this reconstructed ground surface. Individual plants are identified using an improved Euclidean clustering algorithm, and consistent measurement regions are defined. Within each region, a ground plane is fitted using the Random Sample Consensus (RANSAC) algorithm to ensure alignment with the X–Y plane. Plant height is then determined by the elevation difference between the canopy and the interpolated ground surface. Experimental results show mean absolute errors (MAEs) of 7.19 mm and 18.45 mm for early and late growth stages, respectively, with coefficients of determination (R2) exceeding 0.85. These findings demonstrate that the proposed method provides reliable and continuous plant height monitoring across the full growth cycle, offering a robust solution for high-throughput phenotyping of leafy vegetables in field environments.
Why it matches plant phenotyping methods葉菜類の草丈を取得するための点群位置合わせ・地面復元・個体抽出・高さ推定手法を開発し、誤差と決定係数で検証している。植物フェノタイピング手法が研究の中心である。
abstractTo address this, we propose a multi-temporal point cloud alignment method for accurate plant height measurement
Individual tree structure plays a key role in forest monitoring, biomass estimation, and ecological assessment. However, ground-based remote sensing methods such as terrestrial and mobile laser scanning frequently produce incomplete point clouds due to occlusion, particularly in the upper canopy. This limits the accuracy of derived structural metrics such as tree height or crown volume. In this study, we present a novel deep learning-based method to reconstruct the outer crown shape of coniferous trees from incomplete point clouds. Instead of completing the full tree structure, we focus on predicting the alpha-shape of the crown, enabling a more efficient and generalizable approach for structural reconstruction. We train a geometry-aware transformer model (AdaPoinTr) on synthetically generated partial tree crowns and evaluate its performance across three independent datasets encompassing different forest types and acquisition conditions. The model consistently improved crown shape similarity metrics and reduced height estimation errors compared to using partial data alone (reduced bias from -11% to -3.5%). Our results demonstrate that this shape-based strategy enables the extraction of key tree-level parameters from incomplete data, offering a practical solution for gaining improved 3D forest structural information from cost-sensitive or logistically constrained forest monitoring acquisitions.
Why it matches plant phenotyping methods不完全な点群から樹冠形状を再構成し、樹高などの樹木形質を推定する深層学習手法の開発と複数データセットでの評価が中心であるため、植物フェノタイピング手法に該当する。
abstractwe present a novel deep learning-based method to reconstruct the outer crown shape of coniferous trees from incomplete point clouds.
Adlay ( Coix lacryma-jobi L.) stands out as a vital health-promoting cereal due to its dual nutritional and medicinal properties; however, it remains significantly underdeveloped compared to major crops. The lack of mechanistic understanding of its caryopsis development and trait formation severely constrains targeted genetic improvement. While transformative technologies, specifically micro-computed tomography (micro-CT) imaging combined with AI-assisted analysis (e.g., Segment Anything Model (SAM)) and multi-omics approaches, have been successfully applied to unravel the structural and physiological complexities of model cereals, their systematic adoption in adlay research remains fragmented. Going beyond a traditional synthesis of these methodologies, this article proposes a novel, multidimensional framework specifically designed for adlay. This forward-looking strategy integrates high-resolution 3D phenotyping with spatial multi-omics data to bridge the gap between macroscopic caryopsis architecture and microscopic metabolic accumulation. By offering a precise digital solution to elucidate adlay's unique developmental mechanisms, the proposed framework aims to accelerate precision breeding and advance the scientific modernization of this promising underutilized crop.
Why it matches plant phenotyping methods穀粒の3DフェノタイピングとAI画像解析を中核に据えた、方法論的な枠組みを提案するレビュー／展望論文である。
abstractThis forward-looking strategy integrates high-resolution 3D phenotyping with spatial multi-omics data
Strawberries are among the most economically significant fruits in the United States, generating over $2 billion in annual farm-gate sales and accounting for approximately 13% of the total fruit production value. Plant phenotyping plays a vital role in selecting superior cultivars by characterizing plant traits such as morphology, canopy structure, and growth dynamics. However, traditional plant phenotyping methods are time-consuming, labor-intensive, and often destructive. Recently, neural rendering techniques, notably Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), have emerged as powerful frameworks for high-fidelity 3D reconstruction. By capturing a sequence of multi-view images or videos around a target plant, these methods enable non-destructive reconstruction of complex plant architectures. Despite their promise, most current applications of 3DGS in agricultural domains reconstruct the entire scene, including background elements, which introduces noise, increases computational costs, and complicates downstream trait analysis. To address this limitation, we propose a novel object-centric 3D reconstruction framework incorporating a preprocessing pipeline that leverages the Segment Anything Model v2 (SAM-2) and alpha channel background masking to achieve clean strawberry plant reconstructions. This approach produces more accurate geometric representations while substantially reducing computational time. With a background-free reconstruction, our algorithm can automatically estimate important plant traits, such as plant height and canopy width, using DBSCAN clustering and Principal Component Analysis (PCA). Experimental results show that our method outperforms conventional pipelines in both accuracy and efficiency, offering a scalable and non-destructive solution for strawberry plant phenotyping.
Why it matches plant phenotyping methods植物の3D再構成、背景除去、クラスタリングを統合し、草丈やキャノピー幅を自動推定するフェノタイピング手法の開発が中心である。
abstractwe propose a novel object-centric 3D reconstruction framework incorporating a preprocessing pipeline that leverages the Segment Anything Model v2 (SAM-2) and alpha channel background masking to achieve clean strawberry plant reconstructions.
Aerial remote sensing efficiently surveys large areas, but accurate direct object-level measurement remains difficult in complex natural scenes. Advancements in 3D computer vision, particularly radiance field representations such as NeRF and 3D Gaussian splatting, can improve reconstruction fidelity from posed imagery. Nevertheless, direct aerial measurement of important attributes like tree diameter at breast height (DBH) remains challenging. Trunks in aerial forest scans are distant and sparsely observed in image views; at typical operating altitudes, stems may span only a few pixels. With these constraints, conventional reconstruction methods have inaccurate breast-height trunk geometry. TreeDGS is an aerial image reconstruction method that uses 3D Gaussian splatting as a continuous scene representation for trunk measurement. After SfM--MVS initialization and Gaussian optimization, we extract a dense point set from the Gaussian field using RaDe-GS's depth-aware cumulative-opacity integration and associate each sample with a multi-view opacity reliability score. Then, we isolate trunk points and estimate DBH using opacity-weighted solid-circle fitting. Evaluated on 10 plots with field-measured DBH, TreeDGS reaches 4.79 cm RMSE (about 2.6 pixels at this GSD) and outperforms a LiDAR baseline (7.66 cm RMSE). This shows that TreeDGS can enable accurate, low-cost aerial DBH measurement .
Why it matches plant phenotyping methods樹木のDBHという明示的な植物形質を、航空画像と3D Gaussian splattingから推定する新規手法を開発し、実測値およびLiDARと比較検証しているため、方法中心の植物フェノタイピング研究として含める。
abstractTreeDGS is an aerial image reconstruction method that uses 3D Gaussian splatting as a continuous scene representation for trunk measurement.
Abstract The northern hardwood forests of Eastern Canada, particularly stands dominated by sugar maple (Acer saccharum Marsh.), are facing ongoing decline due to historical and contemporary environmental pressures. Traditional ground-based crown assessments for tree health are essential for management, but can be subjective, costly, and limited by their viewing perspective. While conventional remote sensing methods can effectively capture tree crown structure from above, and terrestrial approaches can capture the stems and crowns from beneath, occlusion by the dense crowns of mature sugar maples makes capturing reliable estimates challenging. We examine the potential of intra-canopy aerial drone-based photogrammetry, involving flights beneath, within, and above tree crowns, to generate detailed 3D point clouds of 29 sugar maple trees in Quebec, Canada. From these point clouds, we derived estimates of key structural attributes including diameter at breast height (DBH), tree height, and crown base height (CBH). We used ray-marching to quantify crown transparency across 162 viewing angles, forming a sphere around the crown, and compared predictions to ground-based visual estimates and health categories. Photogrammetric estimates had significant correlations with ground-measured attributes, including DBH (r = 0.82), tree height (r = 0.55), and CBH (r = 0.73 and 0.78 across two distinct definitions). Modeled crown transparency correlation was also significant when compared to ground-based visual assessments (ρ = 0.54), suggesting that intra-canopy drone-based photogrammetry can offer rapid and objective assessment of crown condition.
Why it matches plant phenotyping methods樹冠内ドローン画像から3D点群を生成し、樹高・DBH・樹冠基部高・透明度などの樹木形質を推定して地上測定と検証しており、フェノタイピング手法が中心である。
abstractWe examine the potential of intra-canopy aerial drone-based photogrammetry, involving flights beneath, within, and above tree crowns, to generate detailed 3D point clouds of 29 sugar maple trees in Quebec, Canada.
Achieving an optimal plant architecture is a key objective in cotton breeding for enhancing yield potential, and accurate quantification of the fruit branch angle (FBA) is essential for understanding genotype–phenotype relationships and advancing ideotype breeding. However, in-field FBA measurement remains technically challenging due to severe occlusion, variable illumination, and background interference. To overcome these limitations, we propose a streamlined 3D phenotyping framework that integrates 3D Gaussian Splatting (3DGS) with a novel structural segmentation model, the Linear Point Cloud Reverse Model (LPCRM). The framework decomposes reconstructed cotton point clouds into linear micro-elements using RANSAC, followed by geometric clustering via K-means to identify and separate the main stem and fruit branches. This process operates without topological priors or large annotated datasets. Model fidelity assessment shows that 80% of point pairs between the LPCRM and the original 3DGS reconstruction exhibit Euclidean distances ≤ 0.5 cm. Phenotypic validation using 268 fruit branches from 25 cultivars demonstrates high measurement accuracy, achieving an R² of 0.874 and an RMSE of 4.01° for FBA extraction. Plant height estimation also shows strong agreement with manual measurements (R² = 0.915, RMSE = 3.858). Overall, this study presents a lightweight and robust solution for extracting 3D structural traits of field-grown cotton. The proposed framework reduces data dependency, adapts well to complex field conditions, and offers an efficient approach for high-throughput phenotyping and cotton ideotype breeding.
Why it matches plant phenotyping methods綿花の果枝角度などの3D植物形質を抽出する画像ベース表現型解析フレームワークを開発し、複数品種・枝で精度検証しており、方法開発と技術検証が研究の中心である。
abstractwe propose a streamlined 3D phenotyping framework that integrates 3D Gaussian Splatting (3DGS) with a novel structural segmentation model, the Linear Point Cloud Reverse Model (LPCRM).
Background Rice plant architecture underpins yield and grain quality, yet two obstacles impede accurate field characterization in dense paddies. First, single-plant reconstruction is constrained by severe inter-plant occlusion, cluttered backgrounds, and limited viewpoints. These factors obscure culms, leaves, basal tillers, and the true physical scale of the plant. Active ranging devices are cumbersome in outdoor plots and can lose accuracy, whereas conventional passive photogrammetry performs poorly under such conditions. Second, delineating panicles within a 3D rice model is intrinsically difficult. Panicles are slender, highly branched, and visually similar to surrounding foliage, often interwoven and partially hidden. These factors result in fragmented boundaries and missing details. Direct point-cloud segmentation struggles with such discontinuous geometry and requires costly 3D annotation, whereas generic image segmentation models trained on natural scenes transfer poorly to paddy imagery. These challenges motivate a field-ready workflow that both reconstructs whole plants at high resolution in dense plantings and reliably segments panicles to enable trait extraction. Results A low-cost, in-field, multi-view pipeline for whole-plant three-dimensional reconstruction, termed One Stop 3D Target Reconstruction And segmentation (OSTRA), operates on color images with a reference-board setup. The pipeline builds detailed three-dimensional models of individual rice plants and automatically segments key organs (in this case, panicles), despite dense surrounding vegetation. When applied to 231 diverse rice landraces grown in a crowded field setting, the method produced high-fidelity plant models with clearly delineated panicle structures. From these reconstructions, three architectural traits were derived: plant height, leaf area, and panicle length. Genome-wide association analysis of the measured traits identified strong genotype-phenotype associations tagging known candidate genes. Natural variants at D2 and RFL/APO2 were associated with plant height variation, variants at FLW7 were linked to differences in leaf area, and allelic variation at AAI1 corresponded to panicle length variation. These loci are established regulators of plant growth and morphology, indicating that this three-dimensional phenotyping pipeline attains accuracy sufficient to rediscover meaningful genetic signals. Conclusions This study provides a practical tool for precise rice phenotyping even under dense field planting conditions, overcoming occlusion and structural complexity. By enabling non-destructive, field-based measurement of complete plant architecture and linking these phenotypes to specific genes, the pipeline bridges field phenomics and genomics. The integrated reconstruction and analysis framework advances the study of rice architecture and offers a general route to connect complex traits with their genetic determinants.
Why it matches plant phenotyping methods密植圃場でのイネ全体3D再構築、器官分割、形質抽出を中核とする画像ベース表現型解析手法の開発・実証であり、明確に収載対象。
abstractA low-cost, in-field, multi-view pipeline for whole-plant three-dimensional reconstruction, termed One Stop 3D Target Reconstruction And segmentation (OSTRA), operates on color images with a reference-board setup.
Reproduction assets foundThe paper explicitly states that the 3D rice plant models (231 landraces) are deposited on Zenodo and the OSTRA source code is publicly available on GitHub. Both are paper-specific, public, and actionable.Code · publicThe source code of OSTRA is available on GitHub at [http://github.com/ganlab/ostra] (http:/github.com/ganlab/ostra).Open asset ↗github · ganlab/ostralines:217-246Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Published18 Jan 2026Forest - Rivista di Selvicoltura ed Ecologia ForestaleCited by 0 · OpenAlex ↗
Monumental trees in Italy represent a natural and cultural heritage of great value, whose conservation is essential. This study highlights the potential of LiDAR (Light Detection and Ranging)-based technologies, such as terrestrial laser scanning (TLS) and drones, for three-dimensional reconstruction of these specimens within virtual environments. Villa del Colle del Cardinale, a monumental complex managed by the National Museums of Perugia - Regional Directorate of National Museums of Umbria under the Ministry of Culture, located in the province of Perugia, was selected as a case study for developing a 3D digital archive of its monumental trees. This archive serves not only as a resource that can be consulted over time - useful for monitoring, scientific dissemination, and cultural promotion - but also as a replicable example at the national level for the protection and conservation of monumental tree heritage.
Why it matches plant phenotyping methodsLiDARとドローンによる樹木の三次元再構築とデジタルアーカイブ開発が中心で、樹木の形態・構造を記録し長期監視に利用する方法論的研究である。
abstractThis study highlights the potential of LiDAR (Light Detection and Ranging)-based technologies, such as terrestrial laser scanning (TLS) and drones, for three-dimensional reconstruction of these specimens within virtual environments.
Semantic reconstruction of agricultural scenes plays a vital role in tasks such as phenotyping and yield estimation. However, traditional approaches based on manual scanning or fixed camera setups remain a major bottleneck, while active-mapping methods based solely on occupancy grids are too coarse for accurate trait estimation. To address this gap, we propose an active 3D reconstruction framework for horticultural environments using a mobile manipulator. The system integrates OctoMap with 3D Gaussian Splatting to enable accurate and efficient target-aware mapping. A low-resolution OctoMap provides probabilistic occupancy information for informative viewpoint selection and collision-free planning, while 3D Gaussian Splatting leverages geometric, photometric, and semantic information to optimize 3D Gaussians for high-fidelity scene reconstruction. We further introduce a robust mapping strategy that mitigates semantic segmentation and depth noise, together with a background pruning method that reduces memory and computational cost. We validate our framework across simulated, laboratory, and real greenhouse scenes, showing consistent improvements across three state-of-the-art Gaussian Splatting backbones. In simulation, where ground-truth geometry is available, our approach outperforms occupancy-based mapping in both reconstruction accuracy and runtime efficiency: compared with a 0.01m-resolution OctoMap, it doubles the fruit-level F1 score under noisy conditions while achieving up to a threefold reduction in runtime. Beyond simulation, novel-view synthesis quality also improves consistently in laboratory and real greenhouse environments, with PSNR and mIoU improving by up to 1.5 dB and 18%, respectively. Finally, the reconstructed semantic maps enable fruit counting and volume estimation with accuracies approaching 80%.
Why it matches plant phenotyping methods園芸ロボット向けの3D再構成・能動マッピング手法を開発し、果実の計数・体積推定という植物形質の取得に適用・検証しているため、フェノタイピング手法が中心的です。
titleOctoSplat: Hybrid OctoMap-Gaussian Splatting for Active Semantic Mapping and Phenotyping with Horticultural Robots
Reproduction assets foundThe paper's supplementary material is hosted on the authors' public project page (jrcuaranv.github.io/octosplat), and the authors state that all code and data are publicly available. The SimSense repository is a third-party depth-sensor simulator tool, not a paper-specific asset.Code · publicAll code and data are publicly available to facilitate reproducibility.Open asset ↗lines:59-163Code / dataset availability confirmedOpenAlex · Crossref · checked 13 Sept 2026
Banana (Musa spp.) fruit morphology is a key determinant of yield and quality, yet modeling its 3D structural dynamics across genotypes remains difficult. To address this challenge, we developed a generic, biomass-driven 3D structural model for banana fruit fingers that quantitatively links growth and morphology. Field experiments were conducted over two growing seasons in Hainan, China, using three representative genotypes. Morphological traits, including outer and inner arc length, circumference, and pedicel length, along with dry (Wd) and fresh weight (Wf), were measured every 10 days after flowering until 110 days. Quantitative relationships between morphological traits and Wf, as well as between Wd and Wf, were fitted using linear or Gompertz functions with genotype-specific parameters. Based on these functions, a parameterized 3D reconstruction method was implemented in Python, combining biomass-driven growth equations, curvature geometry, and cross-sectional interpolation to simulate the fruit’s bending, tapering, and volumetric development. The resulting dynamic 3D models accurately reproduced genotype-specific differences in curvature, length, and shape with average fitting R2 > 0.95. The proposed biomass-driven 3D structural model provides a methodological framework for integrating banana fruit morphology into functional–structural plant models.
Why it matches plant phenotyping methodsバナナ果実の形態形質を推定・再現するバイオマス駆動型3D構造モデルを開発し、遺伝子型間の形状を検証しており、フェノタイピング手法が中心である。
abstractwe developed a generic, biomass-driven 3D structural model for banana fruit fingers that quantitatively links growth and morphology.
Reproduction assets foundThe paper explicitly states that the source code of the Banana Morphology Simulation System and the datasets are publicly available on GitHub at the authors' URL, which matches an allowed URL. This covers the paper's phenotyping datasets and analysis/3D modeling code.Code · publicData analysis was performed using a custom-developed software platform, the Banana
Morphology Simulation System. The source code and datasets are publicly available
on GitHub (https://github.com/Interstingsun/SimBanana, accessed on 4 January 2026).Open asset ↗Interstingsun/SimBananapdf-page:5 lines:1-24Dataset · publicThe source code and datasets are publicly available
on GitHub (https://github.com/Interstingsun/SimBanana, accessed on 4 January 2026).Open asset ↗Interstingsun/SimBananapdf-page:5 lines:1-24Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 13 Sept 2026
Accurate assessment of rice resistance to Sogatella furcifera (Horváth) is essential for breeding insect-resistant cultivars. Traditional assessment methods rely on manual scoring of damage severity, which is subjective and inefficient. To overcome these limitations, this study proposes an automated resistance evaluation approach based on multi-view 3D reconstruction and deep learning–based point cloud segmentation. Multi-view videos of rice materials with different resistance levels were collected over time and processed using Structure from Motion (SfM) and Multi-View Stereo (MVS) to reconstruct high-quality 3D point clouds. A well-annotated “3D Rice WBPH Damage” dataset comprising 174 samples (15 rice materials, three replicates each, 45 pots) was established, where each sample corresponds to a reconstructed 3D point cloud from a video sequence. A comparative study of various point cloud semantic segmentation models, including PointNet, PointNet++, ShellNet, and PointCNN, revealed that the PointNet++ (MSG) model, which employs a Multi-Scale Grouping strategy, demonstrated the best performance in segmenting complex damage symptoms. To further accurately quantify the severity of damage, an adaptive point cloud dimensionality reduction method was proposed, which effectively mitigates the interference of leaf shrinkage on damage assessment. Experimental results demonstrated a strong correlation (R2 = 0.95) between automated and manual evaluations, achieving accuracies of 86.67% and 93.33% at the sample and material levels, respectively. This work provides an objective, efficient, and scalable solution for evaluating rice resistance to S. furcifera, offering promising applications in crop resistance breeding.
Why it matches plant phenotyping methods3D画像再構成と深層学習によってイネの害虫被害症状・被害重症度を定量化する手法を開発・検証しており、植物表現型取得が研究の中心である。
abstractthis study proposes an automated resistance evaluation approach based on multi-view 3D reconstruction and deep learning–based point cloud segmentation.
Forest surveying and inspection face significant challenges due to unstructured environments, variable terrain conditions, and the high costs of manual data collection. Although mobile robotics and artificial intelligence offer promising solutions, reliable autonomous navigation in forest, terrain-aware path planning, and tree parameter estimation remain open challenges. In this paper, we present the results of the AI4FOREST project, which addresses these issues through three main contributions. First, we develop an autonomous mobile robot, integrating SLAM-based navigation, 3D point cloud reconstruction, and a vision-based deep learning architecture to enable tree detection and diameter estimation. This system demonstrates the feasibility of generating a digital twin of forest while operating autonomously. Second, to overcome the limitations of classical navigation approaches in heterogeneous natural terrains, we introduce a machine learning-based surrogate model of wheel–soil interaction, trained on a large synthetic dataset derived from classical terramechanics. Compared to purely geometric planners, the proposed model enables realistic dynamics simulation and improves navigation robustness by accounting for terrain–vehicle interactions. Finally, we investigate the impact of point cloud density on the accuracy of forest parameter estimation, identifying the minimum sampling requirements needed to extract tree diameters and heights. This analysis provides support to balance sensor performance, robot speed, and operational costs. Overall, the AI4FOREST project advances the state of the art in autonomous forest monitoring by jointly addressing SLAM-based mapping, terrain-aware navigation, and tree parameter estimation.
Why it matches plant phenotyping methods自律ロボット、3D点群、深層学習を用いて樹木の直径・高さを推定する手法を開発し、点群密度による推定精度も評価しており、植物形質取得が中心である。
abstractwe develop an autonomous mobile robot, integrating SLAM-based navigation, 3D point cloud reconstruction, and a vision-based deep learning architecture to enable tree detection and diameter estimation.
Reproduction assets foundThe paper's point-cloud-density/tree-parameter analysis (Section 2.3) is based on two open-source MLS forest point cloud datasets (Forest 1 from southern Finland, and Forest 2 openly accessible via the 3DFin platform), which are public, paper-specific phenotype/trait data assets. However, the supplied blocks do not包含 aDataset · publicTwo different open-source datasets acquired using a Mobile Laser Scanning (MLS)
system (i.e., GeoSLAM Zeb-Horizon) and available online were considered in this study.Open asset ↗pdf-page:12 lines:1-60Code / dataset availability confirmedCrossref · checked 14 Sept 2026
MaizeRoot2D/3D reconstructionSegmentationRoot system architecture
Abstract Background and aims Root system architecture (RSA) shapes biogeochemical concentration patterns in the rhizosphere. Root-soil studies are often conducted on plants cultivated in rectangular rhizotrons, including when using 2D hydrochemical analysis methods. However, roots naturally expand in three dimensions, with the rhizosphere extending accordingly. Three-dimensional neutron imaging can enhance interpretation of such studies, yet imaging flat, slab-shaped rhizotrons is technically challenging. This study presents a methodological comparison between conventional neutron tomography (NT) and neutron computed laminography (NCL) to assess whether NT under high-flux conditions can achieve image quality sufficient for 3D root segmentation, comparable to NCL, without requiring tilting of the rotation axis. Methods NT and NCL were applied to maize plants grown in rectangular rhizotrons. Imaging artifacts and their impact on root segmentation were assessed for two plants representing low and high soil moisture conditions suitable for neutron imaging. Results Both methods produced 3D tomograms of comparable quality across the tested moisture range, enabling effective segmentation of primary and seminal roots. Lateral root detection was more challenging and depended on soil moisture. NCL captured a greater number of horizontally oriented lateral roots while NT was more effective in resolving vertically oriented roots. Conclusions NCL is not required to resolve 3D RSA of maize plants in flat rhizotrons. Under high-flux neutron beam conditions, NT is preferable as it simplifies sample handling, reduces plant stress, avoids soil water redistribution and enables direct integration with timeseries of 2D chemical and neutron radiographic imaging.
Why it matches plant phenotyping methods3D中性子画像法を用いた根系構造の抽出を中心に、NTとNCLを比較検証しており、植物表現型取得手法が研究の主題である。
abstractThis study presents a methodological comparison between conventional neutron tomography (NT) and neutron computed laminography (NCL) to assess whether NT under high-flux conditions can achieve image quality sufficient for 3D root segmentation, comparable to NCL
Reproduction assets foundThe paper's neutron imaging datasets (NT and NCL scans of maize in slab rhizotrons) are stated to be publicly available on the ILL Data Portal under DOI 10.5291/ILL-DATA.UGA-111. No author analysis code or trained models are explicitly deposited.Dataset · publicacknowledge funding of the
research presented here by the German Research Foundation
(DFG project numbers 396368046 and 516672636).
Data availability The datasets used in this study were gener-
ated as part of a measurement campaign on the neutron imag-
ing instrument NeXT at the ILL and are available on the ILL
Data Portal at https://doi.org/10.5291/ILL-DATA.UGA-111.Declarations
Competing interests The authors have no relevant financial
or non-financial interests to disclose.
Open Access This article is licensed under a Creative Com-
mons Attribution 4.0 International License, which permits
use, sharing, adaptation, distribution and reproduction in any
medium or format, as long as you give Open asset ↗10.5291/ILL-DATA.UGA-111pdf-raw-page:16 lines:1-92Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Generating accurate and visually realistic 3D models of plants from single-view images is crucial yet remains challenging due to plants' intricate geometry and frequent occlusions. This capability matters because it supplements current plant datasets and enables non-destructive, high-throughput phenotyping for crop breeding and precision agriculture. More broadly, 3D reconstruction is particularly important because plant morphology is inherently three-dimensional, while 2D representations miss occluded leaves, branching geometry, and volumetric traits. However, plants present unique challenges compared to common rigid objects, and most current generative methods have not been systematically tested in this domain, leaving a gap in understanding their reliability for realistic plant reconstruction. This study systematically evaluates six advanced generative techniques-Hunyuan3D 2.0, Trellis (Structured 3D Latents), One2345++, InstantMesh, Direct3D and Unique3D-using the existing PlantDreamer dataset. Specifically, this research reconstructs mesh models from images of Bean plants and quantitatively assesses each method's performance against ground-truth models using Chamfer Distance, Normal Consistency, F-Score, PSNR, LPIPS, and CLIP Score. The paper also presents qualitative results of Kale and Mint plants. The results indicate that Hunyuan3D 2.0 achieves superior performance overall, suggesting its effectiveness in capturing complex plant structures. This work provides valuable insights into strengths and limitations of contemporary 3D generative approaches, guiding future improvements in realistic plant digitisation.
Why it matches plant phenotyping methods植物画像からの3D再構成手法を体系的に比較・定量評価し、植物形態の非破壊・高スループット表現型解析への利用可能性を検証しており、方法が研究の中心です。
abstractThis study systematically evaluates six advanced generative techniques-Hunyuan3D 2.0, Trellis (Structured 3D Latents), One2345++, InstantMesh, Direct3D and Unique3D-using the existing PlantDreamer dataset.
Introduction In precision agriculture, accurate measurement of maize stem diameter during the jointing stage is crucial for lodging resistance assessment and yield prediction. However, existing methods have certain limitations: manual measurement is time-consuming and highly subjective, while two-dimensional image recognition can only capture local features and fails to reconstruct the true three-dimensional structure of the stem. Therefore, there is a critical need for an accurate and automated three-dimensional stem diameter measurement approach. Methods This study proposes a three-dimensional stem diameter measurement method that integrates an improved PointNet++ segmentation network with structural feature fitting, focusing on the position of the second above-ground internode of maize plants. Specifically, multi-view image reconstruction is employed to generate three-dimensional point clouds of maize stems, and Relative Position Encoding, the Local Group Rearrangement Module, and the Local Region Self-Attention mechanism are incorporated into the PointNet++ network to achieve precise segmentation of stems from the ground. On this basis, a structural feature fitting strategy is applied, where principal axis analysis and ellipse fitting are utilized to extract cross-sectional features, thereby obtaining the major axis and minor axis parameters for stem diameter estimation. Results Experimental results demonstrate that the proposed method maintains high accuracy under complex field conditions, achieving a mean absolute error (MAE) of 1.27 mm (R² = 0.87) for major-axis stem diameter and 1.38 mm (R² = 0.82) for minor-axis stem diameter. Discussion The proposed method effectively overcomes the limitations of traditional manual and two-dimensional measurement techniques. It provides a robust and accurate solution for maize stem diameter measurement during the jointing stage. This approach offers technical support for intelligent maize growth monitoring, lodging resistance analysis, and three-dimensional phenotypic trait extraction.
Why it matches plant phenotyping methodsトウモロコシ茎径という植物形態形質を、3D再構成、点群セグメンテーション、構造特徴フィッティングで自動推定する手法が研究の中心であり、精度検証も行っている。
abstractThis study proposes a three-dimensional stem diameter measurement method that integrates an improved PointNet++ segmentation network with structural feature fitting
Maize-soybean intercropping is a sustainable intensive agroecosystem, though the productivity is constrained by interspecific competition for water and light resources. To enhance the water use efficiency in this intercropping system and understand canopy structure dynamics under the water-limited conditions of arid northwest China, this study proposes a novel optimization strategy that synchronizes deficit irrigation scheduling with crop-specific water requirements during critical phenological phases. Four irrigation regimes were implemented: W1 (full irrigation for both maize and soybean crops), W2 (maize-full and soybean-deficit), W3 (maize-deficit and soybean-full), and W4 (dual deficit). Through UAV-based high-resolution 3D canopy reconstruction (R = 0.98 for plant height validation), 14 spatial-geometric descriptors were quantified. The W2 strategy demonstrated superior competitive coordination, enhancing aggressivity of maize (Ams) by 85.9 % through strategic canopy reconfiguration: 11.8 % reduction in maize maximum leaf layer width position (MLLWP), 28.3 % decrease in inter-specific canopy overlap area (COA), and 40.0 % compression of shading convex hull volume (SCHV). These optimized structural adaptations synergistically enhanced photosynthetically active radiation interception (+13.4 %) while achieving concurrent reductions in crop evapotranspiration (ET, -19.7 %) without yield penalty, thereby elevating irrigation water use efficiency (IWUE) by 14.4 % and water equivalent ratio (WER) by 15.9 %. This work provides mechanistic insights into canopy architecture-mediated resource competition mitigation and establishes a technological framework for sustainable intensification in water-limited environments.
Why it matches plant phenotyping methodsUAVによる3Dキャノピー再構成を用いた植物構造形質の取得と検証が、灌漑試験の主要な解析基盤として明示されているため、実質的なフェノタイピング手法の応用に該当する。
abstractThrough UAV-based high-resolution 3D canopy reconstruction (R = 0.98 for plant height validation), 14 spatial-geometric descriptors were quantified.
Reproduction assets foundThe paper's data availability statement explicitly deposits the authors' analysis source code on a public GitHub repository, which qualifies as a paper-specific public code asset. The study's phenotype data (UAV-derived 3D canopy point clouds, geometric trait measurements, yield/biomass data) are only available upon请求,Code · publicThe source code used in this study is available for noncommercial use and the code can be downloaded from https://github.com/Pepe-oss/3D-Reconstruction-analysis-of-maize-soybean-intercropping-competition-under-water-stress . The data of this study are available from the corresponding author upon request.Open asset ↗Pepe-oss/3D-Reconstruction-analysis-of-maize-soybean-intercropping-competition-under-water-stresslines:320-407Code / dataset availability confirmedEurope PMC · OpenAlex · checked 5 Sept 2026
Three-dimensional (3D) reconstruction technologies for crops are of significant importance in the context of smart breeding and precision agriculture, as they enable accurate characterization of crop spatial architecture and developmental dynamics. Such capabilities provide essential phenotypic information for the rapid selection of breeding materials and informed agronomic decision-making. A critical requirement for the practical application of crop 3D models is high-accuracy organ-level segmentation. However, the absence of a stage-universal segmentation framework capable of operating across complete soybean growth cycle remains a major bottleneck hindering progress in this field. To address this issue, we propose SOY3DSEG-a high-precision framework based on an improved Point Transformer, designed to support the full developmental spectrum of soybean (V1-R7). The framework incorporates a novel down sampling strategy termed Dynamic Multi-Stage Sampling Strategy (DMSS), alongside multi-scale feature enhancement and a local geometry-aware attention mechanism, enhancing segmentation accuracy and efficiency. Performance evaluations across 12 consecutive soybean growth stages (V1 to R7) indicate that SOY3DSEG achieved an average mean Intersection-over-Union (mIoU) of 93.34 % for stem-leaf segmentation-surpassing RandLA-Net, BAAF-Net, PointNet++, and PointConv by over 30 %, and outperforming the baseline Point Transformer by 14.18 %. A moderate accuracy decline appears at R6-R7 due to dense canopies and strong occlusion, yet SOY3DSEG retains clear superiority over the baseline Point Transformer, demonstrating robustness under complex morphology. In cross-crop transfer tests limited to early seedling stages of maize and tomato, the model achieves an mIoU of approximately 99 %, indicating strong early-stage transferability while mature-stage generalization across species remains open for future study. SOY3DSEG thus provides a stage-robust and scalable solution for full-cycle soybean phenotyping and growth monitoring, contributing to precision agricultural practice.
Why it matches plant phenotyping methods大豆の3D点群から器官レベル形態を抽出する分割フレームワークを開発・評価しており、植物表現型取得手法が研究の中心である。
abstractA critical requirement for the practical application of crop 3D models is high-accuracy organ-level segmentation.
Reproduction assets foundThe authors state that the dataset (Soybean-MVS point clouds) and program code used in this study are publicly available at their GitHub repository, which is an allowed URL.Code · publicThe dataset and program code used in this study can be found at the link below: https://github.com/NiuJiarui718/SOY3DSEG .Open asset ↗NiuJiarui718/SOY3DSEGlines:306-323Code / dataset availability confirmedEurope PMC · OpenAlex · checked 15 Sept 2026
Fruit growth has long been described using single- or double-sigmoid curves; however, these temporal models cannot fully capture the spatial heterogeneity that ultimately shapes a fruit. Here, we present a three-dimensional analysis pipeline that non-destructively tracks spatial fruit growth dynamics from field-collected imagery. Surface landmarks were drawn, and video recordings were taken throughout development for three pome fruits, apple ( Malus × domestica ), Japanese pear ( Pyrus pyrifolia ) and European pear ( Pyrus communis ), and two drupe fruits, peach ( Prunus persica ) and Japanese apricot ( Prunus mume ), to track their motion. Using 3D Gaussian splatting, we successfully reconstructed 3D models of the fruits, and the landmark displacement could be measured with high accuracy, with R 2 ≥ 0.98 when compared to manual recordings. We found a common spatial growth gradient in the longitudinal growth shared in the pomes and drupes of the Rosaceae; proximal (stem-end) regions exhibited more pronounced growth than the distal (stylar) end. An exception was found in European pear 'Bartlett,' which showed relatively vigorous growth in the distal region, explaining its distinct shape with expanded distal end. Transverse expansion varied far less than longitudinal expansion, with a possible association with initial fruit morphology. Inter-fruit growth variability peaked in the fastest-growing regions, particularly in the distal area of the European pear, highlighting the link between growth vigor and phenotypic variance. These results provide foundational insights into the developmental dynamics of both pome and drupe fruits of the Rosaceae family, contributing to the optimization of fruit size, shape, and uniformity.
Why it matches plant phenotyping methods3D画像解析パイプラインと3D Gaussian Splattingを用いて果実の空間的成長を非破壊計測し、手動記録との精度比較で検証しているため、植物表現型取得法が中心である。
abstractHere, we present a three-dimensional analysis pipeline that non-destructively tracks spatial fruit growth dynamics from field-collected imagery.
Reproduction assets foundThe authors deposited a subset of the 3DGS-reconstructed fruit models (the paper's phenotyping outputs) on Figshare with a public DOI; additional data only on request. No author analysis code or raw imagery deposit is stated.Dataset · publicFootnotes
Appendix A
Supplementary data to this article can be found online at https://doi.org/10.1016/j.plaphe.2026.100166 .
Appendix A.
Supplementary data
The following is the Supplementary data to this article:
Multimedia component 1
Multimedia component 1
Data availability
A subset of the generated 3D models is available at https://doi.org/10.6084/m9.figshare.30854579 , where the quality of the 3DGS reconstructions and the marking/measurement procedure can be examined. Additional data may be provided upon reasonable request to the corresponding author.
ReferencesOpen asset ↗figshare · 10.6084/m9.figshare.30854579lines:151-171Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Reconstructing the three-dimensional structure of forests in complex and heterogeneous environments is a persistent challenge in forest remote sensing, particularly when integrating multi-source LiDAR datasets with varying acquisition geometries. Traditional registration methods such as the Iterative Closest Point (ICP) algorithm have shown promise but often suffer from accuracy degradation in dense canopies and understorey clutter, limiting their applicability in operational forest monitoring. To address these limitations, this study systematically evaluates the robustness of classical ICP (point-to-point and point-to-plane) in forests with different stand densities and introduces a novel segmented registration strategy that integrates canopy height stratification.Three representative forest types were analyzed—Type I (700–1100 trees/ha), Type II (1100–1400 trees/ha), and Type III (1400–1800 trees/ha)—by jointly utilizing UAV Laser Scanning (ULS) and Backpack Laser Scanning (BLS) data. Registration performance was examined across five stratified scenarios (Type 0–0 m, 0–2 m, 0–4 m, 0–6 m, and 0–8 m), reflecting varying thresholds of aboveground points. Results demonstrate that while classical ICP consistently achieved sub-meter accuracy (RMSE < 0.50 m), the point-to-plane variant induced rigid-body displacement, leading to artifacts such as ground-negative values and stem noise. In contrast, the proposed segmented strategy, which incorporates height-based filtering of ULS data, effectively suppresses interference from understory vegetation and low-canopy structures, yielding substantial gains in registration stability and precision.Notably, Type II plots achieved the highest overall registration accuracy (average RMSE: 0.27 m), while Type I plots exhibited the most pronounced relative improvement, underscoring the method’s adaptability across stand densities. Importantly, this strategy ensures consistent canopy alignment while significantly mitigating stem-related distortions and ground-level errors.By integrating multi-source LiDAR fusion with stratified height-domain constraints, this work advances beyond conventional ICP implementations, providing a scalable framework for precise forest 3D reconstruction. The findings not only refine registration methodologies for heterogeneous forest environments but also lay the groundwork for enhanced forest inventory, biomass estimation, and ecosystem monitoring at scale.
Why it matches plant phenotyping methods森林LiDARデータから樹冠・林分の3次元構造を取得するための点群登録手法を提案・評価しており、植物構造の測定基盤が中心的な貢献である。
abstractintroduces a novel segmented registration strategy that integrates canopy height stratification
Seed phenomics is a critical research field for understanding seed germination mechanisms. Metasurfaces, composed of subwavelength nanostructures, offer a promising pathway to achieve both dispersion control and imaging functionalities within an ultra-compact form factor. Recent advances in micro–nano-optics and computational imaging have opened new avenues for high-dimensional, multimodal imaging. However, conventional hyperspectral and light-field systems still face limitations in compactness, depth resolution, and spectral–spatial integration. This review summarizes recent progress in metalens and metasurface lens array-based light-field systems for hyperspectral imaging and 3D reconstruction, with a focus on the underlying principles, design strategies, and reconstruction algorithms that enable single-shot 3D hyperspectral acquisition. We further present a forward-looking roadmap toward the realization of a revolutionized imaging paradigm: a metasurface-based light-field platform that fully integrates 3D and hyperspectral imaging capabilities. In particular, we examine how dispersive metasurfaces serve as core optical elements for precise dispersion control in hyperspectral imaging systems, while metalens arrays enable accurate modulation of spatial–angular distributions in light-field configurations. We systematically review both 3D and spectral reconstruction algorithms, highlighting their roles in decoding complex optical encodings. The application of these integrated systems in seed phenotyping is emphasized, demonstrating their capability to capture 3D spatial–spectral distributions in a single exposure. This approach facilitates high-throughput analysis of morphological traits, germination potential, and internal biochemical composition, offering a comprehensive solution for advanced seed characterization. Finally, we outline a practical roadmap for implementing a metasurface-based light-field platform that integrates hyperspectral imaging and computational 3D reconstruction. This review offers a comprehensive overview of the state of the art in compact 3D light-field systems and multimodal hyperspectral imaging platforms, while providing forward-looking insights aimed at advancing smart seed phenotyping, precision agriculture, and next-generation optical imaging technologies.
Why it matches plant phenotyping methods種子フェノミクス向けの3D・ハイパースペクトル画像取得および再構成プラットフォームを中心にレビューし、形態形質や発芽能の解析への適用を扱うため、方法論が中心です。
abstractThis review summarizes recent progress in metalens and metasurface lens array-based light-field systems for hyperspectral imaging and 3D reconstruction
This study develops a voxel-based leaf area estimation framework and validates it using a three-year multi-temporal dataset (2022–2024) of pergola-trained grapevines. The workflow integrates 2D image analysis, ExGR-based leaf segmentation, and 3D reconstruction using Structure-from-Motion (SfM). Multi-angle canopy images were collected repeatedly during the growing seasons, and destructive leaf sampling was conducted to quantify true leaf area across multiple vines and years. After removing non-leaf structures with ExGR filtering, the point clouds were voxelized at a 1 cm3 resolution to derive structural occupancy metrics. Voxel-based leaf area showed strong within-vine correlations with destructively measured values (R2 = 0.77–0.95), while cross-vine variability was influenced by canopy complexity, illumination, and point-cloud density. In contrast, optical LAI tools (DHP and LAI–2000) exhibited negligible correspondence with true leaf area due to multilayer occlusion and lateral light contamination typical of pergola systems. This expanded, multi-year analysis demonstrates that voxel occupancy provides a robust and scalable indicator of canopy structural density and leaf area, offering a practical foundation for remote-sensing-based phenotyping, yield estimation, and data-driven management in perennial fruit crops.
Why it matches plant phenotyping methodsブドウ樹の葉面積・樹冠構造を推定する画像解析、SfM、ボクセル化ワークフローを開発し、破壊測定および既存LAI手法と比較検証しており、植物表現型取得法が研究の中心である。
abstractThis study develops a voxel-based leaf area estimation framework and validates it using a three-year multi-temporal dataset (2022–2024) of pergola-trained grapevines.
AppleCottonPearField / plotNeRF / 3D Gaussian SplattingFruitCounting2D/3D reconstructionSegmentation
Rigorous crop counting is crucial for effective agricultural management and informed intervention strategies. However, in outdoor field environments, partial occlusions combined with inherent ambiguity in distinguishing clustered crops from individual viewpoints poses an immense challenge for image-based segmentation methods. To address these problems, we introduce a novel crop counting framework designed for exact enumeration via 3D instance segmentation. Our approach utilizes 2D images captured from multiple viewpoints and associates independent instance masks for neural radiance field (NeRF) view synthesis. We introduce crop visibility and mask consistency scores, which are incorporated alongside 3D information from a NeRF model. This results in an effective segmentation of crop instances in 3D and highly-accurate crop counts. Furthermore, our method eliminates the dependence on crop-specific parameter tuning. We validate our framework on three agricultural datasets consisting of cotton bolls, apples, and pears, and demonstrate consistent counting performance despite major variations in crop color, shape, and size. A comparative analysis against the state of the art highlights superior performance on crop counting tasks. Lastly, we contribute a cotton plant dataset to advance further research on this topic.
Why it matches plant phenotyping methodsNeRFと3Dインスタンスセグメンテーションを用いて作物個体・器官数を推定する画像ベース表現型計測手法を開発・検証しており、方法が研究の中心である。
abstractwe introduce a novel crop counting framework designed for exact enumeration via 3D instance segmentation.
Reproduction assets foundThe paper contributes a public infield cotton plant dataset (8 plants, ~150 iPhone images each, ground-truth boll counts, SAM instance masks) and states that source code, dataset, and multimedia are available at the authors' public project page, which is an allowed URL. The spectacularai GitHub URL is a generic third-pDataset · publicthat incorporates crop visibility
and mask consistency, enabling robustness against occlusions and annotation
discrepancies.
•
We release a public infield cotton plant dataset designed for 3D
rendering and cotton boll counting tasks.
The source code, dataset, and multimedia material associated with this project
can be found at
https://robotic-vision-lab.github.io/cropnerf .
II Related Work
II-A Image-Based Techniques
Image-based methods typically employ object detection to identify crops within
images. For example, Chen et al. [ 4 ] utilized multiple
convolutional neural networks (CNNs) to map input images to total fruit counts.
Similarly, Häni et al. [ 5 ] formulated crop counting as a
multOpen asset ↗lines:108-187Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Published1 Jan 2026International journal of agricultural and biological engineeringCited by 0 · OpenAlex ↗
Accurate 3D morphological characterization is critical for precision breeding. However, traditional phenotypingmethods suffer from inherent limitations such as low efficiency and loss of dimensional information. To overcome theselimitations, this study proposes a robust three-dimensional phenotypic analysis framework based on the monocular FringeProjection Profilometry (FPP) method. This method is specifically optimized for the high-precision measurement of youngplant leaf features. The framework employs a hybrid decoding strategy combining 12-step phase shifting with complementarygray codes to resolve phase ambiguity and reconstruct dense point clouds at sub-millimeter resolution. It addresses theproblems of leaf occlusion and adhesion using the section segmentation method. Based on the Oriented Bounding Box (OBB)method of principal component analysis (PCA) and Delaunay triangulation technology, it achieves precise quantification of keyphenotypic parameters such as leaf length, width, inclination angle, and area. Systematic verification using 72 bitter gourdseedlings as samples indicates that this method has an extremely high consistency with manual measurement results: thedetermination coefficients (R2) for leaf length, leaf width, and leaf inclination angle reach 0.9992, 0.9991, and 0.9933respectively, with corresponding root mean square errors (RMSE) as low as 0.68 mm, 0.54 mm, and 0.92°, and the R2 for leafarea reaches 0.9996. This system achieves the complete process from raw scanning to parameter extraction in a low-cost, non-contact, and semi-automated manner, providing reliable data support for the digital perception and intelligent gradingstandardization of seedling growth dynamics in precision breeding. Keywords: fringe projection, phase unwrapping, 3D reconstruction, leaf phenotyping DOI: 10.25165/j.ijabe.20261904.10643 Citation: Li B, Duan W W, Li Y B, Liu Y D, Chen G, Ouyang S T, et al. 3D phenotypic measurement of bitter gourd seedlings based on monocular structured light. Int J Agric & Biol Eng, 2026; 19(4): 191–202.
Why it matches plant phenotyping methods単眼構造化光による3D葉形質計測フレームワークを開発し、手動測定との系統的検証も行っており、植物フェノタイピング手法が研究の中心である。
abstractthis study proposes a robust three-dimensional phenotypic analysis framework based on the monocular FringeProjection Profilometry (FPP) method.
ABSTRACT Three‐dimensional measurement technology based on point clouds can effectively solve the problem of plant occlusion and is a hot research direction for plant phenotyping methods. Rapid and low‐cost 3D reconstruction and accurate 3D point cloud segmentation are two major challenges in 3D phenotyping technology. Taking watermelon seedlings as an example, we proposed a new phenotyping method that uses the Instant‐NGP for 3D reconstruction and the improved PointNet++ for 3D point cloud segmentation. We performed dynamic downsampling and filtering based on the point cloud scale and designed different phenotypic measurement methods for hypocotyl and leaf point clouds. To overcome the difficulty of measuring the hypocotyl caused by slenderness, curvature and inclination, we proposed a segmented stem 3D point cloud skeleton extraction algorithm. The experimental results show that our method achieved satisfactory measurement results for the seedling phenotypes of four growth stages. The detection accuracy of the number of cotyledon leaves and the number of true leaves both exceed 95% and the coefficient of determination ( R 2 ) of leaf area, hypocotyl length and stem diameter phenotypes are all beyond 0.8. The proposed method provides a novel, efficient and precise 3D plant phenotyping solution, with good application and promotion value.
Why it matches plant phenotyping methods3D再構成、点群セグメンテーション、骨格抽出、形質測定を統合した植物フェノタイピング手法の開発が中心であり、精度評価も実施している。
abstractwe proposed a new phenotyping method that uses the Instant‐NGP for 3D reconstruction and the improved PointNet++ for 3D point cloud segmentation.
Three-dimensional (3D) plant modeling is fundamental for precise phenotyping analysis. In this study, a high-throughput, multi-stage 3D reconstruction pipeline is proposed to efficiently generate point clouds with real-world physical scales. The pipeline comprises five core components: data acquisition, semantic segmentation, sparse reconstruction, dense reconstruction, and phenotypic trait extraction. To enhance the accuracy of plant structure identification, the SegFormer semantic segmentation model is employed for pixel-level segmentation, thereby guiding the subsequent reconstruction stages to focus specifically on plant regions. In feature-based sparse reconstruction, the scarcity of texture information often results in an insufficient number of matching point pairs, leading to failures in camera parameter estimation. Furthermore, the reconstructed point clouds frequently lack consistency with real-world scale. To address these challenges, a calibration-constrained sparse reconstruction method, Sparse Reconstruction from Calibrated Images (SRCI) was proposed. By integrating precise calibration results computed in a custom world coordinate system, SRCI circumvents the limitations of traditional feature matching in scenarios with scarce features, thereby resolving camera pose estimation failures caused by insufficient matching pairs and generating sparse point clouds with true physical scale. Subsequently, CL-MVSNet was employed to generate dense point clouds. The validation experiments are conducted from three perspectives: visual comparison, phenotypic accuracy assessment, and reconstruction accuracy evaluation. First, five groups of rapeseed plants are selected to perform visual comparisons between the proposed reconstruction pipeline and other advanced reconstruction software. The results demonstrate that the proposed reconstruction pipeline achieves superior performance in terms of visual quality. Additionally, three phenotypic parameters of rapeseed plants—plant height, leaf width, and chord length are manually measured and compared with the corresponding phenotypic parameters extracted from the reconstructed point clouds. The analysis revealed mean absolute errors of 4.93 mm, 3.16 mm, and 6.02 mm; root mean square errors of 6.38 mm, 4.56 mm, and 8.35 mm; and coefficients of determination of 0.98, 0.94, and 0.93, respectively. To further validate the generalization performance and accuracy of the proposed method, four additional plant categories with progressively increasing complexity were selected for accuracy evaluation. For the first three plant categories, the Chamfer distances between the reconstructed point clouds and ground truth point clouds were all within 2.4 mm. In the most complex rice reconstruction experiments, the Chamfer distances between the reconstructed point clouds and ground truth point clouds were all within 9 mm, while other methods failed to achieve effective reconstruction. The proposed high-throughput, automated, and high-quality 3D reconstruction framework provides reliable technical support and data resources for genetic research applications, including gene localization, quantitative trait locus analysis, and genome-wide association studies. • We propose a Plant-to-Camera system for high-quality 3D plant reconstruction within 6 minutes, showing strong generalizability. • Our Sparse Reconstruction from Calibrated Images (SRCI) method prevents failures in feature-scarce scenes. • We develop SFNet, a feature descriptor module that fuses multi-frequency to enhance plant feature representation.
Why it matches plant phenotyping methods植物の3D再構成と形質抽出パイプラインを開発し、植物形質および再構成精度を検証しており、フェノタイピング手法が中心である。
abstracta high-throughput, multi-stage 3D reconstruction pipeline is proposed to efficiently generate point clouds with real-world physical scales
Tree height is a key indicator in both forestry ecosystems and tree breeding. Thus, accurately and rapidly monitoring the height distribution and dynamic changes of individual trees and forest stands is of vital importance. With the advancement of deep learning, there has been growing attention on canopy height mapping using a single remote sensing image. However, current research primarily relies on satellite or airborne laser scanning (ALS) data for wall-to-wall canopy estimation. In recent years, close-range remote sensing technologies, particularly unmanned aerial vehicle (UAV)-based methods, have demonstrated significant potential in forest inventory, monitoring, and high-throughput phenotyping of trees. Thus, this work focuses on canopy height mapping using UAV-acquired imagery. To address challenges such as the loss of canopy details and the insufficient precision in crown height variation extraction, we propose a transformer-based method for canopy height mapping, named DepthCanopyNet, specifically designed for ultrahigh-resolution UAV imagery. The DepthCanopyNet integrates a bidirectional gradient-enhancement module into a conditional random field, leveraging gradient-based structural knowledge to emphasize height details, particularly the variations in the tree crown and the edges between the tree crown and the background. Furthermore, a lightweight global stepwise aggregation (GSA) module is employed for multilevel feature aggregation, progressively integrating low-level details with high-level global semantic features. This facilitates the flow of multiscale information across different layers, thereby enhancing the ability of the model to represent tree structures with varying sizes and spatial distributions. A two-stage training strategy is further introduced to alleviate the domain gap between the pretrained model and our depth mapping task. Comprehensive experiments conducted on two UAV datasets—one from a coniferous forest with moderate stand density and another from a broad-leaved forest with high stand density—demonstrate that the proposed method outperforms state-of-the-art architectures. On the coniferous forest dataset, the absolute relative loss decreased by 0.0128, while on the broad-leaved forest dataset, it decreased by 0.0076. In addition, cross-dataset out-of-distribution transfer experiments validate the generalization capability of the proposed method. This work marks a significant advancement in applying monocular depth estimation to centimeter-level remote sensing imagery and highlights the potential of using a single image for extracting individual tree-level crown parameters.
Why it matches plant phenotyping methodsUAV単画像から個体木の樹冠高を抽出する深層学習手法を開発し、複数データセットとクロスデータセット実験で検証しており、植物表現型取得が中心である。
abstractwe propose a transformer-based method for canopy height mapping, named DepthCanopyNet
Tree growth determines how much CO2 is sequestered from the atmosphere and temporarily stored in woody biomass. At the same time tree growth is affected by increasing temperatures, more frequent drought periods, late frosts and other extreme events associated with climate change. While continuous measurements of radial (secondary) tree growth using dendrometers are well established, monitoring of shoot elongation (primary growth) has largely been neglected because suitable measurement techniques are lacking. As a result, the effects of climate change on primary tree growth remain insufficiently understood. This work aims at reconstructing native deciduous trees in 3D as a basis for measuring and monitoring shoot elongation over entire tree canopies. Here we explored the use of low-cost UAV photogrammetry and of a multi-camera CraneCam system under real-world conditions. Data were collected in two study areas over an entire growing season. We present sensor evaluations, photogrammetric data acquisition and processing strategies. A special focus is placed on the analysis of the resulting photogrammetric 3D point clouds in terms of accuracy, resolution and completeness. Results demonstrate 3D point accuracies of 5-6 mm for entire trees using consumer-grade UAVs weighing less than 250 g and a 3D reconstruction completeness between 92% and 98% depending on the UAV type. The paper introduces a novel 3Dprinted ground-truth branch to evaluate the capability to reconstructing fine-detail structures such as thin tree shoots. Finally, we discuss operational challenges and initial experiments towards a skeletonization of entire trees based on photogrammetric point clouds.
Why it matches plant phenotyping methodsUAVおよびマルチカメラによる3D再構成を開発・評価し、樹冠全体のシュート伸長という植物形質の測定に用いる方法が中心である。
abstractThis work aims at reconstructing native deciduous trees in 3D as a basis for measuring and monitoring shoot elongation over entire tree canopies.
Background High-throughput phenotyping is addressing the current bottleneck in phenotyping within breeding programs. Imaging tools are becoming the primary resource for improving the efficiency of phenotyping processes and providing large datasets for genomic selection approaches. The advent of artificial intelligence (AI) brings new advantages by enhancing phenotyping methods using imaging, making them more accessible to breeding programs. In this context, we have developed an open Python workflow for analyzing morphology, color, and morphometric traits using AI, which can be applied to fruits and other plant organs. Results The workflow was implemented in almond (Prunus dulcis (Mill.) D. A. Webb), a species where breeding efficiency is critical due to its long breeding cycle. Over 25,000 kernels, more than 20,000 nuts, and over 600 individuals were phenotyped, making this the largest morphological study conducted in almond so far. The best segmentation and reconstruction approaches achieved error rates below 1%. Weight and area variables enabled accurate estimation of kernel thickness, with a root mean squared error of 0.47. Fifty-five heritable morphological, morphometric, and color traits were identified, highlighting their potential as target traits in breeding programs. Conclusion The proposed workflow demonstrated robust performance across diverse datasets and was effective with limited training data for fine-tuning. Its compatibility with the output of AI-based labeling tools allows users to fully leverage the advantages of these technologies-reducing manual effort, accelerating dataset preparation, and streamlining the fine-tuning process of segmentation models. This flexibility enhances the scalability and practical applicability of the workflow in real-world phenotyping scenarios, especially in the context of breeding programs.
Why it matches plant phenotyping methods植物器官の形態・色・形状特性を抽出するオープンなRGB画像解析ワークフローを開発し、分割・再構成精度も検証しているため、植物フェノタイピング手法が中心です。
abstractwe have developed an open Python workflow for analyzing morphology, color, and morphometric traits using AI, which can be applied to fruits and other plant organs.
Reproduction assets foundThe authors publicly release their almond phenotyping workflow (AlmondCV) as Python/R notebooks on GitHub and as a registered WorkflowHub workflow, covering preprocessing, segmentation model development/deployment, morphology, and morphometric analyses used for this paper's measurements.Code · publiche manual process, which is challenging to automate because of variability in shell hardness and size. This extensive dataset will facilitate future studies aimed at dissecting quantitative traits and implementing genomic selection approaches.
Availability of Source Code and Requirements
Project name: AlmondCV
Project homepage: https://github.com/jorgemasgomez/almondcv2
Operating system(s): Platform independent
Programming language: Python, R
Other requirements: see public environment file released under GNU GPL v3
RRID: SCR_027064
WorkflowHub: https://workflowhub.eu/workflows/1731
Bio.tools: https://bio.tools/almondcv2
Additional Files
Supplementary Table S1 . Article metrics studied relateOpen asset ↗https://github.com/jorgemasgomez/almondcv2lines:222-243Code · publicselection approaches.
Availability of Source Code and Requirements
Project name: AlmondCV
Project homepage: https://github.com/jorgemasgomez/almondcv2
Operating system(s): Platform independent
Programming language: Python, R
Other requirements: see public environment file released under GNU GPL v3
RRID: SCR_027064
WorkflowHub: https://workflowhub.eu/workflows/1731
Bio.tools: https://bio.tools/almondcv2
Additional Files
Supplementary Table S1 . Article metrics studied related to quantitative almond morphological traits.
Supplementary Fig. S1 . Workflow description outlining the steps involved in developing the segmentation model (green) and deploying it (purple).
Supplementary Fig. S2 . YOpen asset ↗https://workflowhub.eu/workflows/1731lines:222-243Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
The accurate three-dimensional (3D) distribution of plant area density (PAD) within forests is crucial for understanding canopy structure and provides essential scene inputs for 3D Radiative Transfer Models (RTMs) to facilitate remote sensing interpretation. However, current lidar-based voxelization methods that estimate detailed PAD distributions often cover limited areas, constraining their applications in conducting broad forest studies and interpreting Earth Observation Satellite (EOS) data of various scales and resolutions. To address this, we developed the Large-Scale Path Volume Leaf Area Density (LS-PVlad), a novel forest 3D reconstruction workflow capable of producing extensive high-resolution 3D voxelized forest scenes (up to 100 km² with ≤2 m voxel size) from worldwide open-access airborne lidar scanning (ALS) data. By applying LS-PVlad to the ALS data acquired during the extensive NASA Goddard's LiDAR, Hyperspectral & Thermal Imager (G-LiHT) campaigns, we developed the first release of FoScenes—a high-fidelity PAD product comprising 40 seamless scenes from 28 diverse forest sites, with individual area ranging from ∼50 to ∼11,000 ha. The leaf area estimates of LS-PVlad have been validated by two-year field-measured leaf area index (LAI) from litter collection (best RMSE = 0.35 m²/m²) and digital hemispherical photography (DHP) images (RMSE = 0.46 m²/m²) across multiple plots at a deciduous forest site. Additionally, a broad comparison between FoScenes and MODIS plant/leaf area index product demonstrates high consistency (R² = 0.70, RMSE = 0.86 m²/m²). By providing multi-dimensional forest characterizations, FoScenes enables temporal insights into structure dynamics. Its integration with the discrete anisotropic radiative transfer (DART) model underscores the potential of FoScenes for extensive 3D RTM applications at various scales.
Why it matches plant phenotyping methods森林の植物面積密度を推定する3D再構成ワークフローを開発し、実測LAI等で検証した大規模フェノタイピング製品・データセットであり、植物形質取得が中心である。
abstractwe developed the Large-Scale Path Volume Leaf Area Density (LS-PVlad), a novel forest 3D reconstruction workflow
In recent years, the development of automated methods for phenotypic assessments of observable plant traits is gaining interest because they can provide advantages over standard ones. This study presents a novel methodology to estimate the canopy volume of grapevine plants during their growth cycle using a low-cost red, green, blue and depth (RGB-D) sensor mounted on a ground-based platform. The aim is to address the need for cost-effective and easy-to-use systems that can operate under variable environmental conditions without requiring highly specific data acquisition constraints and advanced technical skills. Using a Microsoft Azure Kinect RGB-D camera, detailed 3-D images of the plants are captured. The iterative closest point (ICP) algorithm is then applied to reconstruct a full view of the plants useful to estimate the canopy volumes. The effectiveness of this approach is validated by comparing the volumetric estimates with the leaf area index (LAI) measures obtained with traditional agronomic techniques during significant phenological periods of the plants’ lifecycle. The results demonstrate a correlation between the two approaches, indicating the reliability of the proposed method and highlighting advantages in terms of precision. These outcomes demonstrate the potential of automated vineyard monitoring systems, providing reliable data to assess plant growth conditions.
Why it matches plant phenotyping methodsRGB-D画像とICP再構成によりブドウ樹の樹冠体積を推定する手法を開発・検証しており、植物形質の取得が研究の中心です。
abstractThis study presents a novel methodology to estimate the canopy volume of grapevine plants during their growth cycle using a low-cost red, green, blue and depth (RGB-D) sensor mounted on a ground-based platform.
The tea plant (Camellia sinensis) is economically and nutritionally important because of its bioactive compounds. Photosynthesis directly affects tea's growth and productivity, requiring a detailed study of its relationship with cultivation outcomes. We developed a novel computational pipeline for constructing three-dimensional (3D) canopy photosynthesis models of tea plant, leveraging multi-view stereo 3D reconstruction. The ISBNet architecture was optimized for precise leaf–stem segmentation from point cloud data, achieving 0.897 average precision (AP) for leaves and 0.793 AP for stems. We then created a plant leaf morphology-adapted meshing algorithm optimized for plant leaf morphology, achieving an average mesh reduction of approximately 96% while maintaining morphological fidelity compared with conventional meshing methods. We generated multiple tea plant canopies representing distinct planting patterns, and used a ray tracing algorithm to simulate the spatiotemporal distribution of light within these structures. Canopy photosynthesis simulation revealed significant cultivar-specific differences, with 'Yuehuang 1' exhibiting the highest photosynthetic activity. Dense planting (10 cm spacing) significantly enhanced canopy photosynthetic rates compared with wider spacing (20 cm), and a strong linear correlation (r = 0.99) was identified between total leaf area and daily canopy photosynthetic rate across cultivars. This work establishes a methodological foundation for precision agriculture optimization in perennial crops, providing quantitative guidance for maximizing tea plantations' productivity through optimal cultivar selection and spatial configuration.
Why it matches plant phenotyping methods茶樹キャノピーの3D再構築、葉・茎セグメンテーション、形態適応メッシュ化、光線追跡による光合成推定を統合した方法開発が中心であり、植物形態・光合成状態の定量化に直接つながる。
abstractWe developed a novel computational pipeline for constructing three-dimensional (3D) canopy photosynthesis models of tea plant, leveraging multi-view stereo 3D reconstruction.
Three-dimensional (3D) reconstructions of orchards offer richer data for digital phenotyping and underpin smart-agriculture applications. However, achieving high-level reconstruction quality and robustness is challenging due to the complex structure of the orchard. This study presents a novel framework that provides centimeter-level 3D realtime reconstructions and phenotyping for apple orchards. A multi-RGBD camera array was adopted, creating a wide overlapping view and robust features. The hybrid odometry front-end and the dual loop-closure strategy ensured low-drift pose estimation. The generated pointcloud was input into the ellipsoid-fitting routine to extract fruit diameter and volume. We validated this framework through reconstruction and phenotypic errors in four rows of an apple orchard with different tree spacings. The root mean square error of the absolute trajectory error in global reconstruction was less than 16 mm. The mean absolute percentage error (MAPE) of the local fiducial distance of approximately 5 m was less than 0.12%. The system was implemented at higher than 12.5 frames per second in an embedded system. The MAPEs of the fruit’s diameter were 2–2.17%, and those of its volume were 5.3–5.6%. Additionally, ablation experiments were carried out on multi-camera and loop-closed elements, and comparisons were made with existing methods to further demonstrate their effectiveness. In conclusion, this research provides an efficient and stable deployable solution for 3D reconstruction of orchards, which is conducive to the development of more advanced and multilayer modern orchard models and promotes the practice of smart agriculture.
Why it matches plant phenotyping methodsリンゴ園向けのマルチRGB-Dによる3D再構成と、点群から果実径・体積を抽出するフェノタイピング手法を開発・検証しており、方法が研究の中心である。
abstractThis study presents a novel framework that provides centimeter-level 3D realtime reconstructions and phenotyping for apple orchards.
Understanding the relatedness of angiosperms and the evolution of their inflorescence remains challenging, as these structures are highly modified and prone to convergent evolution. The current research describing inflorescence architecture, whether genetically or morphologically focused, typically relies on text based explanations, 2D images, or 3D based models created with CAD modeling software. This creates a disparity in the reader's understanding of these models since descriptions rely heavily on the author's interpretation of the inflorescence. The goal of our study is to bridge this disconnection by producing anatomically and color-correct 3D inflorescence models using Solanaceae flowers that readers can explore directly, which will allow readers to view and rotate the inflorescence structure in real time. The Solanaceae clade serves as an excellent platform to demonstrate this concept, as it is an active area of floral research and exhibits high inflorescence diversity within the clade. Its ancestral scorpioid cyme-like morphology is presently thought to have been the result of convergent evolution from a currently unknown driving force. Developing 3D models of extant Solanaceae species could provide valuable insights into these evolutionary patterns and help clarify the mechanisms underlying inflorescence diversification. Here, we use a novel photogrammetry approach to create 3D renderings of the inflorescences of Juanulloa sp., in the Solanaceae clade. This process will involve taking high-resolution 360° photos at various angles using a camera. Photos will be processed with Agisoft Metashape software, which generates 3D models using photographs. It is expected that the rendered 3D images will accurately reflect specimen dimensions with precise color. This will serve as a way to study and provide a larger 3D inflorescence library that can bridge the gap between authors and readers within the literature.
Why it matches plant phenotyping methodsSolanaceaeの花序形態を対象に、フォトグラメトリと3D再構成による植物形質の取得・可視化手法を開発しており、方法が研究の中心である。
abstractHere, we use a novel photogrammetry approach to create 3D renderings of the inflorescences of Juanulloa sp., in the Solanaceae clade.
This systematic literature review investigates the development of a Ground-Penetrating Radar (GPR)-based object detection system tailored for under-ground garlic crop monitoring. While garlic-specific GPR applications re-main limited, studies on structurally similar root crops such as potatoes and carrots provide a valuable reference framework. Using a PRISMA-guided methodology, 16 relevant studies were analysed and synthesized, highlighting advancements in GPR signal processing, object reconstruction, and machine learning integration. Results show that mid- frequency GPR (500–800 MHz), especially when paired with deep learning models such as 3D Convolutional Neural Networks (CNNs), offers high accuracy in detecting root structures. Key challenges such as signal attenuation in clay-rich and tropical soils are addressed through electromagnetic induction (EMI) hybridization and antenna optimization. A comparative matrix summarizes the most relevant findings, and actionable recommendations are proposed to guide future research. These include the development of garlic-specific datasets, localized field testing, and AI- enhanced signal classification. GPR, when effectively configured and paired with machine learning, presents a viable solution for real-time, non-invasive garlic crop monitoring in tropical agriculture.
Why it matches plant phenotyping methods地下作物の成長・根構造をGPRで検出する手法の開発に焦点を当てた系統的レビューであり、植物形態の非破壊取得・抽出方法が中心です。
titleA Systematic Literature Review for the Development of an Object Detection System for Monitoring Underground Crop (Garlic) Growth Using GPR
Maize-soybean intercropping is a sustainable intensive agroecosystem, though the productivity is constrained by interspecific competition for water and light resources. To enhance the water use efficiency in this intercropping system and understand canopy structure dynamics under the water-limited conditions of arid northwest China, this study proposes a novel optimization strategy that synchronizes deficit irrigation scheduling with cropspecific water requirements during critical phenological phases. Four irrigation regimes were implemented: W1 (full irrigation for both maize and soybean crops), W2 (maize-full and soybean-deficit), W3 (maize-deficit and soybean-full), and W4 (dual deficit). Through UAV-based high-resolution 3D canopy reconstruction (R = 0.98 for plant height validation), 14 spatial-geometric descriptors were quantified. The W2 strategy demonstrated superior competitive coordination, enhancing aggressivity of maize (Ams) by 85.9 % through strategic canopy reconfiguration: 11.8 % reduction in maize maximum leaf layer width position (MLLWP), 28.3 % decrease in inter-specific canopy overlap area (COA), and 40.0 % compression of shading convex hull volume (SCHV). These optimized structural adaptations synergistically enhanced photosynthetically active radiation interception (+13.4 %) while achieving concurrent reductions in crop evapotranspiration (ET, -19.7 %) without yield penalty, thereby elevating irrigation water use efficiency (IWUE) by 14.4 % and water equivalent ratio (WER) by 15.9 %. This work provides mechanistic insights into canopy architecture-mediated resource competition mitigation and establishes a technological framework for sustainable intensification in water-limited environments.
Why it matches plant phenotyping methodsUAVベースの3Dキャノピー再構成を用いて植物構造形質を抽出し、草丈検証と14個の空間・幾何記述子の定量を行っており、表現型取得・解析が実質的に記述されている。灌漑試験への応用ではあるが、方法の検証と再利用可能なワークフローが明示されているため採用。
abstractThrough UAV-based high-resolution 3D canopy reconstruction (R = 0.98 for plant height validation), 14 spatial-geometric descriptors were quantified.
RiceLaboratory / benchtopSeed / grain2D/3D reconstructionVisualization / data management
The distribution of inorganic elements in brown rice has been vigorously investigated for many years using the most advanced instruments of each era. The present study was a challenge to gain new insights into the distribution of various inorganic elements in brown rice by autoradiography using radioisotopes: 22Na, 45Ca, 54Mn, 55Fe, 60Co, 63Ni, 65 Zn, 90Sr, 203 Hg, and 210 Pb. Autoradiography of tissue sections using the Imaging Plate (IP) fully exploited its advantage of high-throughput imaging, enabling three-dimensional reconstruction that encompassed the entire brown rice grain. Consequently, characteristic distribution patterns of individual elements in the peripheral layer, endosperm, and embryo were identified following radiotracer supplementation to the culture solution. For instance, 63Ni was uniformly distributed within the endosperm during the early stages of development but progressively accumulated in the outer layers and embryo as growth advanced; such a pattern was not observed for 54Mn or 55Fe. To minimize the cost of the experiment, a direct injection method into the node was developed. This approach successfully visualized 203 Hg, demonstrating that its entry into the embryonic tissue is severely restricted irrespective of the developmental stage of the rice grain.
Why it matches plant phenotyping methods褐色米粒を対象に、オートラジオグラフィーとイメージングプレートで元素分布を高スループットに可視化し、三次元再構成する測定手法を中心に扱っているため、植物器官の状態を抽出するフェノタイピング手法として含める。
abstractThe present study was a challenge to gain new insights into the distribution of various inorganic elements in brown rice by autoradiography using radioisotopes
High-precision plant phenotyping requires efficient 3D reconstruction with high fidelity, yet existing methods such as MVS and NeRF all have problems of feature dependence and error accumulation during 3D reconstruction, which leads to geometric distortion in reconstruction and restricts the reconstruction efficiency. To address this bottleneck, this study first determined the multi-view image acquisition strategy. Further, based on the self-built multi-view dataset of chili peppers, it proposed an algorithm for efficient and high-fidelity 3D reconstruction of complex plant structures through global adaptive pose optimization and gaussian splash rendering technology, referred to as the GAPose-GS algorithm. Experimental results indicate that the Peak Signal-to-Noise Ratio ( PSNR ) improves by 52.0 %, 26.4 %, and 4.2 % compared to NeRF, Instant-NGP, and 3D Gaussian Splatting respectively. Additionally, the Structural Similarity Index Measure ( SSIM ) increases by 22.9 %, 12.8 %, and 4.3 % respectively over above methods. The point cloud data reconstructed based on this algorithm also has advantages in the measurement of phenotypic parameters. Compared with the actual measured values, the R² of the phenotypic parameters such as pepper plant height, canopy width, and leafstalk angle obtained in this study are 0.997, 0.954 and 0.978 respectively, and the RMSE are 0.236 cm, 1.082 cm and 2.344° respectively, and the MAE are 0.209 cm, 0.880 cm and 1.965° respectively. The accuracy was significantly better than that of the existing phenotypic calculation methods. Verification across different growth stages of wheat and maize was performed universally, with all errors remaining below 1.1 %, providing new ideas and technologies for high-precision, low-cost, and high-throughput crop phenotypic research.
Why it matches plant phenotyping methods植物の多視点画像から3D再構成し、草丈・群落幅・葉柄角などの形質を推定する手法を開発・比較検証しており、フェノタイピング手法が中心的である。
abstractit proposed an algorithm for efficient and high-fidelity 3D reconstruction of complex plant structures through global adaptive pose optimization and gaussian splash rendering technology, referred to as the GAPose-GS algorithm.
Field / plotPhotogrammetry / SfM / MVSLiDAR / point cloudMultispectral / hyperspectralLeaf2D/3D reconstructionArchitecture / morphology / geometry
Conifer shoots possess highly complex geometrical structures at a very fine spatial resolution. Accurately characterizing the full architecture of a conifer shoot, which influences how radiation is scattered, has proven challenging. Previous radiative transfer models for coniferous stands have represented these structures in a relatively simplified or coarse manner. This paper presents a dataset that can be used for up-scaling of needle to shoot optical properties and studying the influence of detailed three-dimensional (3D) structure of shoot to light scattering within tree crown. The dataset includes 3D structural information as well optical properties of needles and twigs for 27 shoots of two conifer species present in both locations (3 shoots per species and position in the crown) - Scots pine ( Pinus sylvestris L.) and Norway spruce ( Picea abies L. Karst. ). The samples were collected on 22nd April 2024 in Rájec, the Czech Republic and 17th September 2024 in Järvselja, Estonia. Subsequently blue light 3D photogrammetry scanning technique was used to obtain their high-resolution 3D point cloud representations. Reflectance and transmittance measurements of needles were obtained using a spectroradiometer and an integrating sphere. For each of these samples, the dataset comprises a photo of the sampled shoot, obtained 3D surface reconstruction, and optical properties of conifer needles and twigs (hemispherical-conical reflectance and transmittance factors) in the spectral range of 400-2000 nm. A detailed 3D representation of needle shoots, when combined with radiative transfer modeling, may offer a means to study and compensate for inaccuracies in the measurement of needle optical properties and to enhance the assessment of shoot scattering characteristics.
Why it matches plant phenotyping methods針葉樹シュートの3D構造をフォトグラメトリで取得し、光学特性とともに再利用可能なデータセットとして提供しているため、植物形態・構造の計測手法が中心です。
abstractThis paper presents a dataset that can be used for up-scaling of needle to shoot optical properties and studying the influence of detailed three-dimensional (3D) structure of shoot to light scattering within tree crown.
Reproduction assets foundThe paper is a Data in Brief article describing a public Mendeley Data repository containing the paper's own phenotyping measurements: 3D surface geometry models (.obj) of Scots pine and Norway spruce shoots, sample photos (.jpg), and needle/twig optical property spectra (HCRF/HCTF, .csv, 400-2000 nm). The repository, Dataset · publicRepository name: Mendeley
Data identification number: 10.17632/h39f9t7fjg.1
Direct URL to data: https://data.mendeley.com/datasets/h39f9t7fjg/2Open asset ↗Mendeley · 10.17632/h39f9t7fjg.1lines:47-74Code / dataset availability confirmedarXiv · OpenAlex · checked 13 Sept 2026
Automating tasks in orchards is challenging because of the large amount of variation in the environment and occlusions. One of the challenges is apple pose estimation, where key points, such as the calyx, are often occluded. Recently developed pose estimation methods no longer rely on these key points, but still require them for annotations, making annotating challenging and time-consuming. Due to the abovementioned occlusions, there can be conflicting and missing annotations of the same fruit between different images. Novel 3D reconstruction methods can be used to simplify annotating and enlarge datasets. We propose a novel pipeline consisting of 3D Gaussian Splatting to reconstruct an orchard scene, simplified annotations, automated projection of the annotations to images, and the training and evaluation of a pose estimation method. Using our pipeline, 105 manual annotations were required to obtain 28,191 training labels, a reduction of 99.6%. Experimental results indicated that training with labels of fruits that are $\leq95\%$ occluded resulted in the best performance, with a neutral F1 score of 0.927 on the original images and 0.970 on the rendered images. Adjusting the size of the training dataset had small effects on the model performance in terms of F1 score and pose estimation accuracy. It was found that the least occluded fruits had the best position estimation, which worsened as the fruits became more occluded. It was also found that the tested pose estimation method was unable to correctly learn the orientation estimation of apples.
Why it matches plant phenotyping methodsリンゴの姿勢推定アノテーションを大幅に効率化する3D再構成・自動ラベル投影パイプラインを開発し、姿勢推定性能も評価しているため、植物フェノタイピング手法が中心である。
abstractWe propose a novel pipeline consisting of 3D Gaussian Splatting to reconstruct an orchard scene, simplified annotations, automated projection of the annotations to images, and the training and evaluation of a pose estimation method.
Reproduction assets foundThe paper explicitly provides two paper-specific public assets: the authors' phenotyping/pose-estimation pipeline code on GitHub and the collected apple orchard image dataset on a 4TU DOI. Both are directly used for the paper's measurements and analysis.Dataset · publicIn total, 367 images were collected. The dataset is available at https://doi.org/10.4121/976c94f2-028f-4291-adfd-20eb82b0f647Open asset ↗10.4121/976c94f2-028f-4291-adfd-20eb82b0f647lines:92-108Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 13 Sept 2026
Introduction Three-dimensional (3D) point clouds acquired by LiDAR are fundamental for applications such as autonomous navigation, mobile robotics, infrastructure inspection, and cultural-heritage documentation. However, environmental disturbances and sensor limitations often yield incomplete or noisy point clouds, degrading downstream performance. This study addresses robust, high-fidelity point cloud completion under such practical conditions. Methods We propose an unsupervised deep learning framework, Multi-Resolution Completion Net (MRC-Net), which builds on ShapeInversion by integrating a Generative Adversarial Network (GAN) inversion strategy with multi-resolution principles. The architecture comprises an encoder for feature extraction, a generator for completion, and a discriminator to assess geometric integrity and detail. Two key designs enable strong performance without supervision: (i) a multi-resolution degradation mechanism that guides reconstruction across coarse-to-fine scales, and (ii) a multi-scale discriminator that captures both global structure and local details. Results Extensive experiments on multiple datasets demonstrate that MRC-Net achieves accuracy comparable to leading supervised approaches. On virtual datasets (e.g., CRN), MRC-Net attains an average Chamfer Distance (CD) of 8.0 and an F1 score of 91.3. On a custom dataset targeting agricultural scenarios, the model preserves object integrity across varying complexity: for regular cartons, it achieves CD 3.3 and F1 97.3; for structurally complex simulated plants, it maintains overall shape while delivering average CD 8.6 and F1 88.1. Discussion These results indicate that MRC-Net advances unsupervised point cloud completion by balancing global shape consistency with fine-grained detail. The method provides a reliable data foundation for downstream tasks—including autonomous navigation, high-precision 3D modeling, and agricultural robotics—thereby contributing to improved data quality in precision-agriculture and related domains.
Why it matches plant phenotyping methods植物3D LiDAR点群の欠損補完を主題とする手法開発であり、植物形状の再構成性能を実験的に検証しているため、植物形態フェノタイピングに再利用可能な中心的手法研究と判断する。
titleStructure-aware completion of plant 3D LiDAR point clouds via a multi-resolution GAN-inversion network
Dexfruit is a robotic manipulation framework that enables gentle, autonomous handling of fragile fruit and precise evaluation of damage. Soft fruits have long faced an issue of produce loss in both the harvesting and post-harvesting processes due to their extreme fragility and susceptibility to bruising, making them one of the hardest produce type to manipulate with automation. In this work, we demonstrate by using optical tactile sensing, autonomous manipulation of fruit with minimal damage can be achieved. We show that our tactile informed diffusion policies outperform baselines in both reduced bruising and pickand- place success rate across three fruits: strawberries, tomatoes, and blackberries. In addition, we introduce FruitSplat, a novel technique to represent and quantify visual damage in a highresolution 3D representation via 3D Gaussian Splatting (3DGS). Existing metrics for measuring damage lack quantitative rigor or require expensive equipment. With FruitSplat, we distill a 2D fruit mask as well as a 2D bruise segmentation mask into the 3DGS representation from just a web-cam video. Furthermore, this representation is modular and general, compatible with any relevant 2D model. Overall, we demonstrate a 92% grasping policy success rate, up to a 15% reduction in visual bruising, and up to a 31% improvement in grasp success rate on challenging fruit compared to our baselines across our three tested fruits. We rigorously evaluate this result with over 630 trials. Please checkout our website, which contains our code and datasets athttps://dex-fruit.github.io/.
Why it matches plant phenotyping methodsFruitSplatは、果実の損傷・打撲を3D表現として定量化する画像ベースの植物状態計測手法であり、開発と厳密な評価が研究の中心です。
abstractwe introduce FruitSplat, a novel technique to represent and quantify visual damage in a highresolution 3D representation via 3D Gaussian Splatting (3DGS).
Accurate measurement of tree diameter in forests is essential for sustainable management of forest resources, ecological assessment, and scientific research. However, most trees in tropical forests have irregularities at the base of the trunk, making it challenging to measure the trunk diameter above them with a tape measure. To meet the increasing demand for data accuracy and reliability, approaches using three-dimensional (3D) point clouds offer a valuable new source of data for tree measurements. This study examines the accuracy of diameter measurements above irregularities using the Close-Range Photogrammetric approach, with diameter tape serving as the reference. A total of 212 trees measured in the north of the Republic of Congo were reconstructed in three dimensions (3D), including 128 trees in semi-deciduous forest and 84 trees in evergreen forest. Comparisons were made in terms of dependence (simple linear regression), correlation (Pearson, Kendall, and Spearman tests), agreement (Bland and Altman method), and difference (Mean Absolute Error - MAE, Root Mean Square Error - RMSE, bias - BIAS, and coefficient of variation - CV). In addition to a near perfect match, a strong association of diameter measurements and a good degree of agreement, the results indicated the presence of differences between diameter measurement approaches in semi-deciduous forest (MAE = 9.25 cm, RMSE = 16.95 cm, BIAS = 7.45 cm) and evergreen forest (MAE = 3.88 cm, RMSE = 8.47 cm, BIAS = 2.37 cm). These differences are minor in the evergreen forest. The magnitude of the differences found is mostly due to the size of the large-diameter classes. In addition, the coefficients of variation (CV) of diameter obtained from the Close-Range Photogrammetric approach were lower than those obtained from the classic conventional approach in both forests, indicating the higher accuracy of the former approach. Further studies could use larger data samples to provide more accurate estimates and verify the limits of these applications’ measurement capabilities.
Why it matches plant phenotyping methods樹木直径という植物形態形質を近距離写真測量で取得し、従来法を基準に精度・一致度を検証しており、フェノタイピング手法が中心である。
abstractThis study examines the accuracy of diameter measurements above irregularities using the Close-Range Photogrammetric approach, with diameter tape serving as the reference.
We present a method for jointly recovering the appearance and internal structure of botanical plants from multi-view images based on 3D Gaussian Splatting (3DGS). While 3DGS exhibits robust reconstruction of scene appearance for novel-view synthesis, it lacks structural representations underlying those appearances (e.g., branching patterns of plants), which limits its applicability to tasks such as plant phenotyping. To achieve both high-fidelity appearance and structural reconstruction, we introduce GaussianPlant, a hierarchical 3DGS representation, which disentangles structure and appearance. Specifically, we employ structure primitives (StPs) to explicitly represent branch and leaf geometry, and appearance primitives (ApPs) to the plants' appearance using 3D Gaussians. StPs represent a simplified structure of the plant, i.e., modeling branches as cylinders and leaves as disks. To accurately distinguish the branches and leaves, StP's attributes (i.e., branches or leaves) are optimized in a self-organized manner. ApPs are bound to each StP to represent the appearance of branches or leaves as in conventional 3DGS. StPs and ApPs are jointly optimized using a re-rendering loss on the input multi-view images, as well as the gradient flow from ApP to StP using the binding correspondence information. We conduct experiments to qualitatively evaluate the reconstruction accuracy of both appearance and structure, as well as real-world experiments to qualitatively validate the practical performance. Experiments show that the GaussianPlant achieves both high-fidelity appearance reconstruction via ApPs and accurate structural reconstruction via StPs, enabling the extraction of branch structure and leaf instances.
Why it matches plant phenotyping methods植物の多視点画像から枝構造と葉インスタンスを再構成・抽出する手法を開発しており、植物フェノタイピングへの適用と形態情報の抽出が中心的な技術貢献である。
abstractWe present a method for jointly recovering the appearance and internal structure of botanical plants from multi-view images based on 3D Gaussian Splatting (3DGS).
Accurate and efficient plant phenotyping is essential for modern precision agriculture. as it provides reliable information for seedling quality evaluation, early detection of plant stress, and data support for crop breeding and yield prediction. Traditional three-dimensional (3D) reconstruction and analysis methods are often costly and time-consuming, because they usually depend on expensive laser scanning devices or require many input images. Even with these resources, they often fail to capture fine plant structures such as leaves and branches, which limits their application in seedling monitoring. To address these challenges, we propose an integrated framework that combines neural radiance fields (NeRFs) for high-fidelity 3D reconstruction, PointNet++ for robust semantic segmentation, and a customized algorithm for extracting key morphological parameters of tomato seedlings. The proposed framework can be used to reconstruct detailed 3D models at a low computational cost using only ordinary cameras and a limited number of 2D images. We validate the framework on the basis of a tomato seedling dataset and show that our approach outperforms traditional multiview stereo scanners and simple commercial 3D scanners in terms of both detail and efficiency. The accuracy of plant part segmentation reaches 90 %, and the extracted parameters (e.g., leaf area, stem height, branch angle, and internode distance) are highly correlated with the manual measurements (e.g., R 2 = 0.875 for the leaf area). This study provides a low-cost and scalable solution for 3D plant analysis, with direct benefits for automated monitoring of seedling quality in nursery production. Moreover, the proposed framework can be extended to other crops with complex structures, thus supporting wider applications in smart agriculture.
Why it matches plant phenotyping methodsトマト苗の3D再構成、植物部位分割、形態形質抽出を統合した低コスト画像ベース手法を開発・検証しており、植物フェノタイピングが研究の中心である。
abstractwe propose an integrated framework that combines neural radiance fields (NeRFs) for high-fidelity 3D reconstruction, PointNet++ for robust semantic segmentation, and a customized algorithm for extracting key morphological parameters of tomato seedlings.
The development of artificial intelligence (AI) and machine learning (ML) based tools for 3D phenotyping, especially for maize, has been limited due to the lack of large and diverse 3D datasets. 2D image datasets fail to capture essential structural details such as leaf architecture, plant volume, and spatial arrangements that 3D data provide. To address this limitation, we present MaizeField3D (website), a curated dataset of 3D point clouds of field-grown maize plants from a diverse genetic panel, designed to be AI-ready for advancing agricultural research. Our dataset includes 1045 high-quality point clouds of field-grown maize collected using a terrestrial laser scanner (TLS). Point clouds of 520 plants from this dataset were segmented and annotated using a graph-based segmentation method to isolate individual leaves and stalks, ensuring consistent labeling across all samples. This labeled data was then used for fitting procedural models that provide a structured parametric representation of the maize plants. The leaves of the maize plants in the procedural models are represented using Non-Uniform Rational B-Spline (NURBS) surfaces that were generated using a two-step optimization process combining gradient-free and gradient-based methods. We conducted rigorous manual quality control on all datasets, correcting errors in segmentation, ensuring accurate leaf ordering, and validating metadata annotations. The dataset also includes metadata detailing plant morphology and quality, alongside multi-resolution subsampled point cloud data (100k, 50k, 10k points), which can be readily used for different downstream computational tasks. MaizeField3D will serve as a comprehensive foundational dataset for AI-driven phenotyping, plant structural analysis, and 3D applications in agricultural research.
Why it matches plant phenotyping methods3D点群の収集・分割・注釈・手続き型モデル化を中核とする、植物表現型解析向けの再利用可能なデータセットである。
abstractwe present MaizeField3D (website), a curated dataset of 3D point clouds of field-grown maize plants from a diverse genetic panel, designed to be AI-ready for advancing agricultural research.
Reproduction assets foundThe paper's own MaizeField3D dataset (1045 TLS point clouds, 520 segmented/annotated plants, metadata, STL/DAT procedural model outputs) is publicly available on Hugging Face, with a project website and public GitHub code for the procedural NURBS surface generation used in the analysis.Dataset · publicThe MaizeField3D dataset is publicly available on the Hugging Face Datasets platform at https://huggingface.co/datasets/BGLab/MaizeField3D. It includes high-resolution point clouds, segmented plant models, metadata, and reconstructed outputs in STL and DAT formats.Open asset ↗BGLab/MaizeField3Dhtml-lines:343-354Code · publicThe code for procedural NURBS surface generation used in this work is available at https://github.com/baskargroup/ProceduralMaize3D.Open asset ↗baskargroup/ProceduralMaize3Dhtml-lines:343-354Code / dataset availability confirmedOpenAlex · Crossref · checked 6 Sept 2026
Automated three-dimensional plant phenotyping is an essential tool for non-destructive analysis of plant growth and structure. This paper presents a low-cost system based on stereo vision for depth estimation and morphological characterization of maize plants. The system incorporates an automatic detection stage for the object of interest using deep learning techniques to delimit the region of interest (ROI) corresponding to the plant. The Semi-Global Block Matching (SGBM) algorithm is applied to the detected region to compute the disparity map and generate a partial three-dimensional representation of the plant structure. The ROI delimitation restricts the disparity calculation to the plant area, reducing processing of the background and optimizing computational resource use. The deep learning-based detection stage maintains stable foliage identification even under varying lighting conditions and shadowing, ensuring consistent depth data across different experimental conditions. Overall, the proposed system integrates detection and disparity estimation into an efficient processing flow, providing an accessible alternative for automated three-dimensional phenotyping in agricultural environments.
Why it matches plant phenotyping methods植物の3次元形態を取得・特徴づけるステレオビジョンと深度推定システムの開発が中心であり、明確な植物フェノタイピング手法です。
abstractThis paper presents a low-cost system based on stereo vision for depth estimation and morphological characterization of maize plants.
Reproduction assets foundThe paper's Data Availability Statement openly deposits the original study data (the 544 stereo RGB maize images and related phenotyping data) on OSF at a DOI, which is a paper-specific, publicly actionable asset. No author analysis code repository is explicitly stated.Dataset · publicData Availability Statement: The original data presented in the study are openly available in OSF at
https://doi.org/10.17605/OSF.IO/MN6P9.Open asset ↗OSF · 10.17605/OSF.IO/MN6P9pdf-page:18 lines:1-59Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Published10 Dec 2025ISPRS Open Journal of Photogrammetry and Remote SensingCited by 3 · OpenAlex ↗
Light drones provide a cheap and effective tool to monitor forest canopy, especially in tropical and equatorial contexts, where infrastructure and resources are limiting. In these regions, good quality optical satellite images are rare, yet the stakes are maximal to characterize forest function, dynamics, diversity, and phenology, and more generally the vegetation-climate interplay. We describe a complete processing chain based on photogrammetric tools that seeks to optimize the spatial and spectral coherence between repeat image mosaics at centimetric resolution. Our target is to allow individual tree-level monitoring over tens to hundreds of hectare scales with consumer grade equipment (i.e., quadcopter with stabilized RGB camera, standard GNSS positioning). We demonstrate the increase in spatial accuracy achieved using Time-SIFT and Arosics algorithms, which allow (individually and synergistically) to reduce global and local spatial misalignment between mosaics from several meters to a few centimeters. Time-SIFT provides the advantage of increased robustness in initial image alignment and 3D reconstruction, and hence reduces occasional distortions or data gaps. Using Agisoft's color and white balance corrections combined with the use of vegetation indices provides meaningful quantitative signal despite considerable changes in acquisition conditions. In particular, indices that are less sensitive to illumination changes, like the green chromatic coordinate (GCC), allowed evidencing a seasonal signal over four years of monitoring in the evergreen moist forest at Paracou in French Guiana. The signal was decorrelated from obvious geometrical effect (sun height), and provided information on the vegetative stage at tree, species, and stand levels.
Why it matches plant phenotyping methodsドローン画像のステレオフォトグラメトリ処理チェーンを開発・検証し、個体樹木レベルで植生状態や季節変動を定量化する方法を中心的に扱っている。
abstractWe describe a complete processing chain based on photogrammetric tools that seeks to optimize the spatial and spectral coherence between repeat image mosaics at centimetric resolution.
PoplarField / plotRoot2D/3D reconstructionRoot system architecture
Root system architecture (RSA) is pivotal for comprehending the ecological adaptation strategies and resource acquisition mechanisms of urban flora, playing a vital role in soil stability, carbon sequestration, and ecosystem sustainability. However, the non-destructive detection and precise three-dimensional (3D) reconstruction of RSA within urban environments remain challenging. In this study, a non-destructive reconstruction method utilizing ground-penetrating radar (GPR) technology was developed to achieve 3D reconstruction and visualization of RSA, with the goal of advancing the intelligent construction and precise ecological management of urban forest parks. Field-based GPR surveys of a 9-year-old triploid poplar were conducted using a square grid and concentric circular scanning scheme. A 3D data volume (C-scan) was constructed from two-dimensional (2D) profiles, and the spatial distribution of RSA was reconstructed using instantaneous amplitude analysis. The method was validated by comparing the results with actual root structures in sandy loam environments. The research results of the 1600 MHz GPR under the square grid scanning scheme show that extracting the instantaneous amplitude isosurface of GPR can effectively reflect the spatial distribution of roots with diameters greater than 1 cm within a depth of 0.4 m subsurface. The accuracy of RSA reconstruction can reach 89 %. The results demonstrate the applicability of the proposed method for non-destructive environmental monitoring in urban forest parks, showing significant potential for the large-scale detection and reconstruction of subsurface root systems. This research provides a novel approach for RSA reconstruction with significant implications for urban ecosystem management, soil conservation, and climate resilience research. The method enhances our capability to monitor the growth and adaptation of urban roots, laying the groundwork for the large-scale, non-destructive analysis of RSA.
Why it matches plant phenotyping methodsGPRと瞬時振幅解析を用いて樹木根系構造を3D再構成する方法を開発し、実際の根構造との比較で検証しており、根系形態の取得が中心的な研究目的である。
abstracta non-destructive reconstruction method utilizing ground-penetrating radar (GPR) technology was developed to achieve 3D reconstruction and visualization of RSA
Reproduction assets foundThe paper's Data Availability statement explicitly releases the GPR root scanning data on Zenodo and the RSA reconstruction analysis code on GitHub, both with public URLs matching allowed entries.Code · publicCode is available at https://github.com/Niceguoqiu/RSA-Reconstruction-Code.git .Open asset ↗GitHub · Niceguoqiu/RSA-Reconstruction-Codelines:268-286Plant phenotyping relevance match · UnverifiedOpenAlex · checked 13 Sept 2026
While the root architecture of potted crop seedlings directly determines subsequent crop productivity and adaptability, these root systems remain challenging to quantify using conventional methods due to their structural complexity. To investigate the microscopic characteristics of the root systems of pepper seedlings within pots, Micro-CT was employed to scan the seedling pots. After three-dimensional (3D) reconstruction was conducted on the data acquired from the pot scans, the 3D model of the root system was segmented and extracted using the watershed algorithm. Vertically, the three-dimensional root model was divided from top to bottom into four equally spaced regions (a, b, c, and d), showing the volumetric distribution characteristics of pepper seedling roots within the pots. The results showed that region a had the largest average root volume proportion (29.72%), primarily due to the substantial volume contribution of the taproot. Region d followed with an average proportion of 27.26%, resulting from root coiling and entanglement at the pot bottom caused by the spatial constraints of the seedling tray. The middle regions of the pot, b and c, showed average root volume proportions of 23.14% and 19.89%, respectively. To further investigate the influence of root system characteristics on root injury during seedling gripping, the seedlings were categorized into three types based on their taproot growth positions. A gripping experiment was conducted on these three seedling types using spatula-equipped needles. The results showed that the greatest root injury (12.67%) was observed in Type 1 seedlings, which had taproots located closest to the needle insertion point. In contrast, the least injury (4.09%) was found in Type 3 seedlings, characterized by centrally positioned taproots. Type 2 seedlings, with their taproots growing on the side (laterally away from the insertion point), sustained intermediate injury (5.45%). This was because their lateral positioning led to an uneven distribution of mechanical stress during gripping compared with Type 3 seedlings. A validation experiment conducted on an automated seedling retrieval platform confirmed the root injury analysis. The experimental results showed maximum root injury in Type 1 seedlings (14.16%), followed by Type 2 (6.03%) and Type 3 (4.82%) seedlings, with a successful retrieval rate of 95.29%. These findings were consistent with the Micro-CT analysis. This study could provide a theoretical foundation for low-injury seedling gripping in fully automated seedling transplanters.
Why it matches plant phenotyping methodsMicro-CT、3D再構成、watershed分割を用いて苗の根系形態を定量化する手法が研究の中心であり、根容積分布と根傷害の評価まで検証している。
abstractMicro-CT was employed to scan the seedling pots. After three-dimensional (3D) reconstruction was conducted on the data acquired from the pot scans, the 3D model of the root system was segmented and extracted using the watershed algorithm.
During the 3D reconstruction of strawberry plants, methods based on 3D Gaussian Splatting (3DGS) face significant challenges due to motion-induced image blur. Such blurring substantially reduces the feature matching accuracy in Structure from Motion (SfM) algorithms and compromises the reliability of camera pose estimation, thereby degrading the quality of subsequent 3DGS reconstruction. This ultimately manifests as geometric distortion and loss of texture details in the reconstructed models. The issue is particularly severe on the surface of strawberry fruits: under blurred image conditions, point cloud registration fails, resulting in the loss of high-frequency details in the high-density achene regions, which blurs seed contours and degrades reconstruction accuracy. To address this technical bottleneck, this study proposes an optimized reconstruction scheme integrated with 3DGS. By incorporating the Canny edge detection algorithm to filter h i gh-quality i n put i m ages, t h e a c curacy of the reconstructed model is significantly improved. The optimized approach achieves remarkable results on the strawberry plant dataset: the average Peak Signal-To-Noise Ratio (PSNR) of the 3DGS model reaches 35.99, representing a 15.2% improvement over the baseline 3DGS. The morphology of high-density achenes on the fruit surface is clearly distinguishable, supporting the accurate monitoring of phenotypic parameters in strawberry plants.
Why it matches plant phenotyping methodsイチゴ植物の3D再構成精度を向上させる画像処理・3DGS手法を開発し、果実表面形態などの表現型パラメータ監視に直接利用するため、方法開発が中心である。
abstractTo address this technical bottleneck, this study proposes an optimized reconstruction scheme integrated with 3DGS.
The usage of photogrammetric technologies is essential in the concept of precise forestry. Dense unmanned laser scanning (ULS) point clouds are the innovative and precise data source for canopy height model (CHM) generation. It is necessary to choose the CHM generation method and its settings appropriately. This study evaluated different CHM generation methods and aimed to select the optimal parameters for CHM generation based on dense ULS point clouds of a temperate forest in central Europe. The results show that the choice of method and settings influences the quality of parameters describing forest stands, such as tree height or volume, and determining the location of tree tops and 2D tree contours. The most accurate CHMs were generated using the pit-free method. This method provides the lowest differences between the reference values, which were evaluated using the proposed CHM quality index. The cell size of generated rasters had the most significant influence on the quality of CHM, regardless of the method. Among all variants, the optimal variant was selected with a spatial resolution of CHM of 20 cm and a number of height levels of 4 and no interpolation of values for areas without data. For coniferous forest, this variant has a mean tree top location error of 0.1 m, a mean tree top height error of 0.1 m, and a mean tree crown volume error of 8.5 m 3 . For deciduous forest, this variant has a mean tree top location error of 0.3 m, a mean tree top height error of 0.7 m, and a mean tree crown volume error of 40.8 m 3 .
Why it matches plant phenotyping methods森林樹冠高モデル（CHM）の生成法とパラメータを比較・最適化し、樹高・樹冠体積・樹頂位置などの植物形質を定量評価しているため、フェノタイピング手法が中心である。
abstractThis study evaluated different CHM generation methods and aimed to select the optimal parameters for CHM generation based on dense ULS point clouds of a temperate forest in central Europe.
SoybeanLaboratory / benchtopRootMorphology / geometry measurement2D/3D reconstructionSegmentationVisualization / data managementRoot system architecture
Root system analysis remains methodologically challenging in plant research: traditional soil cultivation obstructs comprehensive root observation, whereas hydroponic visualization lacks ecological relevance due to soil environment exclusion—a critical limitation for crops like soybean. This manuscript developed a cost-effective hybrid imaging system integrating transparent acrylic plates, semi-permeable membranes, and natural soil substrates with high-resolution imaging and controlled illumination, enabling non-destructive root monitoring in quasi-natural soil conditions. Complementing this hardware innovation, this manuscript proposed an unsupervised semantic segmentation algorithm that synergizes path planning with an enhanced DBSCAN framework, achieving the precise extraction of primary and lateral root architectures. Experimental validation demonstrated superior performance in soybean root analysis, with segmentation metrics reaching 0.8444 accuracy, 0.9203 recall, 0.8743 F1-score, and 0.7921 mIoU—significantly outperforming existing unsupervised methods (p 0.94) with WinRHIZO in quantifying root length, projected area, dimensional parameters, and lateral root counts confirmed system reliability. This soil-compatible phenotyping platform establishes new opportunities for root research, with future developments targeting multi-crop adaptability and complex soil condition applications through modular hardware redesign and 3D reconstruction algorithm integration.
Why it matches plant phenotyping methods根系観察用ハードウェアと画像セグメンテーション手法を開発し、根形質抽出性能を検証した、中心的な植物フェノタイピング研究である。
abstractThis manuscript developed a cost-effective hybrid imaging system
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the study's soybean root image data (the time-series NRMS dataset and scanner validation dataset used for phenotyping) in a public GitHub repository under the authors' account, matching an allowed URL. No separate analysis code availability is stated, so the资产Dataset · publicData Availability Statement: The data presented in this study are openly available in [GitHub] at
[https://github.com/xusiyue/RootPO_DBSCAN/tree/master/project_rootSystem/data (accessed
on 31 October 2025)].Open asset ↗GitHub · xusiyue/RootPO_DBSCANpdf-page:18 lines:1-58Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Drones are increasingly used in forestry to capture high-resolution remote sensing data, supporting enhanced monitoring, assessment, and decision-making processes. While operations above the forest canopy are already highly automated, flying inside forests remains challenging, primarily relying on manual piloting. In dense forests, relying on the Global Navigation Satellite System (GNSS) for localization is not feasible. In addition, the drone must autonomously adjust its flight path to avoid collisions. Recently, advancements in robotics have enabled autonomous drone flights in GNSS-denied obstacle-rich areas. In this article, a step towards autonomous forest data collection is taken by building a prototype of a robotic under-canopy drone utilizing state-of-the-art open source methods and validating its performance for data collection inside forests. Specifically, the study focused on camera-based autonomous flight under the forest canopy and photogrammetric post-processing of the data collected with the low-cost onboard stereo camera. The autonomous flight capability of the prototype was evaluated through multiple test flights in boreal forests. The tree parameter estimation capability was studied by performing diameter at breast height (DBH) estimation. The prototype successfully carried out flights in selected challenging forest environments, and the experiments showed promising performance in forest 3D modelling with a miniaturized stereoscopic photogrammetric system. The DBH estimation achieved a root mean square error (RMSE) of 3.33 - 3.97 cm (10.69 - 12.98 %) across all trees. For trees with a DBH less than 30 cm, the RMSE was 1.16 - 2.56 cm (5.74 - 12.47 %). The results provide valuable insights into autonomous under-canopy forest mapping and highlight the critical next steps for advancing lightweight robotic drone systems for mapping complex forest environments.
Why it matches plant phenotyping methods森林内ドローンとステレオ画像による3D計測・DBH推定を開発および性能評価しており、樹木形質の取得方法が研究の中心である。
abstractbuilding a prototype of a robotic under-canopy drone utilizing state-of-the-art open source methods and validating its performance for data collection inside forests
Vegetation vertical structure refers to the 3D distribution of vegetation aboveground biomass. Vegetation vertical structure of tropical forests influences other ecological and environmental variables that are essential for the functioning of the ecosystems. Integrating over 5.9 million Globel Ecosystem Dynamics Investigation (GEDI) LiDAR (Light Detection and Ranging) footprints, multispectral, and synthetic aperture radar (SAR) imagery, we built five national maps at 25 m resolution of five forest structural metrics for Colombia, South America, for the year 2020. We mapped canopy height, the height of half the cumulative returned energy from GEDI (RH50), total canopy cover, foliage height diversity, and total plant area index. The resulting maps tended to have the highest errors in the Amazon and Andean regions. Total cover had the highest relative error. Interrelationship curves between forest structural metrics of GEDI footprints are maintained across mapped metrics, indicating that the predictive models preserve structural relationships observed in GEDI data. Due to the medium-high spatial resolution and national coverage of the forest structural maps presented in this work, these maps will be useful for evaluating and mapping other ecological variables and conservation priorities in Colombia.
Why it matches plant phenotyping methodsGEDI LiDAR・マルチスペクトル・SARを統合し、森林キャノピー高、被覆率、葉群高多様性、植物面積指数などの植物構造形質を全国規模で推定・検証することが中心であり、単なる生態学的応用ではない。
abstractIntegrating over 5.9 million Globel Ecosystem Dynamics Investigation (GEDI) LiDAR (Light Detection and Ranging) footprints, multispectral, and synthetic aperture radar (SAR) imagery, we built five national maps at 25 m resolution of five forest structural metrics for Colombia, South America, for the year 2020.
Reproduction assets foundThe paper's resulting forest vertical structure maps (CH, COVER, FHD, PAI, RH50 for Colombia, 2020) are publicly available on Zenodo and via Google Earth Engine assets, and the authors' analysis code is publicly available on GitHub. These are paper-specific, public, actionable assets.Code · publicCode availability
The code is publicly accessible on Github76: https://github.com/CamiloFaguaUNAL/Forest_Structure_Colombia.Open asset ↗GitHubhtml-lines:731-755Plant phenotyping relevance match · UnverifiedOpenAlex · checked 13 Sept 2026
Digital agriculture is increasingly important for plant-level analysis, enabling detailed assessments of growth, nutrition and overall condition. Multispectral point clouds are promising due to the integration of geometric and radiometric information. Although RGB point clouds can be generated with commercial terrestrial scanners, multi-band multispectral point clouds are rarely obtained directly. Most existing methods are limited to aerial platforms, restricting close-range monitoring and plant-level studies. Efficient workflows for generating multispectral point clouds from terrestrial sensors, while ensuring geometric accuracy and computational efficiency, are still lacking. Here, we propose a workflow combining photogrammetric and computer vision techniques to generate high-resolution multispectral point clouds by integrating terrestrial light detection and ranging (LiDAR) and multispectral imagery. Bundle adjustment estimates the camera’s position and orientation relative to the LiDAR reference system. A frustum-based culling algorithm reduces the computational cost by selecting only relevant points, and an occlusion removal algorithm assigns spectral attributes only to visible points. The results showed that colourisation is effective when bundle adjustment uses an adequate number of well-distributed ground control points. The generated multispectral point clouds achieved high geometric consistency between overlapping views, with displacements varying from 0 to 9 mm, demonstrating stable alignment across perspectives. Despite some limitations due to wind during acquisition, the workflow enables the generation of high-resolution multispectral point clouds of vegetation.
Why it matches plant phenotyping methods植物の高解像度マルチスペクトル点群を生成するワークフローの開発と幾何精度評価が中心であり、植物の状態・生育評価に利用可能なフェノタイピング基盤に該当する。
abstractHere, we propose a workflow combining photogrammetric and computer vision techniques to generate high-resolution multispectral point clouds by integrating terrestrial light detection and ranging (LiDAR) and multispectral imagery.
Three-dimensional phenotyping technology is paramount in the field of peanut breeding and cultivation. The intricate topological structure of plants substantially complicates the development of effective peanut phenotyping technologies. In this study, we present the development of a point-cloud-based pipeline for three-dimensional phenotypic analysis of peanut plants. An efficient multi-view image acquisition system and three-dimensional reconstruction techniques were employed to generate point clouds of peanut plants. A dataset comprising 188 labelled samples of peanut point clouds was constructed for the development of semantic and leaf-instance segmentation models based on the transformer architecture. The segmentation accuracy of these models surpassed that of the conventional general segmentation techniques for plant point clouds. Based on the results of the segmentation, 11 three-dimensional phenotypic traits were automatically calculated at both the plant and leaf scales. Among these, five phenotypic traits, including plant height and leaf length, exhibited a mean absolute percentage error (MAPE) of less than 0.12 compared to the measured values. In addition, the Jensen-Shannon divergence (JS divergence) between the probability distributions of the three leaf phenotypic traits and their corresponding measured values was below 0.1. The three-dimensional phenotypic analysis pipeline developed in this study exhibited satisfactory generalisation capabilities, thereby offering an efficacious and expeditious high-throughput phenotyping analysis instrument for the intelligent breeding and cultivation of peanuts.
Why it matches plant phenotyping methodsピーナッツの3D画像取得、点群再構成、分割、形質自動算出を統合したフェノタイピングパイプラインの開発と精度検証が中心である。
abstractwe present the development of a point-cloud-based pipeline for three-dimensional phenotypic analysis of peanut plants
Abstract High‐throughput and noninvasive phenotyping methods are promising technology for improving efficiency in plant research and breeding. In this study, we evaluated the performance of a digital phenotyping system (DPS) based on three‐dimensional (3D) model reconstruction for quantifying key growth traits in rice ( Oryza sativa ). The DPS was used to estimate plant height, biomass, color, leaf morphology, and tiller angle in four rice varieties (Koshihikari, Nipponbare, PL9, and Tachiaoba). The results show high accuracy and correlation between manually measured and DPS‐derived traits. Notably, the 3D volume analysis can quantify biomass accumulation and growth dynamics and revealed distinct differences among varieties. The strong correlation between the green‐red normalized difference index (a red‐green‐blue‐based index) and soil plant analysis development also demonstrated the viability of the system in monitoring leaf color without using a multispectral instrument. The analysis also captured growth patterns over time, including canopy development and senescence, which are often challenging to quantify through manual measurements alone. Furthermore, the tiller angle estimation derived from DPS provided an alternative method to plant architecture evaluation, demonstrating its potential for use in breeding programs aimed to optimize canopy structure. These findings establish DPS as a reliable and scalable tool for a digital phenotyping platform that enables comprehensive trait analysis with reduced labor and increased precision and the capability to continuously monitor plant growth and biomass accumulation. This study shows the potential of this novel digital tool for automating manual measurements, which can increase efficiency and expedite research and breeding in rice and other crops.
Why it matches plant phenotyping methods3Dモデル再構築に基づくデジタル表現型解析システムを開発・評価し、イネの複数形質を手測定と比較検証しているため、方法が研究の中心です。
abstractwe evaluated the performance of a digital phenotyping system (DPS) based on three‐dimensional (3D) model reconstruction for quantifying key growth traits in rice
Reproduction assets foundThe paper's data availability statement explicitly says the analysis code is openly available on GitHub at the authors' repository Rice_VTGa.O, which contains the digital phenotyping/leaf-tracing analysis code for this study. No phenotype dataset or image deposit is stated.Code · publicGrant Number 39 [2023] and 38 [2024]),
and Microbiome and Metabolome Control Project, University
of Miyazaki, Japan.
C O N F L I C T O F I N T E R E S T S TAT E M E N T
The authors declare no conflicts of interest.
DATA AVA I L A B I L I T Y S TAT E M E N T
Codes used for analysis in this study are openly available on
GitHub at https://github.com/sandysan42/Rice_VTGa.O RC I D
SorawichPongpiyapaiboon https://orcid.org/0000-0002-9314-8375
Kenji Aoki https://orcid.org/0000-0001-7003-1994
MasatsuguHashiguchi https://orcid.org/0000-0003-0637-2780
RyoAkashi https://orcid.org/0000-0002-5651-8285
Yuji Kishima https://orcid.org/0000-0002-0942-3371
Hidenori Tanaka https://orcid.org/0000-0002-4237-8154Open asset ↗https://github.com/sandysan42/Rice_VTGa.O · Rice_VTGa.Opdf-raw-page:13 lines:1-84Code / dataset availability confirmedCrossref · checked 14 Sept 2026
SoybeanLiDAR / point cloudRootMorphology / geometry measurement2D/3D reconstructionRoot system architecture
Abstract Accurate phenotyping of root traits is essential for understanding how plants respond to varying soil water treatment conditions, yet traditional phenotyping methods are often destructive and limited in capturing the full three‐dimensional (3D) complexity of root systems. Existing two‐dimensional imaging techniques and advanced 3D methods for performing root phenotyping, like magnetic resonance imaging or computed tomography, either compromise on resolution, are cost‐prohibitive, or lack scalability. To address these limitations, this study proposes fringe projection profilometry (FPP), a rapid, nondestructive 3D imaging method, for root phenotyping. Using FPP, two architectural root traits were extracted: the number of root tips and the volumetric occupancy of the root system. These traits, difficult to obtain through manual phenotyping or conventional imaging, were automatically derived from the FPP 3D point clouds and validated against expert‐assigned fibrosity scores serving as the biological reference. The study involved 36 soybean ( Glycine max (L.) Merr.) plants from six genotypes, pre‐classified as either stress‐treated or grown under rain‐fed conditions. Results showed strong alignment between FPP‐derived traits and expert evaluations. Stress‐ treated plants consistently exhibited more root tips and greater volumetric occupancy, confirming the biological relevance of these metrics. While this study does not attempt to classify drought tolerance directly, the structural variations observed under drought stress may serve as a foundation for identifying stress‐responsive phenotypes in future work. Overall, the findings demonstrate that FPP provides a fast, scalable, and accurate tool for 3D root phenotyping under variable water conditions.
Why it matches plant phenotyping methodsFPPによる根系の3次元形質取得・自動抽出を開発し、専門家評価と検証した研究であり、フェノタイピング手法が中心です。
abstractthis study proposes fringe projection profilometry (FPP), a rapid, nondestructive 3D imaging method, for root phenotyping.
Reproduction assets foundThe paper's Data Availability Statement provides a public Google Drive link to the datasets generated and/or analyzed in this soybean root FPP phenotyping study, which is an allowed URL. No author analysis code is explicitly deposited.Dataset · publicying and Overcoming Weaknesses via Breed-
ing, Genomics, Phenomics and Physiology).
C O N F L I C T O F I N T E R E S T S TAT E M E N T
The authors declare no conflicts of interest.
DATA AVA I L A B I L I T Y S TAT E M E N T
The datasets generated and/or analyzed dur-
ing the current research are available at Google
Drive link: https://drive.google.com/file/d/1BJ4yq8QEWY3E5qQIQmYcOXHhEYn1zTE-
/view?usp=sharing
O RC I D
JiaqiongLi https://orcid.org/0009-0006-2247-425X
ZengluLi https://orcid.org/0000-0003-4114-9509
BeiwenLi https://orcid.org/0000-0001-8130-7730
R E F E R E N C E S
Balasubramaniam, B., Li, J., Liu, L., & Li, B. (2023). 3D imaging with
fringe projection for food and agriculturalOpen asset ↗pdf-raw-page:17 lines:1-91Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · checked 15 Sept 2026
The selection of sensors for a high-throughput plant phenotyping platform is crucial for its subsequent development. It impacts the control mode, data storage and transmission, phenotype analysis algorithm and accuracy. This paper compared and evaluated the three-dimensional (3D) data acquisition performance of LiDAR, Multi-View Stereo (MVS) reconstruction, and depth image synthesis in five growth stages of maize canopies. The study found that LiDAR was the most stable and least affected by the environment. Additionally, it had the highest plant height estimation accuracy, with an average R 2 of 0.80 across all five stages. However, LiDAR is greatly affected by the stationarity of the platform and the noise of the resulting maize point cloud can be significant. The sensor required for MVS mode is low-cost, has minimal influence on platform stationarity, and allows for convenient point cloud synthesis and colour information. However, it is greatly affected by the lighting environment, resulting in a certain degree of distortion in the obtained point cloud. Additionally, it has the highest pre-processing complexity. Depth point cloud has the highest synthesis efficiency and the lowest data pre-processing complexity, making it suitable for online pre-processing and analysis. However, the initial data obtained is large and its stability is low due to its susceptibility to environmental factors. The point cloud acquired by MVS and Depth are clearer than LiDAR, making it easier for plant segmentation. This study provides a valuable foundation for the development of a high-throughput plant phenotyping platform and sensor selection.
Why it matches plant phenotyping methods植物表現型プラットフォーム向けに複数の3D取得センサーを比較評価し、トウモロコシの草丈推定精度や点群品質を検証しているため、取得・解析手法が中心です。
abstractThis paper compared and evaluated the three-dimensional (3D) data acquisition performance of LiDAR, Multi-View Stereo (MVS) reconstruction, and depth image synthesis in five growth stages of maize canopies.
Existing tasks such as fruit growth monitoring, harvesting, and quality sorting still suffer from low precision and insufficient automation. 3D reconstruction technology can accurately capture the external characteristics of fruits and shows great potential for enhancing automated fruit detection and processing. This paper, guided by two core application needs in the fruit industry: real-time online sensing and offline high-precision analysis, provides a detailed overview of research progress in 3D reconstruction technology for fruits. It first introduces the principles, workflows, and advantages and limitations of classical 3D reconstruction methods. Then, it focuses on the basic framework of learning-based 3D reconstruction approaches, their improvement directions, and their applications in fruits and other agricultural products. In addition, the challenges encountered in fruit 3D reconstruction, such as occlusion and complex lighting conditions, are summarized, along with potential solutions. Finally, future research directions are discussed. This review serves as a valuable reference for promoting the integration of computer vision and agricultural intelligence and advancing the fruit industry chain’s digital and intelligent transformation.
Why it matches plant phenotyping methods果実の外部形質を取得する3D再構成手法を中心に、原理・ワークフロー・限界・応用・課題を体系的にレビューしており、植物フェノタイピング手法のレビューに該当する。
titleProcess, challenges and solutions of fruit 3D reconstruction: A review
Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 15 Sept 2026
Plant phenotyping plays a pivotal role in understanding plant traits and their interactions with the environment, making it crucial for advancing precision agriculture and crop improvement. 3D reconstruction technologies have emerged as powerful tools for capturing detailed plant morphology and structure, offering significant potential for accurate and automated phenotyping. This paper provides a comprehensive review of the 3D reconstruction techniques for plant phenotyping, covering classical reconstruction methods, emerging Neural Radiance Fields (NeRF), and the novel 3D Gaussian Splatting (3DGS) approach. Classical methods, which often rely on high-resolution sensors, are widely adopted due to their simplicity and flexibility in representing plant structures. However, they face challenges such as data density, noise, and scalability. NeRF, a recent advancement, enables high-quality, photorealistic 3D reconstructions from sparse viewpoints, but its computational cost and applicability in outdoor environments remain areas of active research. The emerging 3DGS technique introduces a new paradigm in reconstructing plant structures by representing geometry through Gaussian primitives, offering potential benefits in both efficiency and scalability. We review the methodologies, applications, and performance of these approaches in plant phenotyping and discuss their respective strengths, limitations, and future prospects (https://github.com/JiajiaLi04/3D-Reconstruction-Plants). Through this review, we aim to provide insights into how these diverse 3D reconstruction techniques can be effectively leveraged for automated and high-throughput plant phenotyping, contributing to the next generation of agricultural technology.
Why it matches plant phenotyping methods植物フェノタイピング向け3D再構成手法を体系的にレビューし、方法論・応用・性能を扱うことが中心であるため。
abstractThis paper provides a comprehensive review of the 3D reconstruction techniques for plant phenotyping, covering classical reconstruction methods, emerging Neural Radiance Fields (NeRF), and the novel 3D Gaussian Splatting (3DGS) approach.
With the continuous progress in micro-optical machine technology, miniature spectral imaging devices have been rapidly developed; however, three-dimensional (3D) imaging measurement technology has become increasingly mature and widely used. The evolution of these technologies has established a robust foundation for the integration of three-dimensional imaging and spectral information. To achieve accurate alignment between 3D data and spectral information to obtain a more comprehensive spectral representation of objects in 3D space, we developed a binocular multispectral stereo imaging (BMSI) system. This system acquires images in synchrony with a binocular multispectral imager, thereby ensuring accurate alignment between 3D data and spectral data at the pixel level and facilitating the construction of a four-dimensional (4D) dataset. The segmentation of leaf regions from shadow backgrounds in two distinct plant species was achieved through optimal band fusion and hue-saturation value (HSV) color space transformation, significantly improving the segmentation accuracy, processing efficiency, and robustness across different plant species. A systematic evaluation was conducted to quantify the reconstruction precision and system stability at different measurement distances. The designed system acquired 4D image spectral data with plants as the objects to be tested. The distribution characteristics of chlorophyll (Chl) on the 3D surface of plants were obtained by first-order derivatives of the spectral data and the normalized difference red edge (NDRE) index. This technique provides a new means for plant phenotyping research and a more effective technical approach for the digitalization and precision monitoring of the agricultural industry.
Why it matches plant phenotyping methods植物の3D・マルチスペクトル画像取得、葉領域分割、再構成精度・安定性評価を中核とする新規フェノタイピングシステムの開発研究である。
abstractwe developed a binocular multispectral stereo imaging (BMSI) system.
Reproduction assets foundThe paper's data availability statement explicitly deposits raw data and essential source code for the BMSI plant phenotyping analysis on a public GitHub repository.Code · publicThe raw data and the essential parts of the source code have been uploaded to Github: https://github.com/wwxsoul1234/BMSI/tree/master.Open asset ↗wwxsoul1234/BMSI · BMSIhtml-lines:278-306Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Dec 2025Computers and Electronics in Agriculture.
Multispectral image analysis is an effective way to detect crop growth status. However, the complexity of manufacturing process and technology of multispectral image acquisition equipment make data acquisition expensive. Therefore, a method based on a window-adaptive spatial-spectral attention transformer is proposed to reconstruct multispectral images using RGB images of maize. First, RGB and hyperspectral images of the maize are obtained, and the reflectance data from classic and preferred band combinations are extracted from the hyperspectral image. Then, a transformer model is constructed to evaluate and compare the reconstruction efficacy of the 5-band and 10-band combinations across four attention modes: spatial, spectral, spatial-spectral, and window-adaptive spatial-spectral attention. The best-performing reconstruction results are selected and compared with the original data from three perspectives: image, spectrum, and model effect. The 10-band multispectral image reconstructed by the window-adaptive spatial-spectral attention mechanism is highly similar to the original image, with a reflectance correlation exceeding 0.99. Furthermore, its application in monitoring crop growth status (i.e., maize chlorophyll) yields results closely aligned with actual reflectance data: RC² is 0.76, RV² is 0.64, while RMSEC and RMSEV are 3.63 mg/L and 2.94 mg/L, respectively. To further explore the model performance, the new sensitive bands are selected to be reconstructed in the maize V7 stage. The results from the chlorophyll content prediction model are as: RC² is 0.64, RV² is 0.60, with RMSEC and RMSEV are 5.61 mg/L and 5.62 mg/L, respectively. Therefore, the window-adaptive spatial-spectral attention transformer can accurately reconstruct multispectral images and establish precise growth status monitoring models, providing technical support for low-cost field maize growth detection.
Why it matches plant phenotyping methodsRGB画像からマルチスペクトル画像を再構成する手法を開発・評価し、トウモロコシのクロロフィル量（生育状態）推定に適用しているため、植物表現型取得・推定法が中心である。
abstracta method based on a window-adaptive spatial-spectral attention transformer is proposed to reconstruct multispectral images using RGB images of maize.
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
Under the dual pressures of food security and sustainable agricultural development, rapid and simultaneous detection of multiple crop growth parameters has become a core technological requirement for optimizing field management and improving resource utilization efficiency. UAVs carrying one or more sensors to collect of different crop growth parameters have achieved remarkable results in the field of single morphological or physiological parameter analysis. However, existing low-cost devices often failed to collect 3D geometric data and high-resolution spectral information simultaneously in field conditions, while the different nature and data structure of point cloud and spectral data brought special challenges to data fusion, restricting the ability of simultaneous multi-parameter resolution. Facing such challenges, in this paper, we design and develop a system that can take into account the simultaneous acquisition of 3D geometric data and high-resolution spectral information in field conditions through multi-sensor fusion and deep learning algorithm innovation. Based on a mature color point cloud data structure, we combine RGB cameras and laser radar sensors to fuse RGB images and point cloud data. We improved a spectral reconstruction network, construct a dedicated chlorophyll response sensitive band dataset for training, and reconstructed hyperspectral images with 36 channels in the 500–850 nm band range from RGB images, which greatly reduces the cost of the spectral information acquisition device. Experiments show that the SAM (Spectral Angle Mapper) value between the reconstructed hyperspectral data and the original hyperspectral data is less than 0.03. Finally, the growth parameters of crops are estimated using spectral and point cloud data. The developed equipment was calibrated and tested, and experimental data were collected under real field conditions for plant height (PH), leaf area index (LAI), and chlorophyll content estimation. The experimental results showed that the system could accurately analyze maize PH and LAI with Rt2 of 0.98 and 0.97, respectively, and that the chlorophyll content analysis capability was at the same level as that of other studies that have used UAV-mounted hyperspectral cameras for leaf chlorophyll content (LCC) detection, and the established estimation model Rt2 reached 0.66. The canopy chlorophyll content (CCC) of maize could be accurately estimated by fusing the data, and the Rt2 reached 0.95.
Why it matches plant phenotyping methodsRGB画像・LiDAR融合と深層学習による3D形状およびハイパースペクトル情報の再構成システムを開発し、圃場で植物形質推定を校正・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 design and develop a system that can take into account the simultaneous acquisition of 3D geometric data and high-resolution spectral information in field conditions through multi-sensor fusion and deep learning algorithm innovation.
We present the first application of geometry-based relationship constraints for point-cloud registration and unsupervised 3D reconstruction of tree structure in semi-arid forest using unmanned aerial vehicle (UAV) photogrammetry. Accurate three-dimensional (3D) reconstruction of tree structure is essential for a plethora of subsequent tasks like assessing ecosystem health and informing sustainable forest management strategies, in particular over ecologically sensitive arid and semi-arid ecosystems that increasingly face decline due to prevalence of environmental stressors. This highlights the need for high-resolution geospatial monitoring approaches. While UAV-based photogrammetry offers a flexible and cost-effective means of capturing forest structure, conventional top-of-canopy imaging fails to sufficiently represent critical under-canopy features, including stem morphology and lower crown structure. Here, we suggest an integrated 3D reconstruction framework that combines dual-layer UAV photogrammetry, acquiring data from both above and below the canopy, with an innovative geometry-based point cloud registration method. Unlike conventional approaches like Iterative Closest Point (ICP) and Random Sample Consensus (RANSAC), this method leverages spatial relationships among individual trees to robustly align multi-view point clouds acquired under occluded and variable conditions. To further refine the reconstructed tree models, we suggest an updated unsupervised Generative Adversarial Network (Denoise-GAN), enabling both noise reduction and structural completion without reliance on labeled training data. The resulting models were used to extract key phenotypic features with high accuracy compared to reference data (root collar diameter (DRC) R² = 0.93, height R² = 0.97,Crown area R² = 0.99, number of stems R² = 1), providing vital indicators for quantifying forest structure and health. The presented methodology not only enhances the completeness and accuracy of 3D tree reconstruction in semi-arid forest, but also represents a significant advancement toward a scalable, data-driven semi-arid forest monitoring system. This workflow offers substantial potential for ecological applications, particularly in degraded and topographically complex ecosystems.
Why it matches plant phenotyping methodsUAV画像からの3D樹木再構成、点群登録、ノイズ除去・構造補完を開発し、樹木形質の抽出精度を検証しているため、植物フェノタイピング手法が中心である。
abstractWe present the first application of geometry-based relationship constraints for point-cloud registration and unsupervised 3D reconstruction of tree structure in semi-arid forest using unmanned aerial vehicle (UAV) photogrammetry.
In-orchard apple size grading remains challenging under occlusions, variable illumination, and irregular fruit morphology. We present a contact–curvature strategy that integrates bending sensors into fin–ray flexible fingers to estimate fruit size during grasping, unifying grasp–and–grade operations. Neural Radiance Fields (NeRF) reconstruction provides offline ground-truth curvature for calibration, yielding strong linear agreement between sensor readings and true curvature (R2=0.9852). Using temporal curvature features across approach, grasp, and steady phases, a gradient-boosting regression model predicts fruit diameter with R2=0.9577 and RMSE = 1.19 mm on the test set. In laboratory conditions, the system achieved an overall grading accuracy of 98.0 % for 200 apples classified into four grades, with a processing capacity of approximately 6 apples·min⁻¹, meeting real-time requirements. In a small-scale orchard pilot study, the system maintainedR2=0.94 andRMSE=1.27 mm, achieving 96 % grading accuracy versus 77 % for a camera-only approach. Compared with vision-only sizing methods, contact-curvature sensing demonstrates inherent robustness to occlusion and illumination while better tolerating morphological irregularities. A methylene–blue protocol confirmed non–destructive operation. Contact–curvature sensing is robust to occlusions/illumination and can, in principle, extend to other near–spherical crops.
Why it matches plant phenotyping methods果実径という植物器官形質を、接触・曲率センサーと回帰モデルで推定する手法を開発し、校正・精度検証・圃場評価まで行っており、表現型取得法が研究の中心である。
abstractWe present a contact–curvature strategy that integrates bending sensors into fin–ray flexible fingers to estimate fruit size during grasping, unifying grasp–and–grade operations.
Hyperspectral imaging (HSI) has recently emerged as a valuable tool for various agricultural applications. However, the widespread adoption of hyperspectral imaging is hindered due to the high cost and complexity of collecting and processing hyperspectral images. To address this gap, we introduce Agro-HSR,¹1Link to dataset: Agro-HSR. a large-scale RGB to hyperspectral image reconstruction dataset of sweet potatoes, specifically curated to promote easy access to hyperspectral images for the agricultural community. Agro-HSR comprises 1322 pairs of RGB and hyperspectral image cubes from 790 samples across three sweet potato varieties. For 141 of these samples, the agro-product quality attributes are included in the dataset. Each hyperspectral image cube covers 31 evenly spaced bands within the wavelength range of 400–1000 nm. Benchmarks for hyperspectral image reconstruction were conducted to demonstrate the importance and applicability of Agro-HSR. These benchmarks evaluated the ability to predict critical quality parameters in sweet potatoes, including Brix, dry matter, and firmness, from reconstructed hyperspectral images. Agro-HSR enhances the accessibility of hyperspectral images and promotes opportunities for cross-domain research in deep learning and agricultural science, addressing critical challenges in assessing the quality of agro-products.
Why it matches plant phenotyping methodsサツマイモのハイパースペクトル画像再構成データセットを構築し、再構成画像から品質形質を推定するベンチマークを実施しており、植物形質取得・推定手法が中心である。
abstractBenchmarks for hyperspectral image reconstruction were conducted to demonstrate the importance and applicability of Agro-HSR.
Unmanned Aerial Vehicles (UAVs) equipped with low-cost RGB and near-infrared (NIR) cameras represent efficient and scalable technology for monitoring sugarcane crops. This study evaluated the potential of UAV imagery and three-dimensional crop modeling to estimate sugarcane height and yield under different nitrogen fertilization levels. The experiment comprised 28 plots subjected to four nitrogen rates, and images were processed using a Structure from Motion (SfM) algorithm to generate Digital Surface Models (DSMs). Crop Height Models (CHMs) were obtained by subtracting DSMs from Digital Terrain Models (DTMs). The most accurate CHM was derived from the combination of the reference DTM and the NIR-based DSM (R2 = 0.957; RMSE = 0.162 m), while the strongest correlation between height and yield was observed at 200 days after cutting (R2 = 0.725; RMSE = 4.85 t ha−1). The NIR-modified sensor, developed at a total cost of USD 61.59, demonstrated performance comparable with commercial systems that are up to two hundred times more expensive. These results demonstrate that the proposed low-cost NIR sensor provides accurate, reliable, and accessible data for three-dimensional modeling of sugarcane.
Why it matches plant phenotyping methodsUAV画像、SfMによる3次元再構成、低コストNIRセンサーを用いてサトウキビの草高・収量を推定し、商用システムとの性能比較も行うため、植物表現型取得法が中心である。
abstractUAV imagery and three-dimensional crop modeling to estimate sugarcane height and yield
Sustainable vineyard management requires precise and efficient application of plant protection products to minimise environmental impact while ensuring plant health. This study presents a variable-rate spraying system that integrates an RGB-D camera with a GPU-equipped edge computing platform to enable accurate, real-time adjustment of spray flow rates in vineyards. A tensor-based representation of RGB-D data is employed to accelerate the entire processing pipeline. Based on this structure, a fast approximate meshing method is applied to rapidly generate 3D meshes from point clouds. To incorporate semantic information from RGB images, an instance segmentation model is used to detect grapevine canopies and trellis posts. The resulting canopy masks are used to isolate the canopy meshes, while the trellis posts serve as reference planes for canopy volume estimation via mesh projection. Based on the computed volume, pulse-width modulation signals are generated to dynamically control spray flow rates. Field experiments were conducted to evaluate the system’s effectiveness and real-time performance. The results demonstrated that the estimated canopy volume is a reliable indicator for regulating application rates. Compared to uniform-rate spraying, the proposed system reduced plant protection product consumption by 57.4% while ensuring adequate droplet coverage. Additionally, the system demonstrated satisfactory real-time performance even on entry-level hardware. Overall, the proposed variable-rate spraying system offers an accurate, real-time, and cost-effective solution for precision viticulture, highlighting its potential for commercial deployment in sustainable vineyard management.
Why it matches plant phenotyping methodsRGB-D画像からブドウ樹冠を分離し、3Dメッシュ投影で樹冠体積という植物形質を推定する技術が中心であり、リアルタイム性能と散布制御への有効性も評価している。
abstractan instance segmentation model is used to detect grapevine canopies and trellis posts
Satellite-derived solar-induced chlorophyll fluorescence (SIF) provides critical insights into large-scale ecosystem functions. However, inherent trade-offs between satellite scan range and spatial resolution, coupled with incomplete coverage and irregular temporal sampling, constrain its utility for fine-scale ecological studies. In this study, we present a monthly 500-meter resolution SIF dataset for China (CNSIF, 2003–2022), reconstructed using a deep learning framework integrating high-resolution Landsat/Sentinel-2 surface reflectance and thermal infrared data. CNSIF accurately captures spatial patterns of vegetation photosynthetic activity and reveals a significant annual growth trend (0.054 mW m⁻² sr⁻¹ nm⁻¹ year⁻¹). Validation against tower-based SIF demonstrates its ability to track monthly photosynthetic dynamics across diverse ecosystems, with R² ranging from 0.324 (p < 0.01) to 0.947 (p < 0.001). A strong correlation with tower-based GPP (R² = 0.55, p < 0.001) further highlights its utility for carbon flux estimation. Comparative analyses show CNSIF’s superiority over existing high-resolution SIF products in resolving fragmented landscapes, reducing spatial artifacts, and improving delineation of fine-scale features (e.g., winter wheat fields, urban boundaries) in heterogeneous ecosystems. CNSIF's higher-resolution estimation of photosynthetic activity offers a promising tool for monitoring vegetation dynamics and assessing fragmented agricultural production. It enables the incorporation of ecosystem fragmentation effects into earth observation and carbon cycle systems. CNSIF is publicly available at https://doi.org/10.6084/m9.figshare.27075145.
Why it matches plant phenotyping methods高解像度SIFの再構成手法と公開データセットを開発し、タワー観測およびGPPで検証しており、植生の光合成活動という生理状態の推定が中心である。
abstractwe present a monthly 500-meter resolution SIF dataset for China (CNSIF, 2003–2022), reconstructed using a deep learning framework integrating high-resolution Landsat/Sentinel-2 surface reflectance and thermal infrared data.