This study systematically evaluated the contribution of UAV LiDAR structural features such as crop height (CH) and multi-layer gap fraction (GF) and the amplitude of the returning signal represented by normalized intensity (INT), together with multispectral (MS) and thermal infrared (TIR) observations for aboveground biomass (AGB) estimation in winter wheat using a common artificial neural network (ANN) framework. Among the evaluated single sensor approaches, LiDAR features consistently provided the strongest performance, demonstrating the complementary value of crop height, vertically distributed canopy density, and normalized LiDAR intensity for characterizing canopy structure and within-canopy variability. Multi-layer GF improved AGB estimation relative to conventional ground-based GF approaches, highlighting the importance of incorporating the vertical distribution of canopy density. Multi-sensor fusion produced only modest additional improvements, indicating limited benefits relative to the increased acquisition and processing requirements. Temporal analysis showed that structural LiDAR features were most informative during early crop development, whereas normalized intensity, spectral reflectance, and thermal observations became increasingly valuable during canopy maturation and senescence. Comparisons with destructively measured plant area index (PAI), leaf area index (LAI), green leaf area index (GLAI), and green fraction of LAI further demonstrated that normalized LiDAR intensity (903 nm) was more closely associated with green canopy components than purely structural LiDAR metrics. Overall, the results demonstrate that fully exploiting both the structural and spectral information contained within LiDAR observations can substantially improve UAV-based biomass estimation, while multispectral and thermal observations provide complementary information whose contribution varies with crop development and monitoring objectives.
Why it matches plant phenotyping methodsUAV LiDAR・マルチスペクトル・熱画像とANNを用いた小麦バイオマス推定手法を系統的に比較・評価しており、植物形質推定の取得・解析方法が研究の中心である。
abstractThis study systematically evaluated the contribution of UAV LiDAR structural features such as crop height (CH) and multi-layer gap fraction (GF) and the amplitude of the returning signal represented by normalized intensity (INT), together with multispectral (MS) and thermal infrared (TIR) observations for aboveground biomass (AGB) estimation in winter wheat using a common artificial neural network (ANN) framework.
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.
Salt stress can markedly alter seedling architecture, creating a need for non-destructive three-dimensional (3D) phenotyping methods capable of resolving fine plant structures. However, organ-level segmentation of plant point clouds remains challenging because of leaf overlap, slender stems, ambiguous stem–leaf boundaries, and severe class imbalance. In this study, we developed PTV-SegCo, a task-adapted Point Transformer model for organ segmentation and structural phenotyping of coriander seedlings under salt stress. PTV-SegCo integrates efficient channel attention, gated shallow–deep feature fusion, and a combined cross-entropy–Dice loss to improve representation of fine and minority organ structures. The dataset comprised 60 manually annotated 3D point-cloud samples from 12 cultivation trays repeatedly observed over five acquisition dates under six NaCl concentrations (0, 50, 100, 150, 200, and 250 mmol L −1 ). Because the earliest acquisition represented a particularly challenging developmental stage, these 12 samples were used as a fixed early-stage model-selection set, while samples from the remaining four dates were organized into four date-blocked training–validation configurations. Under this internal model-development protocol, PTV-SegCo achieved mean mAcc and mIoU values of 93.05% and 89.05%, respectively, and showed numerically higher performance than its direct backbone PTV-Seg50. These values should be interpreted as internal comparative results rather than as an unbiased estimate of generalization to unseen cultivation trays, and the present results should not be interpreted as establishing the broad competitiveness of PTV-SegCo against other point-based, convolution-based, graph-based, transformer-based, or plant-specific segmentation architectures. After semantic segmentation, reconstructed scenes were metrically calibrated using the known cultivation-tray dimensions, followed by individual-plant separation and quality control. Four reconstruction-derived structural descriptors—plant height, projected area, voxel occupancy volume, and leaf point ratio—were extracted to characterize temporal structural variation under different NaCl treatments. For treatment-level inference, individual-plant measurements were aggregated within each cultivation tray at each acquisition time, with the cultivation tray treated as the independent experimental unit. Independent manual validation showed close agreement for plant height and projected area, with R 2 values of 0.9969 and 0.986, respectively. Overall, the proposed workflow provides a feasible approach for organ-level segmentation and automated 3D structural analysis of small coriander seedlings under salt stress. The extracted descriptors primarily represent reconstruction-derived spatial characteristics and should not be interpreted as direct indicators of physiological status; voxel occupancy volume and leaf point ratio remain without direct external validation.
Why it matches plant phenotyping methods3D点群による器官セグメンテーションと構造形質抽出の手法開発・内部比較・手動検証が研究の中心であり、塩ストレスは適用対象である。
abstractwe developed PTV-SegCo, a task-adapted Point Transformer model for organ segmentation and structural phenotyping of coriander seedlings under salt stress.
Abstract Tree crowns are complex, three‐dimensional structures whose morphology varies among species, individuals and environments. Although light detection and ranging (LiDAR) provides high‐resolution, single‐tree point clouds that advance species discrimination and the assessment of intraspecific variation in situ, crown shape is still commonly reduced to low‐dimensional metrics (e.g. crown diameter or crown base height), losing much of its three‐dimensional geometric complexity. We introduce a fully 3D geometric morphometric framework that captures crown shape directly from LiDAR point clouds at both species and individual levels. Pre‐segmented LiDAR single‐tree point clouds of eight temperate forest species were converted into three‐dimensional shape representations using radial bounding volumes (RBVs), which partitioned each crown into a standardized set of vertical layers and radial sectors. Surface points automatically digitized from each RBV formed geospatially aligned, 3D pseudolandmark configurations representing geometric morphometric crown shapes. These configurations served as the input data for multivariate analyses of crown shape variation within and between species. Twelve structural traits, including crown and stem dimensions, were extracted from the same RBVs and integrated into analyses of trait–shape associations. The morphospace of crown shape was structured along different axes of variation in broadleaf species than in conifers. Within these groups, species pairs—such as Fagus versus Quercus and Picea versus Pinus —exhibited contrasting intraspecific morphological gradients, with different structural traits driving shape variation in each. Crown base height and total crown height emerged as the strongest predictors of crown shape. Differences in crown shape among species were primarily captured by symmetric components, with asymmetry providing a negligible signal. Interspecific differentiation was largely driven by architectural variation rather than pure size differences. Morphological differences derived from pseudolandmarks and convolutional neural network features exhibited stronger correlations in conifers than in broadleaf species. We present a reproducible, LiDAR‐native framework for quantifying and comparing 3D crown morphology within and across species. Using the RBV approach, geospatially aligned pseudolandmarks can be derived from any pre‐segmented, single‐tree LiDAR point cloud, enabling scalable, multi‐regional analyses of intraspecific variability. This framework provides a robust foundation for integrating crown shape into ecological, evolutionary, silvicultural and modelling studies, including assessments of environmental effects and architectural constraints.
Why it matches plant phenotyping methodsLiDAR点群から樹冠形状を抽出・定量化する3D幾何形態計測フレームワークを開発し、再現可能な植物形態計測手法として検証・適用しているため。
abstractWe introduce a fully 3D geometric morphometric framework that captures crown shape directly from LiDAR point clouds at both species and individual levels.
Abstract Purpose Evaluate how soil and canopy sensing can map within-block variability in tart cherry orchards and identify indicators robust enough for repeatable management decisions. Methods Soil apparent electrical conductivity (ECa) was mapped in spring 2022 across four commercial tart cherry blocks (8.5–10.5 ha; approximately 3,500 trees per block), followed by canopy sensing in 2023–2024. Canopy structure was measured using unmanned aerial vehicle (UAV) photogrammetry and mobile terrestrial laser scanning (MTLS) using light detection and ranging (LiDAR), and canopy density using mobile ceptometry. Spatial layers were aligned to per-tree grid cells. An August 2025 campaign compared UAV- and LiDAR-derived tree height with ground-truthed height. Results Soil-to-canopy relationships were weak to moderate but consistent within blocks ( r = 0.10–0.40), with strength and direction varying by site conditions. Canopy density was more strongly associated with UAV-derived volume than height. UAV-derived 90th-percentile height best predicted ground-truthed height ( R ² = 0.89; RMSE = 0.34 m), whereas LiDAR showed a weaker relationship and greater error ( R ² = 0.70; RMSE = 0.52 m). Cross-sensor agreement was moderate to strong ( r = 0.41–0.65). Per-tree rankings were stable between years for UAV height and volume. Conclusion Whole-block sensing revealed persistent spatial patterns that could support management-zone delineation. UAV photogrammetry provided accurate canopy metrics, MTLS offered measurements suited to routine orchard operations, ceptometry added seasonal canopy-density information, and ECa provided soil context. Occasional ECa mapping combined with strategically timed UAV surveys and other sensors as needed could reduce redundant sensing while supporting fertilizer evaluation, pruning, and labor allocation.
Why it matches plant phenotyping methodsUAVフォトグラメトリ、LiDAR、セプトメトリーによる樹冠の高さ・体積・密度の取得を中心に、地上実測との検証とセンサー間比較を行っているため、植物フェノタイピング手法の実質的な適用・検証に該当する。
abstractCanopy structure was measured using unmanned aerial vehicle (UAV) photogrammetry and mobile terrestrial laser scanning (MTLS) using light detection and ranging (LiDAR), and canopy density using mobile ceptometry.
High-throughput phenotyping depends on accurate 3D reconstruction of plants across growth stages, yet the development and evaluation of temporal completion methods are limited by the lack of datasets with complete geometric ground truth. To address this challenge, we introduce SynthCrop4D, a procedurally generated synthetic dataset of temporally evolving plant point clouds that provides controllable noise, occlusion, and complete plant geometry for benchmarking reconstruction methods. Using this dataset, we evaluate a two-stage pipeline that combines spatial denoising and temporal point cloud completion. First, a denoising module removes structural artifacts from raw laser-scanned point clouds. The resulting data are then processed by an Adaptive Temporal PoinTr model that reconstructs the current growth stage (t) using information from the previous stage (t-1), enabling recovery of regions missing due to self-occlusion. We evaluate the proposed framework on both SynthCrop4D and the real-world Pheno4D dataset (tomato and maize) under settings with and without denoising. Results show that denoising substantially improves reconstruction quality, with the best configuration achieving a Chamfer Distance of 0.0061 on SynthCrop4D (Temporal PoinTr + Mamba-DG) and an F-Score of 0.2080 on Pheno4D (Vanilla PoinTr + Mamba-DG). We further demonstrate the use of completed point clouds for phenotypic trait extraction, including plant height, canopy width, and convex hull volume, obtaining hull-volume MAEs of 0.021 on synthetic data and 0.343 on real data. Together, SynthCrop4D and the proposed pipeline provide a benchmark and methodology for temporal plant reconstruction and high-throughput crop phenotyping.
Why it matches plant phenotyping methods植物の3D点群再構成・時系列補完を開発し、合成データセットと実データで性能検証するとともに、草丈・群落幅・凸包体積を抽出する手法を中心に扱っている。
abstractwe introduce SynthCrop4D, a procedurally generated synthetic dataset of temporally evolving plant point clouds that provides controllable noise, occlusion, and complete plant geometry for benchmarking reconstruction methods.
Reproduction assets foundThe paper's authors explicitly state that source code, implementation details, and pre-trained model weights are publicly available on GitHub, and that the paper-specific SynthCrop4D synthetic dataset can be reproduced via scripts in that codebase. Pheno4D is a cited prior public dataset, not a paper-specific asset.Code · publicThe source code, implementation details, and pre-trained model weights for this study are publicly available on GitHub at https://github.com/Mrudul2006/3d_plant-reconstruction .Open asset ↗Mrudul2006/3d_plant-reconstructionlines:1724-1761Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · bioRxiv · checked 5 Sept 2026
Abstract Non-invasive, high-throughput phenotyping tools are needed that can identify environmental effects on plant structure and function to diagnose factors responsible for reduced growth in commercial and non-commercial settings. In this study, we explored whether the integration of 3D-multispectral (3D) and 2D-hyperspectral imaging (HSI), aided by machine learning (ML), could be used to identify environmental stress treatments imposed during plant growth. Controlled environment-grown Nicotiana Benthamiana plants were subjected to a range of abiotic treatments – including different growth irradiances, heat treatment and drought stress – with the treatments resulting in differences in shoot height, biomass, leaf area and spectral reflectance. ML models were trained to identify these treatments using morphological and spectral traits measured at 27, 29, 31, and 34 days after sowing (DAS). A 3D-multispectral scanner was used to obtain information on plant height, biomass, and leaf area. A visible and near-infrared (VNIR) HSI camera provided detailed spectral information for deriving spectral indices including the Normalised Difference Vegetation Index (NDVI), Photochemical Reflectance Index (PRI) and Normalized Difference Red Edge (NDRE). Manual measurements provided baseline comparative data. The 3D-multispectral scanner reliably estimated above-ground traits, with high correlations between manual and scanner-derived measurements. The ML models accurately differentiated among environmental stress treatments, with the fused 3D+HSI model achieving the best overall predictive performance across all evaluated metrics compared with models based on either imaging modality alone. Results demonstrated the effectiveness of combining 3D-multispectral and 2D-HSI data with ML analyses for non-destructive, high-throughput phenotyping. The integration of these techniques enabled non-destructive, high-throughput identification of environmental stress treatments imposed during plant growth.
Why it matches plant phenotyping methods3Dマルチスペクトル画像・ハイパースペクトル画像と機械学習を統合し、植物形態・スペクトル形質を非破壊かつ高スループットに取得・検証する方法が研究の中心である。
abstractNon-invasive, high-throughput phenotyping tools are needed that can identify environmental effects on plant structure and function
High-resolution monitoring of forest structure and productivity is essential for effective natural resource management. However, monitoring approaches such as field-based forest inventories or extensive lidar campaigns are costly, time-intensive, and spatially limited. Therefore, inexpensive and accessible methods are needed. SatCHM (Satellite Canopy Height Model) was developed to be an accessible and open-source tool for researchers, allowing for site-specific and temporally flexible predictions of canopy height with limited computational resources. SatCHM requires four inputs: panchromatic satellite imagery, solar and sensor angle metadata of satellite imagery, digital elevation models (DEMs), and lidar-produced CHMs for an area of interest. After SatCHM pre-processes inputs, data is loaded into a collection of convolutional neural networks (CNNs) for image-to-image regression. This ensemble cooperates to yield high-resolution predictions (up to 0.5-meter) of three-dimensional tree structure with discernible tree crowns across a broader defined area of interest. After calculating the mean absolute error for each prediction output, the median of these mean absolute errors was 6.06 meters.
Why it matches plant phenotyping methods森林キャノピー高と樹冠構造という植物形質を衛星画像等から推定するオープンソース手法を開発し、CNNによる推定と誤差評価まで行っており、植物フェノタイピング手法が研究の中心である。
abstractSatCHM (Satellite Canopy Height Model) was developed to be an accessible and open-source tool for researchers, allowing for site-specific and temporally flexible predictions of canopy height with limited computational resources.
Plant phenotyping relevance match · UnverifiedbioRxiv · Europe PMC · OpenAlex · checked 13 Sept 2026
Lodging in sorghum presents a significant challenge for plant breeders due to the trade-off between lodging resistance and grain yield. Manually measuring lodging across thousands of plots is time-consuming, expensive, and error-prone, making selection for lodging resistance challenging in breeding programs. Unmanned aerial vehicle (UAV)-derived metrics provide a potential high-throughput alternative; however, it remains unclear whether photogrammetric heights derived from UAV imagery can estimate plot-level lodging severity in large sorghum breeding trials. This study developed a framework for predicting plot-level lodging from UAV imagery across 2,675 sorghum breeding plots. Multi-temporal canopy height data were collected at two critical time points: maximum crop height and at manual lodging assessment. Height percentiles were extracted from UAV-derived point clouds generated using photogrammetric algorithms. These data were used to develop parametric, non-parametric, and ensemble prediction models, which were evaluated using three statistical metrics. The ensemble model, averaging predictions from all models, achieved the highest accuracy with Pearson correlations of r = 0.80-0.84 and lowest root mean square error (RMSE=16-18%), explaining 64-70% of variation in manual lodging counts. Model diagnostics and iterative refinement, including inspection of UAV imagery and dataset curation, had minimal impact on model performance, demonstrating the robustness of the approach. Model performance was consistent across sites, with minimal effects of stratified sampling on accuracy, confirming the ensemble approach as optimal for plot-level lodging assessment. This study demonstrates that integrated multi-temporal UAV imagery offers a practical alternative to labor-intensive manual evaluation methods by enabling high-throughput lodging assessment suitable for implementation in sorghum breeding programs.
Why it matches plant phenotyping methodsUAV画像と写真測量点群からソルガム区画の倒伏程度を推定する取得・解析フレームワークを開発し、実データで精度評価しており、植物表現型測定法が研究の中心である。
abstractThis study developed a framework for predicting plot-level lodging from UAV imagery across 2,675 sorghum breeding plots.
Introduction Accurate estimation of aboveground biomass (AGB) is essential for monitoring pasture productivity and supporting sustainable management of integrated crop–livestock (ICL) systems. We hypothesized that integrating multispectral, thermal, and canopy-structural information derived from unmanned aerial vehicles (UAVs) would improve AGB prediction relative to spectral information alone, and that Generalized Additive Models for Location, Scale and Shape (GAMLSS) would accommodate seasonal heteroscedasticity while maintaining predictive performance comparable to Random Forest (RF) and Support Vector Machine (SVM) models. Methods We collected 280 destructive biomass samples from two ICL paddocks and one continuously grazed pasture in the Brazilian Cerrado between 2022 and 2024. Twenty-four UAV-derived predictors, including spectral bands, vegetation indices, canopy surface temperature, and canopy height, were evaluated using repeated five-fold cross-validation. Model transferability was assessed by withholding one management paddock at a time. Results and discussion Under repeated five-fold cross-validation, GAMLSS achieved the lowest prediction error (R² = 0.69 ± 0.01; RMSE = 2.15 ± 0.04 Mg ha⁻¹), followed closely by SVM (R² = 0.68 ± 0.01; RMSE = 2.19 ± 0.03 Mg ha -1 ); RF showed lower accuracy (R 2 = 0.53 ± 0.01; RMSE = 2.63 ± 0.02 Mg ha -1 ). In the paddock-transferability assessment, GAMLSS also showed the lowest error (R 2 = 0.63 ± 0.04; RMSE = 2.34 ± 0.26 Mg ha -1 ). For GAMLSS, the complete multisensor configuration reduced RMSE by 6.2% compared with the spectral-only configuration. The selected model was used to generate spatially explicit maps of AGB and standing aboveground biomass carbon, estimated from the mean measured carbon concentration of forage biomass. Integrating multispectral, thermal, and structural UAV data with distributional regression improves AGB estimation and enables spatial monitoring of tropical pastures under contrasting management conditions.
Why it matches plant phenotyping methodsUAVのマルチセンサーデータと統計モデルを用いて牧草の地上部バイオマスを推定し、交差検証と圃場間移 transferability 評価を行っており、植物形質の取得・推定手法が研究の中心である。
titleUAV multisensor data and GAMLSS improve forage biomass estimation in Cerrado integrated crop–livestock pastures
To establish an accurate and interpretable prediction framework for soybean lodging grade and clarify the core regulatory traits and differentiated driving mechanisms of soybean lodging under high-density drip irrigation cultivation, 356 spring soybean germplasm accessions were used as experimental materials in this study. Morphological and mechanical traits including plant height (PH), stem pulling force (SPF), internode number (IN) and petiole length (PL) were measured over two consecutive years of field phenotyping. Two composite evaluation indices, plant height/stem pulling force ratio (PH/SPF) and plant height/internode number ratio (PH/IN), were further constructed. Four machine learning algorithms were adopted to develop multi-classification models for soybean lodging grade prediction. SHAP analysis combined with three global sensitivity approaches (perturbation analysis, Sobol’ method and Morris screening) was applied to decipher the regulatory patterns of key traits. The results showed that lodging grade significantly affected soybean grain yield and explained 25–28% of the phenotypic yield variation; yield reduction tended to plateau under severe lodging. Compared with single indicators such as SPF and PL, the two derived composite indices could stably distinguish soybean accessions with different lodging grades and exhibited stronger discriminatory power. Model comparison revealed that the XGBoost model achieved optimal prediction accuracy and generalization stability for lodging grade, with a weighted F1-score of 95.34% on the test set, significantly outperforming the conventional linear model. Interpretability analysis demonstrated that the PH/IN, PH, and PH/SPF acted as the primary positive traits promoting lodging, while SPF was the sole protective trait. Driving factors of lodging presented obvious gradient heterogeneity: mild lodging was dominated by the imbalance of plant architecture ratio, whereas severe lodging was governed by the cumulative effects of PH and IN. Strong interactions existed among all measured traits. The interpretable machine learning framework established in this study can provide theoretical support and technical references for lodging-resistant germplasm screening and targeted plant architecture regulation for densely planted soybean under drip irrigation systems.
Why it matches plant phenotyping methods大豆の倒伏状態を形態・力学形質から機械学習で推定し、モデル性能比較と解釈性解析を行う枠組みが研究の中心であり、単なる生物学的実験の routine 測定ではない。
abstractTo establish an accurate and interpretable prediction framework for soybean lodging grade and clarify the core regulatory traits and differentiated driving mechanisms of soybean lodging
Maize ( Zea mays L.) is a highly adaptable crop grown worldwide across diverse climates and management practices, with uses across multiple sectors. Consequently, the traits prioritized in maize research and breeding programs vary depending on the specific objectives. Core traits, however, such as flowering time, plant and ear height, and stalk and root lodging, which are important for evaluating and improving the performance and stability of maize genotypes, are routinely evaluated across breeding programs, regardless of their goals. Standardized measurement of these core traits is essential to ensure data reliability and comparability, enabling the integration of phenotypic data across different experiments. Such efforts ultimately support better decision-making and accelerate the development of improved maize genotypes. This is particularly important in public sector programs, where large-scale evaluations, critical for assessing the value of specific genotypes, are often only feasible through collaboration across programs. Here, we provide a protocol for the standardized collection of phenotypic data, specifically focusing on how to measure core traits in maize field trials. These methods promote consistency and accuracy in the evaluation of these traits, and support communication and coordination among groups in the public sector and other research settings. Further, such standardization facilitates the integration and comparison of data across programs, enabling robust longitudinal and multienvironment analyses.
Why it matches plant phenotyping methodsトウモロコシの主要形質を対象に、圃場試験での表現型データ収集を標準化するプロトコル自体を提示しており、測定方法が研究の中心である。
abstractHere, we provide a protocol for the standardized collection of phenotypic data, specifically focusing on how to measure core traits in maize field trials.
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.
Plant phenotyping is essential for modern crop breeding, yet traditional static image analysis fails to capture the nonlinear dynamics of plant growth. Existing time-series forecasting models exhibit notable limitations when processing multimodal data: global pooling operations may compress local 2D spatial topology of plants, and shallow feature concatenation may be insufficient for effective cross-modal semantic alignment. Moreover, current methods typically regress absolute morphological states, which may contribute to temporal lag during nonlinear growth spurts. In this paper, we propose ST-CrossGro-Former, a cross-modal residual forecasting framework for plant dynamic growth. The network removes the final global pooling and classification layers to preserve spatial topology and incorporates a scalar-guided cross-modal attention module based on the standard query-key-value formulation. This module utilizes 1D morphological features as queries to dynamically weight local visual regions, promoting multimodal feature alignment. Concurrently, a residual incremental forecasting strategy is introduced to predict short-term growth increments rather than absolute states, aiming to improve tracking sensitivity to sudden growth events. Evaluations on the UNL-CPPD maize dataset and supplementary validation on the FIP1 wheat field dataset show that the proposed model achieves competitive single-step forecasting accuracy and favorable temporal trajectory alignment compared with adapted spatiotemporal attention, graph-based, and physics-informed baselines under the evaluated settings. In particular, the FIP1 results suggest that ST-CrossGro-Former can maintain favorable height trajectory alignment under a field-acquired wheat setting, indicating its potential for helping mitigate temporal misalignment in dynamic growth forecasting.
Why it matches plant phenotyping methods植物の動的形態成長を予測する新規クロスモーダル手法を開発し、トウモロコシ・コムギデータセットで評価しており、表現型の抽出・予測手法が中心である。
abstractwe propose ST-CrossGro-Former, a cross-modal residual forecasting framework for plant dynamic growth.
Reproduction assets foundThe paper evaluates its ST-CrossGro-Former model on two public plant phenotyping datasets: the UNL-CPPD maize dataset (explicitly stated as publicly available with a repository URL) and the FIP1 wheat field dataset (public dataset from ETH Zürich, with its GigaScience dataset publication DOI). No author analysis code, Dataset · publicThe UNL-CPPD dataset used in this research was acquired from the UNL Plant Phenotyping Datasets repository, accessible at https://plantvision.unl.edu/datasets.Open asset ↗UNL Plant Phenotyping Datasets · UNL-CPPDlines:266-273Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
This study presents a multi-scale framework for reconstructing snow avalanche (SA) frequency and assessing vegetation structural responses in data-scarce mountain environments. The approach integrates dendrogeomorphological reconstructions, satellite-based spectral disturbance detection, and UAV-based Structure-from-Motion (SfM) photogrammetry, complemented by field data, and was applied to two avalanche paths in the Piatra Craiului Mountains (Southern Carpathians, Romania). Tree ring analyses allowed reconstruction of spatially explicit minimum avalanche chronologies for the 1980–2025 period. These reconstructions were combined with a DEM-based upslope algorithm to derive spatially variable avalanche return periods, revealing the highest frequencies in release and upper-track sectors and progressively longer return periods toward lower-track zones. Sentinel-2 imagery was used to assess the surface footprint of a reconstructed avalanche event in 2018. Among the tested spectral indices, the Moisture Stress Index (MSI) showed the most spatially coherent response, while the combined MSI-NDMI-NBR approach reduced index-specific noise. UAV-SfM photogrammetry supports high-resolution mapping of vegetation structure and surface states. Vegetation was classified using a machine-learning-based object-oriented approach (Random Forest) integrating spectral, geometric, structural, and textural parameters. The multi-parameter feature set yielded very high classification accuracy (Cohen’s Kappa ≈ 0.95). Across avalanche return-period gradients, both UAV-derived and field-based metrics showed a systematic associations between tree height and avalanche frequency, whereas tree age and stem diameter exhibited more variable, path-dependent responses. The proposed framework provides a transferable basis for linking avalanche disturbance regimes with vegetation structure and surface stability in mountain landscapes lacking long-term observational records.
Why it matches plant phenotyping methodsUAV-SfMと機械学習による植生構造・樹高の高解像度推定が研究枠組みの主要部分であり、分類精度も評価しているため、植物状態の画像ベース表現型計測として含める。
abstractUAV-SfM photogrammetry supports high-resolution mapping of vegetation structure and surface states.
Accurate estimation of pasture biomass is essential for determining cattle stocking rates and grazing durations. The objective of this study was to comparatively evaluate five sensor-based systems for estimating aboveground Bermudagrass (Cynodon dactylon) biomass and identify the leading sensing approach for continued development and broader validation. The five systems included Structure-from-Motion (SfM), Ultrasound Sensor and Ski (US-Ski), Inertial Measurement Unit and Ski (IMU-Ski), Inertial Measurement Unit and Roller (IMU-Roller), and Depth Camera (DC). These systems were deployed on unmanned aerial and ground vehicles to measure crop height under identical field conditions. Regression models relating measured crop height to wet biomass yield (WBY) were developed as a common calibration framework for statistically comparing sensor performance. These empirical allometric equations were intended to support comparative benchmarking of the sensing systems and were not developed as final operational biomass prediction models for immediate field deployment. The influence of vegetation coverage on yield predictions generated by the crop height-based equations was also examined. The results indicated that the IMU-Ski system demonstrated the strongest overall comparative performance (R2 = 0.97; SeY = 1112 kg-wet/ha), followed by the DC system (R2 = 0.97; SeY = 1132 kg-wet/ha). Based on its overall benchmarking performance, including calibration accuracy, residual error and simplicity, the IMU-Ski system was identified as the leading sensing approach for continued development and broader validation among the five evaluated methods. The results also indicated that addition of vegetation coverage into the crop height-based regression models did not significantly improve prediction accuracy under the experimental conditions evaluated.
Why it matches plant phenotyping methods複数のセンサーシステムによる牧草バイオマス推定を比較・校正・ベンチマークしており、植物形質の取得法と技術性能の評価が研究の中心です。
abstractcomparatively evaluate five sensor-based systems for estimating aboveground Bermudagrass (Cynodon dactylon) biomass
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.
Conventional methods of plant distance and volume measurements are limited by low efficiency, limited spatial coverage, and high measurement error. LiDAR and RGB-D imaging offer cost-effective, precise, and non-destructive techniques for plant distance and volume measurements. This study aimed to measure cabbage height, volume, and distance using LiDAR and RGB-D imaging. The sensors were mounted on a 1.6 kW electric field scouting platform (EFSP) for data collection. Point cloud (PCD) data were collected using LiDAR, whereas data processing, visualization, and measurements were done using commercial software and open-source programming scripts. A total of 20 cabbage plants were analyzed. LiDAR data processing included data frame screening, outlier removal, denoising, voxelization, and generation of 3D PCD density maps. Depth image processing included importing raw data and metadata shaping using intrinsic camera parameters, visualization, extraction of depth points, and pixel-level measurements of distances and volume. RGB image processing involved image conversion, segmentation, normalization, binary masking, mask cleaning, region extraction of cabbages, separation of ROI and preparation of contours, Delaunay triangulation and convex hull preparation, ROI overlay, bounding box preparation, sharing boundary between two boxes, conversion to pixel distances, and for visualization, plant height, volume measurements, and center to center distance measurement for measuring the plant distance. LiDAR demonstrated higher measurement accuracy for cabbage plant height, circumferential volume (geometric canopy volume), and plant distance, followed by RGB-D imaging, while RGB imagery showed comparatively lower performance under the study field conditions. Overall, LiDAR and RGB-D imaging provided reliable and non-destructive approaches for cabbage geometric characterization under field conditions, although accurately capturing complex plant geometry remains challenging. Positive and negative values of bias represent the over- and under-estimated results, respectively. Future studies should include larger and more diverse plant datasets exhibiting diversified size, shape, and geometric structure to further improve the robustness and general applicability of the proposed sensing approaches.
Why it matches plant phenotyping methodsLiDAR、RGB、RGB-Dを用いてキャベツの高さ・体積・株間距離を取得し、処理手順と測定精度を比較評価する手法研究であり、植物表現型取得が中心である。
abstractThis study aimed to measure cabbage height, volume, and distance using LiDAR and RGB-D imaging.
LiDAR (Light Detection and Ranging) and RGB-D camera imaging have emerged as essential tools in agricultural applications, particularly for plant size and distance measurements, enabling non-destructive, cost-effective, and precise estimation. The objective of this study was to measure the plant canopy dimensions and distance between apples using commercial LiDAR, and an RGB-D camera with a speed sprayer platform was used to determine whether LiDAR provides a higher measurement accuracy under field conditions. Data were collected in an apple orchard in Muju, Republic of Korea. Commercial 3D LiDAR, a terminal box, an RGB-D camera, a microcontroller, a power supply, and individual display monitors were integrated into a customized data acquisition (DAQ) box for LiDAR point cloud (PCD), RGB, and depth imagery data collection. Commercial software was used for data acquisition, data conversion (pcap to PCD), segmentation of regions of interest (ROI), and pre-processing of data. PCD processing and measurement consisted of data frame selection, data conversion, outlier removal, downsampling, denoising, ground point removal by filtering, voxelization, and density map generation using an open access programming language script. Depth image processing included importing raw data, shaping metadata using intrinsic camera parameters, visualizing depth images, extracting depth points, and measuring the plant canopy at the pixel level. RGB image analysis involved grayscale conversion, thresholding, segmentation of ROI, contour preparation, noise removal, and binary masking for eliminating the background. Estimated results were compared to measured results. LiDAR measurements showed the closest agreement with the measured results for plant height, canopy volume, plant spacing, and row distance, outperforming both RGB and depth imaging. Under field conditions, plant spacing and row distance were estimated with accuracies of 97.5% and 94.7%, respectively, exhibiting higher measurement accuracies than RGB and depth imagery data results. Despite some discrepancies due to complex plant geometry and dynamic data collection, the results support data collection strategies critical for precision horticulture.
Why it matches plant phenotyping methodsLiDARとRGB-D画像を用いたリンゴ樹の樹冠寸法・体積・樹間距離・列間距離の取得と精度比較が研究の中心であり、植物表現型計測手法の開発・検証に該当する。
abstractThe objective of this study was to measure the plant canopy dimensions and distance between apples using commercial LiDAR, and an RGB-D camera with a speed sprayer platform was used to determine whether LiDAR provides a higher measurement accuracy under field conditions.
Unmanned aerial vehicle (UAV) imagery can support plot-scale crop phenotyping, but spectral, RGB and structural predictors may contribute differently to different traits. We compared six predefined feature groups for predicting soybean SPAD and plant height (PH) in a 1.3 ha field experiment in Sanya, China. The field contained 6197 soybean planting plots, of which 234 had paired SPAD and PH measurements. Multispectral bands, vegetation indices (VIs), RGB descriptors and digital surface model (DSM) metrics were extracted from DJI Mavic 3 Multispectral imagery. Six regression algorithms were evaluated using random fivefold cross-validation, spatial block cross-validation and nested spatial cross-validation. Under random cross-validation, ExtraTrees with multispectral bands, VIs and RGB descriptors produced the numerically highest SPAD performance (R2 = 0.589; RMSE = 6.66), while BayesianRidge with multispectral bands, VIs and DSM metrics produced the highest PH performance (R2 = 0.760; RMSE = 7.14 cm). Nested spatial cross-validation yielded R2 = 0.473 and RMSE = 7.56 for SPAD and R2 = 0.690 and RMSE = 8.13 cm for PH. G4 was selected in four of the five outer folds for SPAD, although the selected algorithm varied, and G5 was selected in all five outer folds for PH. VIs improved prediction of both traits relative to the original bands. Adding RGB descriptors produced only a small and model-dependent improvement for SPAD, whereas adding DSM metrics produced a larger and more consistent improvement for PH. The complete feature set did not outperform G4 for SPAD or G5 for PH. The retained models were applied to all 6197 plots to map SPAD, PH and their field relative combinations. Because all of the validations used one field and one UAV acquisition date, the results describe performance within this experiment and do not establish transferability to other sites, years or growth stages.
Why it matches plant phenotyping methodsUAVマルチスペクトル・RGB・構造特徴からSPADと草丈を推定する特徴抽出および回帰手法を、複数の空間交差検証で比較・評価しており、植物表現型取得が研究の中心である。
titleTrait-Specific Contributions of UAV Multispectral, RGB and Structural Features to Soybean SPAD and Plant Height Phenotyping
Semi-arid rangelands support livelihoods and key ecosystem services, yet sustainable management depends on accurate and scalable monitoring of herbaceous aboveground biomass (AGB). Field-based measurements are spatially limited, while satellite-derived vegetation indices often perform poorly in complex savanna systems such as the Kalahari. Using unoccupied aerial vehicle (UAV) structure-from-motion (SfM) photogrammetry, we evaluate the ability of fine-scale canopy height and spectral reflectance metrics to predict herbaceous biomass across a grazing intensity gradient. Canopy height consistently predicted AGB across all grazing levels, whereas normalised difference vegetation index (NDVI) effects were weak and grazing-dependent. The UAV-derived canopy height showed strong relationships with total herbaceous AGB, explaining up to 72% of observed variation, whereas vegetation greenness measured using NDVI showed limited predictive power. In contrast, predicting biomass of foraging importance proved challenging, with UAV-derived structural and spectral metrics explaining only a small proportion of variation. Together, these findings highlight the value of UAV-derived structural measurements over traditional spectral indices for fine-scale rangeland monitoring in semi-arid systems, while underscoring the limitations of current UAV-based spectral and structural metrics for assessing forage value across species and sites.
Why it matches plant phenotyping methodsUAV SfMフォトグラメトリから植物群落の canopy height を抽出し、地上部バイオマス予測性能を評価しており、植物形質取得法の技術的適用・検証が中心である。
abstractUsing unoccupied aerial vehicle (UAV) structure-from-motion (SfM) photogrammetry, we evaluate the ability of fine-scale canopy height and spectral reflectance metrics to predict herbaceous biomass across a grazing intensity gradient.
Semi-arid rangelands support livelihoods and key ecosystem services, yet sustainable management depends on accurate and scalable monitoring of herbaceous aboveground biomass (AGB). Field-based measurements are spatially limited, while satellite-derived vegetation indices often perform poorly in complex savanna systems such as the Kalahari. Using unoccupied aerial vehicle (UAV) structure-from-motion (SfM) photogrammetry, we evaluate the ability of fine-scale canopy height and spectral reflectance metrics to predict herbaceous biomass across a grazing intensity gradient. Canopy height consistently predicted AGB across all grazing levels, whereas normalised difference vegetation index (NDVI) effects were weak and grazing-dependent. The UAV-derived canopy height showed strong relationships with total herbaceous AGB, explaining up to 72% of observed variation, whereas vegetation greenness measured using NDVI showed limited predictive power. In contrast, predicting biomass of foraging importance proved challenging, with UAV-derived structural and spectral metrics explaining only a small proportion of variation. Together, these findings highlight the value of UAV-derived structural measurements over traditional spectral indices for fine-scale rangeland monitoring in semi-arid systems, while underscoring the limitations of current UAV-based spectral and structural metrics for assessing forage value across species and sites.
Why it matches plant phenotyping methodsUAV-SfMによるキャノピー高とスペクトル指標から植物群落のバイオマスを推定し、手法の予測性能を比較評価しており、植物形質取得が研究の中心です。
abstractUsing unoccupied aerial vehicle (UAV) structure-from-motion (SfM) photogrammetry, we evaluate the ability of fine-scale canopy height and spectral reflectance metrics to predict herbaceous biomass across a grazing intensity gradient.
Semi-arid rangelands support livelihoods and key ecosystem services, yet sustainable management depends on accurate and scalable monitoring of herbaceous aboveground biomass (AGB). Field-based measurements are spatially limited, while satellite-derived vegetation indices often perform poorly in complex savanna systems such as the Kalahari. Using unoccupied aerial vehicle (UAV) structure-from-motion (SfM) photogrammetry, we evaluate the ability of fine-scale canopy height and spectral reflectance metrics to predict herbaceous biomass across a grazing intensity gradient. Canopy height consistently predicted AGB across all grazing levels, whereas normalised difference vegetation index (NDVI) effects were weak and grazing-dependent. The UAV-derived canopy height showed strong relationships with total herbaceous AGB, explaining up to 72% of observed variation, whereas vegetation greenness measured using NDVI showed limited predictive power. In contrast, predicting biomass of foraging importance proved challenging, with UAV-derived structural and spectral metrics explaining only a small proportion of variation. Together, these findings highlight the value of UAV-derived structural measurements over traditional spectral indices for fine-scale rangeland monitoring in semi-arid systems, while underscoring the limitations of current UAV-based spectral and structural metrics for assessing forage value across species and sites.
Why it matches plant phenotyping methodsUAV-SfMによるキャノピー高とスペクトル指標を用いた植物バイオマス推定を比較・評価しており、植物形質の取得・推定手法が研究の中心である。
abstractUsing unoccupied aerial vehicle (UAV) structure-from-motion (SfM) photogrammetry, we evaluate the ability of fine-scale canopy height and spectral reflectance metrics to predict herbaceous biomass across a grazing intensity gradient.
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 · Crossref · Europe PMC · checked 5 Sept 2026
Accurate identification and effective prediction of the maize tasseling stage are of great significance for guiding precision field management and ensuring stable crop yields. Conventional manual observation methods suffer from high labor intensity, poor timeliness, and strong subjectivity. In this study, based on an unmanned aerial vehicle (UAV) remote sensing platform, LiDAR point cloud data and RGB imagery were simultaneously acquired to construct digital surface models (DSMs) and digital terrain models (DTMs). Multi-dimensional statistical features were extracted to establish a high-precision plant height estimation method applicable to the entire growth cycle of maize. On this basis, the Logistic growth curve function was introduced to fit the dynamic changes in plant height, enabling the identification and early prediction of the maize tasseling stage based on the plant height growth curve. The research results indicate the following: (1) For maize plant height estimation, the LiDAR sensor outperforms RGB. The optimal accuracy is achieved by combining the 99th percentile of DSM with the minimum DTM, yielding a root mean square error (RMSE) of 0.17 m. (2) Based on the high-accuracy plant height time series, the point of inflection (POI) achieves the highest accuracy in tasseling stage identification, with an RMSE of 2.586 d under the reconstructed time series. (3) Prediction accuracy of the tasseling stage improves with increasing plant height threshold, and optimal performance is observed when the threshold is ≥1.6 m with a growth rate between 0.11 and 0.13. This study establishes a technical framework of "time-series perception-dynamic simulation-feature identification-early prediction", providing a scientific basis for automated monitoring and precision management of the maize tasseling stage. It holds significant theoretical and practical value for the advancement of smart agriculture and crop phenotyping research.
Why it matches plant phenotyping methodsUAV LiDAR/RGBによる草丈推定と時系列成長曲線から、トウモロコシの抽だい期を識別・予測する技術フレームワークが研究の中心であり、植物形質取得と検証を伴うため採用。
abstractMulti-dimensional statistical features were extracted to establish a high-precision plant height estimation method applicable to the entire growth cycle of maize.
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.
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.
Plant height (PH) and aboveground biomass (AGB) are critical agronomic traits that determine the yield potential of maize ( Zea mays L . ). However, the application of genomic selection (GS) and genome-wide association studies (GWAS) in maize breeding is often hindered by the limitations of phenotypic data collection, which is typically characterized by low throughput and inadequate accuracy. To address this challenge, we employed an unmanned aerial vehicle (UAV) equipped with LiDAR and RGB cameras for high-throughput assessment of pH and AGB in a panel of 817 maize hybrids derived from 364 inbred lines over two growing seasons. Our results demonstrated that the integration of UAV-derived LiDAR point clouds with crop surface models (CSMs) enabled robust estimation of pH across multiple years ( R 2 > 0.90). Furthermore, a three-dimensional AGB estimation model was developed using UAV-derived PH and canopy coverage (CC), achieving high estimation accuracy ( R 2 > 0.83). Subsequently, the UAV-derived PH and AGB were utilized for GS and GWAS analyses. Replicated 10-fold cross-validation showed that the mean predictability was 0.504 for PH and 0.402 for AGB across eight commonly used GS models. Moreover, of the 66,066 potential crosses derived from the 364 inbred lines, the top 200 crosses selected for AGB showed up to twice the AGB of the bottom 200 crosses. Field validation demonstrated that the mean ear weight (EW) in the AGB top group was 39.0% higher than that in the bottom group. A total of 16 and 11 significant SNPs were identified by at least two GWAS methods for PH and AGB, respectively. Based on these SNPs, 81 candidate genes were functionally annotated, six of which were simultaneously associated with both traits. The candidate gene association analysis suggested that variations in the promoter region of ZmFLA9 may affect both traits. Overall, our study highlights the potential of UAV-based high-throughput phenotyping to accelerate maize genomic breeding by enabling rapid, precise, and large-scale trait assessment.
Why it matches plant phenotyping methodsUAVのLiDAR・RGBデータから草丈と地上部バイオマスを推定する高スループット表現型測定モデルを開発・検証しており、フェノタイピング手法が研究の中心です。
abstractwe employed an unmanned aerial vehicle (UAV) equipped with LiDAR and RGB cameras for high-throughput assessment of pH and AGB
Cover crops offer essential agroecosystem benefits, including reduced soil erosion, weed suppression, and improved soil health. Aboveground biomass (AGB) is a key indicator of these benefits; however, field-based quantification is often limited, which hinders effective cover crop management decisions. This study integrated unmanned aerial vehicle (UAV)-based multispectral imagery with machine learning (ML) models to estimate AGB in cover crops across two water-limited regions of Texas. Ground-truth and imagery data were collected over three years (2023–2025) for winter rye ( Secale cereale L.) in Lamesa and two years (2023–2024) for winter wheat ( Triticum aestivum L.) in Chillicothe under varying irrigation regimes. Five ML algorithms, random forest, support vector regression, extreme gradient boosting, partial least squares regression (PLSR), and artificial neural network (ANN), were evaluated across four individual and eleven feature fusion datasets. The ANN model consistently achieved the highest predictive accuracy, particularly when vegetation indices were combined with structural features (R² = 0.87, RMSE = 9.08 g m - ²), while PLSR showed the weakest performance. Grouped validation (leave-one-year-out, leave-one-species-out, and leave-one-treatment-out) revealed reduced model performance compared to random (70/30) splitting of pooled data, yet the ANN maintained moderate predictive ability, indicating reasonable generalizability across years, species, and management conditions. Shapley additive explanations (SHAP) revealed key predictors in the ANN model, including plant height, chlorophyll vegetation index, chlorophyll sensitive index, blue band reflectance, modified chlorophyll absorption in reflectance index, dissimilarity, correlation, and enhanced green vegetation index. These findings demonstrate the effectiveness of UAV-ML integration for accurate AGB estimation and highlight the potential for scalable, data-driven cover crop monitoring in water-limited environments and beyond.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と機械学習を統合し、植物の地上部バイオマスを推定する手法を開発・比較検証しており、表現型取得が研究の中心である。
abstractThis study integrated unmanned aerial vehicle (UAV)-based multispectral imagery with machine learning (ML) models to estimate AGB in cover crops across two water-limited regions of Texas.
Biomass is a key trait in pasture plant breeding and agronomy, but measuring Dry Matter Yield or Fresh Weight across large numbers of samples is labour intensive and costly. Efficient biomass assessment systems must balance accuracy, speed, and cost, while ideally enabling non-destructive measurements. We developed a rapid, real-time, non-destructive, and remotely controlled LiDAR-based platform to estimate biomass in grass monocultures by measuring sward height at high spatial resolution. The system operates under ambient light conditions at a ground speed of 2.7 km per hour. It was evaluated in small-plot perennial ryegrass trials at two field sites in New Zealand, across two seasons at site A and one season at site B. At site A, correlations between LiDAR-derived height and fresh weight ranged from 0.33 to 0.74 across individual measurement cycles, with an overall multilevel R² of 0.72. At site B, the multilevel correlation increased to R² = 0.88. Weekly LiDAR scans at site B were used to estimate plot-level growth rates for 60 plots, demonstrating improved temporal resolution. Statistically significant differences in growth rate within regrowth cycles were detected among plots. The platform reliably differentiates perennial ryegrass plots based on biomass and offers higher temporal resolution than traditional methods.
Why it matches plant phenotyping methodsLiDARプラットフォームを開発し、草高から牧草バイオマスと成長率を非破壊推定・検証しており、植物形質取得手法が研究の中心です。
abstractWe developed a rapid, real-time, non-destructive, and remotely controlled LiDAR-based platform to estimate biomass in grass monocultures by measuring sward height at high spatial resolution.
Broccoli is a globally significant vegetable, but climate change and soil salinization increasingly threaten its productivity. Precise seedling phenotyping is essential for selecting salt-tolerant germplasm, yet traditional manual methods are labor-intensive and error-prone. This study develops LBD-PointNet++, an optimized 3D point cloud semantic segmentation model for automated phenotypic parameter extraction of broccoli seedlings at the germination and early developmental phase under salt stress. High-fidelity 3D point clouds were reconstructed from a precision three-view imaging system using Structure from Motion (SfM) algorithms. LBD-PointNet++ introduces three core optimizations: (1) a Large Kernel Attention (LKA) mechanism using 3D sparse decomposition to capture long-range global dependencies; (2) a Dual Uncertainty and Shape-Adaptive Sampling (DUSAS) mechanism to preserve high-frequency features of fragile stems and margins; and (3) a joint Boundary-Aware Nested Contrastive and Adaptive Varifocal Joint Loss (BNCV-Loss) to effectively isolate overlapping leaves. Experimental results demonstrate superior performance, achieving an overall mean Intersection over Union (mIoU) of 88.07% across all three categories (Leaf, Stem, and Pot) and a Mean F1-score of 93.48%. Compared to state-of-the-art Transformer architectures like PTv3, LBD-PointNet++ achieves higher accuracy with less than 6% of the parameter volume and over twofold faster inference speed. Furthermore, dynamic monitoring across NaCl gradients (0-250 mmol/L) revealed a potential non-linear threshold effect, identifying 100 mmol/L as a preliminary phenotypic threshold under these conditions. Beyond this threshold, growth inhibition intensified rapidly; At 250 mmol/L, plant height decreased by 54.43% and the 3D entity volume shrank to approximately one-fifth of the control group. In summary, LBD-PointNet++ provides a high-efficiency solution for phenotypic identification and digital breeding of salt-tolerant Brassicaceae crops.
Why it matches plant phenotyping methods3D点群分割ネットワークと三視点SfM撮像を開発し、ブロッコリー幼植物の表現型形質抽出を中心的に評価しているため。
abstractThis study develops LBD-PointNet++, an optimized 3D point cloud semantic segmentation model for automated phenotypic parameter extraction of broccoli seedlings
Published30 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. The harvest mouse, Micromys minutus (Pallas, 1771) is the smallest rodent in Japan and now listed in the Red Data Books of Tokyo, 2 prefectural capitals, and 28 prefectures in Japan due to drastic decline of grasslands. For the harvest mouse, the height and density of the tall grass species where nesting occurs are considered particularly important. However, it has been difficult to continuously and extensively acquire information on the three-dimensional structure of herbaceous vegetation. With recent development of UAV technology, UAV data are beginning to be applied to the analysis of herbaceous vegetation. For acquiring three-dimensional information via UAV, methods include using LiDAR sensors or generating 3D point cloud data from aerial photographs using SfM. This study evaluates whether UAV LiDAR or UAV SfM is more suitable for estimating the height of tall grass species such as Japanese silver grass (Miscanthus sinensis), which serve as important nesting sites for the harvest mouse. As a result of analysis, the proposed method was found to be effective to estimate grass height regardless of whether UAV LiDAR or UAV SfM is used. However, when comparing the accuracy of canopy height estimation using UAV LiDAR data alone, UAV SfM data alone, and combined UAV LiDAR and SfM data, combined UAV LiDAR and SfM data found to perform best. Maximum canopy height was found to be best estimated using the combination of median of hand-measured five maximum canopy height values and maximum height calculated using the combined UAV LiDAR and SfM data.
Why it matches plant phenotyping methodsUAV LiDARとSfMを用いて植物群落の草丈・キャノピー高を推定し、センサー間の精度を比較評価することが研究の中心であるため、植物フェノタイピング手法として適格。
abstractThis study evaluates whether UAV LiDAR or UAV SfM is more suitable for estimating the height of tall grass species such as Japanese silver grass (Miscanthus sinensis)
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.
Accurate estimation of crop plant height using unmanned aerial vehicles (UAVs) is essential for field-scale crop monitoring and phenotyping. Most previous studies using UAV-based structure-from-motion (SfM) photogrammetry have relied on raster-based crop surface models (CSMs) and have evaluated their performance using accuracy metrics such as the coefficient of determination ( R 2 ) and root mean square error (RMSE). However, such evaluations provide limited insight into how estimation behavior varies across space and time, particularly during dynamic crop growth stages. To address this gap, this study conducted a time-series comparison of rice plant height estimates derived from UAV-SfM-generated dense point clouds (DPCs) and raster-based CSMs in farmer-managed paddy fields in Cambodia, which are characterized by heterogeneous micro-environmental conditions. Rice plant height was measured throughout the growing season and UAV-derived estimates were evaluated using regression analysis, analysis of covariance, and canopy cover dynamics. In the pooled analysis, both approaches achieved high overall accuracy, with R 2 = 0.92 and RMSE = 7.2 cm for the CSM-based approach and R 2 = 0.90 and RMSE = 8.8 cm for the DPC-based approach. However, time-series analyses revealed that CSM-derived plant height estimates exhibited strong location-dependent variability and sensitivity to early-stage canopy development, whereas DPC-based estimates showed more consistent performance across locations and growth stages. Regression coefficients derived from CSM-based estimates varied significantly among locations, whereas those from DPC-based estimates did not, suggesting that point-based representations may provide more spatially consistent estimation behavior under heterogeneous field conditions. By explicitly considering temporal dynamics, canopy development, and data representation, this study highlights the limitations of current raster-based UAV-SfM workflows for structurally complex crop canopies and suggests that DPC-based approaches may offer a useful complementary representation for crop monitoring and phenotyping, particularly when spatial consistency across heterogeneous field conditions is important.
Why it matches plant phenotyping methodsUAV-SfMによるイネの草丈推定手法を、3D点群と作物表面モデルで時系列比較・検証しており、表現形式と技術性能の評価が研究の中心です。
titleA comparative analysis of 3D point clouds and crop surface models for rice plant height estimation using UAV-SfM
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.
Plants encounter multiple abiotic stresses. Among them, heat and drought stress play a substantial role in reducing the agricultural productivity of commercial plants. Hence, wild and underutilized plants can be a potential alternative as they are naturally tolerant to extreme climatic conditions and are a rich source of nutrition. Manual stress and disease detection is a laborious and expensive process, and hence automation in this field is required to reduce agricultural losses. This study evaluates the prediction and detection of abiotic stress in Acacia senegal bipinnate leaves, exploring various stress-induced changes using machine learning (ML) algorithms and biochemical analysis. A. senegal , an underutilized edible desert legume, was grown under controlled greenhouse conditions. After 2 months, these plants were segregated into groups and subjected to heat and drought treatments. Image acquisition was performed to obtain a dataset of 3,454 images of A. senegal leaves. Physiological parameters, such as fresh and dry leaf weight, shoot length, number of leaves, and biochemical assays like antioxidant assay (DPPH), total phenolic content (TPC), and total flavonoid content (TFC), were determined. LC-MS/MS analysis was conducted to identify over 50 phytochemical compounds. A hybrid model was developed consisting of a fine-tuned EfficientNet-based Convolutional Neural Network (CNN) followed by a Support Vector Machine (SVM) for the binary classification of A. senegal leaves. The model distinguishes between healthy and stress-affected unhealthy leaves and achieved an accuracy score of 86.6%. This report provides a significant lead toward stress phenotyping and prediction of a bipinnate leaf plant using ML algorithms. The overall study is useful to understand how the stress encountered by arid plants alters the nutritional quality.
Why it matches plant phenotyping methods画像データと機械学習モデルを用いて、アカシア葉の健全・ストレス状態を自動分類する手法を開発・評価しており、植物表現型取得が中心です。
abstractThis study evaluates the prediction and detection of abiotic stress in Acacia senegal bipinnate leaves
Reproduction assets foundThe paper's data availability statement explicitly makes the 3,454-image A. senegal leaf imaging dataset public on Zenodo and the ML implementation source code public on GitHub; both are paper-specific, public, and actionable.Dataset · publicThe plant leaf imaging data used in the work is publicly available at https://doi.org/10.5281/zenodo.16531486.Open asset ↗zenodo · 10.5281/zenodo.16531486html-lines:480-497Code · publicThe source code of the implementation is available at https://github.com/softwareinnovationslabBITS/CDRF_ASenegal_MLImagingOpen asset ↗github · softwareinnovationslabBITS/CDRF_ASenegal_MLImaginghtml-lines:480-497Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Background Covered smut in barley caused by Ustilago hordei leads to yield reduction and quality loss of stored grains and is especially challenging in organic production. However, screening for resistance remains challenging. The goal of our research was to evaluate protocols for screening covered smut in barley under normal and speed breeding conditions that could be scaled up for breeding purposes. We considered favorable pathogen growth conditions, a sufficient sample size to detect differences among genotypes through a power analysis, sources of disease escape or avoidance, and the infection effect on agronomic traits. Results In the first experiment, twenty genotypes treated with various inoculum concentrations were screened for disease incidence under a speed breeding system. Generally, low infection levels were found, likely due to disease escape or avoidance. Based on a power analysis, we modified the protocol to include more plants and improved pathogen growth conditions under a normal greenhouse system. With the modified protocol, the incidence of covered smut was significantly different among genotypes. The protocol also reduced the number of plants required to detect at least one infected plant. Artificial inoculation significantly decreased germination rates while head emergence, days to heading, and plant height were affected by disease infection in the most susceptible genotypes. We also found that covered smut incidence varied with tiller emergence order. The genotypes 'DH160779' (RES check), PI 270630', 'CIho15270', and 'MTV-color-158' presented potential resistance to covered smut. Conclusion The protocol has a high power to differentiate moderately resistant barley genotypes and we confirmed that specific agronomic traits were affected by disease incidence in susceptible genotypes.
Why it matches plant phenotyping methodsオオムギ病害の抵抗性スクリーニングプロトコルを評価・改良し、検出力と遺伝子型間の識別性能を検証しているため、植物病害表現型の取得法が研究の中心です。
abstractThe goal of our research was to evaluate protocols for screening covered smut in barley under normal and speed breeding conditions that could be scaled up for breeding purposes.
Reproduction assets foundThe paper's disease-screening and agronomic-trait measurement data are publicly deposited on Zenodo, as stated in the Availability of data and materials section. No author analysis code or trained models are explicitly deposited.Dataset · publicThe data used and/or analyzed in the current study are available through the Zenodo, which is available at Gopinathan, G. (2025). Optimization of a protocol for covered smut in barley [Dataset]. Zenodo. [ 47 ] (https:/doi.org/ https://doi.org/10.5281/zenodo.17906264 ).Open asset ↗Zenodo · 10.5281/zenodo.17906264lines:190-223Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
Field / plotLiDAR / point cloudStem / branchMorphology / geometry measurementBiomass / plant weightPlant / canopy height
Abstract Context : Radiata pine breeding programmes rely on stem volume as a key objective, but phenotyping constraints limit selection intensity. UAV-LiDAR offers a scalable alternative to labour-intensive field measurements. Aims : We evaluated UAV-LiDAR-derived metrics as genetic selection proxies for stem volume in radiata pine genetic trials and quantified their utility relative to field-measured diameter at breast height (DBH). Methods: LiDAR metrics describing tree height and size were assessed against allometric stem volume (ASV) across 11 genetic trials (~27,000 trees, two trial series) using single-step genomic best linear unbiased prediction (ssGBLUP) with ~9,500 SNPs. Results : The 3D surface area of the individual tree convex hull (convexhull3D_area) had the highest correlation with ASV (up to r = 0.87) and similar heritability to DBH (mean h 2 = 0.26). LiDAR tree height had the highest heritability (mean h 2 = 0.37) and moderate to high genetic correlation with DBH. Selecting the top 100 genotypes by convexhull3D_area recovered 67-86% of potential ASV genetic gain, versus 88-96% for DBH. Including malformed trees in the genetic analyses of LiDAR traits marginally reduced their performance as stem volume proxies. Conclusion UAV-LiDAR-derived tree height and 3D convex hull surface present desirable properties to complement field phenotyping for stem volume selection in radiata pine. Their scalability, repeatability and high heritability support lower phenotyping costs, better early selection and accelerated genetic gain in radiata pine breeding.
Why it matches plant phenotyping methodsUAV-LiDARを用いて樹高・樹体サイズなどの形質を抽出し、茎体積の遺伝選抜プロキシとして相関・遺伝率・選抜効果を検証しており、植物フェノタイピング手法が中心である。
abstractWe evaluated UAV-LiDAR-derived metrics as genetic selection proxies for stem volume in radiata pine genetic trials
Blackberry micropropagation enables the rapid production of pathogen-free and genetically uniform plant material, although the evaluation of in vitro shoot development still relies on destructive and time-consuming measurements. This study investigated a low-cost smartphone-based 3D imaging approach for the non-destructive characterization of in vitro blackberry shoots (cultivar ‘Thornfree’) grown under different sucrose concentrations in the media (0, 7.5, 15, and 30 g L−1). Explants were cultured for 30 days under controlled environmental conditions in ventilated vessels containing 15 explants. Three-dimensional reconstructions generated using the viDoC RTK rover system coupled with an Apple iPhone 15 Pro Max were used to extract geometric traits, including shoot height, projected area, and shoot volume estimated through three complementary approaches, together with voxel-derived structural descriptors of shoot spatial organization and compactness. The proposed approach enabled the quantitative assessment of shoot architectural responses to sucrose availability, revealing differences in volumetric development and internal structural organization among treatments that would not be detectable by conventional measurements. The results highlight the potential of smartphone-based 3D phenotyping as a rapid, low-cost, and non-destructive tool for monitoring structural traits in micropropagated plant material and for supporting the optimization of in vitro culture conditions.
Why it matches plant phenotyping methodsスマートフォン由来の3D再構成を用いてシュート形態・構造形質を抽出する手法が研究の中心であり、非破壊植物フェノタイピングへの実質的応用である。
abstractThree-dimensional reconstructions generated using the viDoC RTK rover system coupled with an Apple iPhone 15 Pro Max were used to extract geometric traits, including shoot height, projected area, and shoot volume estimated through three complementary approaches, together with voxel-derived structural descriptors of shoot spatial organization and compactness.
Abstract The present work deals with Computer Vision precision in the agriculture domain designed to monitor the height variation of plants (h) and the percentage of Ground Cover (PGC). It brings together electronics and Computer Vision. the electronic part consists of two sensors: the first is the DHT11 sensor, which will monitor environmental parameters (temperature, humidity), and the second sensor is a camera (5 MP Raspberry Pi Camera Module Rev 1.3) which monitors the image acquisition to capture visual information about plants and Raspberry Pi 4 as the central processing unit for environmental data. For the Computer Vision part we have developed an algorithm able to do the acquisition and the segmentation of images acquired using Raspberry Pi 4 in real time.
Why it matches plant phenotyping methods植物の高さと地被率を画像取得・セグメンテーションで推定する監視システムおよびアルゴリズムが中心であり、植物フェノタイピング手法として適格です。
abstractdesigned to monitor the height variation of plants (h) and the percentage of Ground Cover (PGC)
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.
Agrophotovoltaic (APV) systems provide a unique opportunity for improving agricultural land-use efficiency by combining crop production with solar energy capture via photovoltaic panels. In-depth information on plant growth patterns within the spatially heterogenous microclimate created by APVs would enable better planning and management within such unconventional systems. Thus, the present study demonstrates the implementation of a customized robot-mounted 3D-multispectral imaging system for monitoring the growth and spectral reflectance patterns of a conventional soybean cultivar "Eiko" (EK) and a chlorophyll-deficient mutant variety MinnGold (MG) under an APV system. Weekly trends in canopy morphometric features revealed significant variations in canopy height, surface area, light penetration, and volume across the APV field depending on the proximity with the overhead solar panels for both EK and MG, with plants receiving adequate rainfall and intermittent shade performing the best. Furthermore, although spectral indices exhibited variations between EK and MG due to intrinsic differences in pigmentation, symptoms of stress could be detected for both genotypes within rain-shaded areas of the APV plot. Hence, the present investigation depicts the potential for complementary usage of robotics and machine vision for high-precision high-throughput crop monitoring under APVs, which would help improve crop management within such non-homogenous cultivation systems.
Why it matches plant phenotyping methodsカスタマイズしたロボット搭載3Dマルチスペクトル画像システムを実装し、植物形態・スペクトル・ストレス状態を高精度に取得することが研究の中心である。
abstractthe present study demonstrates the implementation of a customized robot-mounted 3D-multispectral imaging system for monitoring the growth and spectral reflectance patterns
Introduction Accurate and non-destructive estimation of wheat biomass is essential for crop growth monitoring, yield prediction, and precision agriculture. Unmanned aerial vehicle (UAV)-based remote sensing, integrating both spectral and structural information, has shown great potential for biomass estimation. However, the mechanisms by which different types of variables contribute to biomass prediction remain poorly understood, especially when using machine learning models. Methods In this study, we fused spectral reflectance, vegetation indices, and canopy height data derived from a UAV multispectral camera to estimate wheat biomass across four growth stages (jointing, booting, heading, and filling). Four machine learning algorithms-XGBoost, Random Forest Regressor (RFR), Support Vector Regressor (SVR), and LASSO-were employed and compared. Results and discussion The results showed that XGBoost achieved the highest accuracy (R 2 = 0.919, RMSE = 102.43 g/m², MAE = 77.43 g/m², RRMSE = 19.71%). Furthermore, SHAP (SHapley Additive exPlanations) analysis revealed that canopy height (CH) was the most important variable, followed by spectral indices such as R842 and GNDVI. The univariate and global contribution analyses demonstrated that structural and spectral variables played complementary roles in biomass estimation. This study provides a mechanistic understanding of variable contributions and offers a robust framework for UAV-based wheat biomass estimation.
Why it matches plant phenotyping methodsUAVスペクトル・キャノピー高データから小麦バイオマスを推定し、複数機械学習法を比較・評価する方法論的研究であり、植物形質取得が中心である。
abstractwe fused spectral reflectance, vegetation indices, and canopy height data derived from a UAV multispectral camera to estimate wheat biomass across four growth stages
Accurate identification of individual plants from unmanned aerial vehicle (UAV) imagery is essential for high-throughput phenotyping and data-driven decision-making in plant breeding. This study presents MatchPlant, a modular, open-source Python pipeline with a graphical user interface for UAV-based single-plant detection and geospatial trait extraction. The pipeline integrates UAV image processing, user-guided annotation of selected undistorted images, convolutional neural network–based object-detection training, forward projection of bounding boxes onto an orthomosaic, and shapefile generation for spatial phenotypic analysis. This workflow preserves native image geometry during detection while maintaining coordinate traceability from source imagery to georeferenced outputs. Across five independent training runs using early-season maize imagery, MatchPlant achieved source-image-level detection performance of AP@0.5 = 90.3 ± 1.1% and mAP@0.5:0.95 = 43.4 ± 2.9%. Orthomosaic-level evaluation after forward projection showed AP@0.5 = 89.7 ± 0.8% and recall = 93.7 ± 1.2%, demonstrating the workflow’s ability to transfer plant detections into georeferenced outputs. Plant-level traits, including plant height derived from canopy height models and NDVI derived from vegetation index rasters, showed strong agreement with manual annotations ( r = 0.87–0.97). Detection outputs were reused across time points with minimal additional annotation, supporting temporal phenotyping during early growth. The framework was validated using maize imagery from a single site and growing season, where plant separation remained clear. By combining modular design, reproducibility, and coordinate traceability, MatchPlant provides an open-source workflow for UAV-based plant-level analysis, with broader applications requiring validation across additional crops, sensors, growth stages, GSDs, and field conditions.
Why it matches plant phenotyping methodsUAV画像から個体検出と植物形質（草高・NDVI）を抽出する、オープンソースの再利用可能なワークフローを開発・検証しており、植物フェノタイピング手法が中心である。
abstractThis study presents MatchPlant, a modular, open-source Python pipeline with a graphical user interface for UAV-based single-plant detection and geospatial trait extraction.
Reproduction assets foundThe paper's MatchPlant analysis pipeline is publicly available on GitHub, and the maize case-study training dataset and pre-trained model are publicly available on Zenodo; both are paper-specific, public, and actionable.Dataset · publicThe public datasets supporting the case study are available on Zenodo at https://doi.org/10.5281/zenodo.14856123 (accessed on February 14, 2025).Open asset ↗Zenodo · 10.5281/zenodo.14856123lines:169-250Model / weights · publicThe training dataset and pre-trained model used in the maize case study presented in Section 3 are also publicly available via Zenodo ( Sangjan et al., 2025a ) at https://doi.org/10.5281/zenodo.14856123 (accessed on February 14, 2025).Open asset ↗Zenodo · 10.5281/zenodo.14856123lines:70-82Code / dataset availability confirmedOpenAlex · checked 11 Sept 2026
Unmanned aerial vehicle (UAV) photogrammetry offers a cost-effective approach to tree-level detection, however, Structure-from-Motion (SfM) outputs are sensitive to processing choices and site conditions, which can alter canopy representation and reduce individual-tree detection accuracy. Here, we systematically evaluate how SfM reconstruction quality and depth-map filtering influence RGB-only individual-tree detection under controlled acquisition conditions. Objectives were to (i) identify an optimal SfM-derived point-cloud configuration for delineating individual trees, and (ii) implement and test a segmentation workflow (local-maxima treetop detection plus Dalponte2016 in lidR) for detecting and counting trees. We assessed RGB-only SfM for individual-tree detection (ITD) across thirteen 1.21-ha loblolly pine ( Pinus taeda ) plots located in two counties in the state of Alabama in the southeastern United States; eight even-aged plantations and five mixed pine-hardwood stands, while holding image acquisition parameters constant. Using Agisoft Metashape Professional (Agisoft LLC, St. Petersburg, Russia), dense-cloud quality (Lowest, Low, Medium, High, Ultra High) and depth-map filtering (Disabled, Mild, Moderate, Aggressive) were varied in a 5 × 4 full-factorial design; assessment metrics included point-cloud density, canopy-surface completeness, canopy-height-model (CHM) agreement with field heights, and ITD precision/recall/F1. We identified a single high-resolution configuration (Ultra High + Disabled) by screening parameter sets for structural accuracy and suppression of false peaks. Using this configuration, CHMs matched field heights in Washington County, Alabama (R 2 = 0.96; RMSE = 0.44 m; bias = − 0.01 m) and in Cullman County, Alabama (R 2 = 0.44; RMSE = 1.14 m; bias = − 0.09 m); pooled performance was R 2 = 0.98; RMSE = 0.54 m; bias = − 0.01 m. ITD accuracy at the primary 3 m match radius yielded a precision of 0.03; recall = 0.29; F1 = 0.05 in the even-aged plantations (Washington) and a precision of 0.03; recall = 0.12; F1 = 0.05 in mixed pine–hardwood stands (Cullman); pooled F1 = 0.05. The selected parameters and workflow are reproducible and transferable, provide insight into RGB-SfM ITD performance, and indicate when lidar remains preferable for crown delineation.
Why it matches plant phenotyping methodsRGB-SfMによる個体樹の検出・樹高推定と、SfM設定およびセグメンテーションワークフローの系統的評価が研究の中心であり、植物の樹冠構造・樹高という形態形質を抽出する方法を検証している。
abstractwe systematically evaluate how SfM reconstruction quality and depth-map filtering influence RGB-only individual-tree detection
Reproduction assets foundThe paper's Code availability statement deposits the authors' SfM/ITD processing scripts publicly on OSF (DOI 10.17605/OSF.IO/UXBCZ). Phenotype/field datasets are only available on request, so they are not public assets.Code · publicThe workflow and processing scripts used in this study are publicly available through the Open Science Framework
(OSF) repository: Singh and Narine, [32]. Code Repository for Optimizing SfM Parameters for RGB-Only Individual-Tree
Detection in Loblolly Pine and Mixed Pine-Hardwood Stands. https://doi.org/10.17605/OSF.IO/UXBCZ.Open asset ↗10.17605/OSF.IO/UXBCZpdf-page:12 lines:1-70Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
Fall dormancy (FD) and forage yield (FY) are two key traits in alfalfa ( Medicago sativa L.) breeding programs. However, genetic progress has remained limited over the past decades, largely due to the complexity of alfalfa breeding and the reliance on labor-intensive phenotyping methods. High-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) represents a promising alternative for rapid and non-destructive crop evaluation. The objectives of this study were to i) estimate FD using UAV-derived canopy height and RGB vegetation indices and ii) evaluate the predictive performance of machine learning (ML) models for FY estimation. A total of 210 alfalfa populations with diverse genetic backgrounds were evaluated over two growing seasons (2023 to 2025) across seven harvests under Mediterranean conditions in central Chile. FY was measured manually, while FD was estimated using both manual and UAV-based approaches. A total of 19 RGB-derived indices (VIs) including plant height (PH) were extracted and used as predictor variables. Five complex predictive ML models were evaluated: PLS, PCR, SVM, ANN, and MLR. The results showed that UAV-derived FD was significantly correlated with FD obtained through conventional methods ( R 2 = 0.88). The automated UAV-based FD phenotyping framework demonstrated slightly higher precision ( R 2 = 0.92) and broad-sense heritability ( H 2 = 0.69) compared to manual measurements ( R 2 = 0.87–0.89; H 2 = 0.64), providing a more reliable selection tool for breeders. Among the tested ML models, SVM and ANN achieved the highest accuracy ( R 2 ≈ 0.73) for FY prediction. These findings demonstrate that integrating low-cost RGB imagery with complex modeling offers a promising avenue that could assist in refining future selection strategies for this genetically complex species.
Why it matches plant phenotyping methodsUAV画像からアルファルファの休眠性と収量関連形質を推定する高スループット表現型解析手法を開発・検証し、手動測定との比較と機械学習モデル評価を行っているため、方法が研究の中心である。
abstractHigh-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) represents a promising alternative for rapid and non-destructive crop evaluation.
Sustainable forest management requires inventory workflows that provide spatially explicit structural information while retaining field-based calibration and uncertainty control. This case study evaluated a field-calibrated UAV LiDAR workflow for individual-tree inventory in middle-aged and near-mature larch plantations in Chuanying District, Jilin City, China. UAV laser scanning point-clouds were integrated with six 30 m × 30 m field plots to assess three individual-tree extraction algorithms: point-cloud segmentation (PCS), marker-controlled region growing (MCRG), and region-based hierarchical cross-section analysis (RHCSA). Algorithm performance was evaluated using plot-level recall, precision, F-score, localization RMSE, tree-height and crown-width accuracy, bootstrap confidence intervals, exploratory Wilcoxon signed-rank comparisons, and leave-one-plot-out stability checks. MCRG provided the most balanced numerical performance under the tested configuration, with a mean F-score of 0.845, compared with 0.808 for PCS and 0.827 for RHCSA. However, the MCRG-RHCSA paired difference was not robust across the six plots, and the analysis should be interpreted as a dataset-specific workflow comparison rather than a universal algorithm ranking. Tree height was estimated with comparatively high accuracy, whereas crown-width estimation remained weak, indicating that vertical canopy structure was more reliable than lateral crown delineation. After calibration assessment, the workflow was applied to 157.47 ha of UAV LiDAR survey areas and generated 219,996 algorithm-based detections. These outputs are best interpreted as a spatial decision-support layer for compartment updating, density screening, and field-inspection prioritization, not as an independently verified wall-to-wall stem census.
Why it matches plant phenotyping methodsUAV LiDARによる個体樹の抽出・樹高・樹冠幅推定を中心に、複数アルゴリズムの精度比較、校正、安定性検証を行うワークフロー研究であり、植物形質取得手法が中核である。
abstractThis case study evaluated a field-calibrated UAV LiDAR workflow for individual-tree inventory
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.
ABSTRACT The common bean is vital for food security, but its productivity is often limited by competition with weeds, requiring the use of herbicides. The response of genotypes to herbicides such as fomesafen and imazamox is variable, and the traditional evaluation of phytotoxicity through visual methods is subjective. Therefore, the present study aimed to: (i) propose a methodology based on image analysis for phenotyping herbicide‐induced phytotoxicity in common bean genotypes, aiming to reduce the subjectivity of traditional visual assessments; (ii) characterize common bean genotypes under the effects of different herbicides and their doses in progenies and parental lines, based on morphophysiological traits and indices derived from visible RGB (red, green, and blue) digital images. The experiment was conducted in a completely randomized design under a 3 × 3 × 6 factorial scheme (herbicide × dose × genotype) with three replications, evaluating fomesafen and imazamox at doses of 0%, 100%, and 200% of the recommended rates. Data were collected on visual phytotoxicity, plant height, stem diameter, number of leaves, and image indices (Green Index, Excess Green Index, Excess Red Index, and Color Index of Vegetation Extraction). Results indicated that the triple interaction was significant, revealing the complexity of plant responses to herbicides. Canonical discriminant analysis explained 78.66% of the total variation, with the first canonical discriminant function (29.42%) contrasting structural development and vitality with stress, the second canonical discriminant function (27.59%) reflecting overall plant vigor, and the third canonical discriminant function (21.65%) capturing stress and phytotoxicity negatively affecting growth. The analysis demonstrated that image‐based indices combined with multivariate techniques are effective for quantifying phytotoxicity and distinguishing genotypes (tolerant and sensitive to herbicide effects), overcoming the limitations of visual evaluations, and should be used as a complementary tool to traditional techniques. Therefore, the parental genotype IPR Campos Gerais and the progeny F1A were tolerant to herbicides at different doses, while the parental genotype BAF36 and the progeny F2B were sensitive. Hence, the proposed methodology is effective for identifying herbicide‐tolerant and sensitive genotypes.
Why it matches plant phenotyping methodsRGB画像解析と多変量解析による除草剤誘発 phytotoxicity の表現型評価法の提案が研究の中心であり、従来の主観的評価を改善する方法開発に該当する。
abstractthe present study aimed to: (i) propose a methodology based on image analysis for phenotyping herbicide‐induced phytotoxicity in common bean genotypes
Urban forest health may be monitored and supported with the implementation and maintenance of an accurate community tree inventory. The longitudinal recording of a tree’s attributes (i.e. diameter and height) may inform potential inputs and activities related to maintenance, health, and the establishment of a tree protection zone. Urban tree inventories feature barriers to implementation including resources (i.e. labour, time, and finances), competing priorities, and gaps in knowledge. The objectives of this case study were to investigate the feasibility of structure-from-motion (SfM) approaches to measure tree height and trunk diameter (1) with oblique RGB imagery, (2) with and without GCP, and (3) during leaf-off and leaf-on conditions. Structure-from-Motion datasets were obtained using Unoccupied Aerial Systems (UAS) – “drones” and related components – to collect oblique aerial imagery, which was processed with photogrammetry software Agisoft Metashape. Tree height measurements using leaf-on imagery (R2 = 0.58, RMSE = 1.34 m) were more accurate when compared to leaf-off imagery with Ground Control Points (GCP) (R2 = 0.47, RMSE = 3.41 m) and leaf-off imagery without GCPs (R2 = 0.43, RMSE = 3.49 m). Tree height measurements during the leaf-off period had no significant difference when comparing imagery with and without GCPs. Trunk diameter measurements using leaf-off imagery were not significantly different with the use of GCPs (R2 = 0.68, RMSE = 6.39 cm) compared to those without (R2 = 0.68, RMSE = 7.85 cm). This case study highlights the applicability and accuracy of Unoccupied Aerial Systems and Structure-from-Motion methods when collecting important urban tree inventory parameters, and presents an accessible, reliable, and replicable workflow for urban forestry practitioners with limited photogrammetry-related experience.
Why it matches plant phenotyping methodsUAS-SfMフォトグラメトリによる樹高・幹径という植物形態形質の推定法を開発・比較検証し、精度と再現可能なワークフローを評価しているため、方法が中心的です。
abstractThe objectives of this case study were to investigate the feasibility of structure-from-motion (SfM) approaches to measure tree height and trunk diameter
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 5 Sept 2026
Abstract Precision field management and high-throughput plant phenotyping increasingly rely on remote sensing to capture spatial and temporal variability in crop performance. Unmanned aerial vehicle (UAV) – based sensing offers unique advantages for field-scale data collection, including high spatial resolution, flexible deployment, and scalable throughput. However, the full potential of UAV platforms remains constrained by labor-intensive operations across flight execution, data transfer, and processing workflows. This study presents a systematic evaluation of an automatic UAV-based crop sensing platform through a season-long, multi-crop field experiment. Data acquisition was conducted over a maize irrigation trial and a soybean breeding experiment, resulting in 176 completed flights over 28 days during the growing season. High-frequency flights on selected days captured diurnal dynamics in key canopy traits, including maize leaf rolling under drought stress and genotype-dependent plot temperature variation in soybean. In the maize irrigation experiment, significant differences in diurnal canopy cover ratio (CCR) were observed among irrigation treatments. The predictive relationship between CCR and final grain yield strengthened throughout the day, with the coefficient of determination (R 2 ) increasing from 0.05 in the early morning (RMSE = 3.05 Mg ha − 1 ) to 0.65 at midday (RMSE = 1.87 Mg ha − 1 ), highlighting the importance of temporal optimization in UAV-based sensing. Temperature measurements from the onboard thermal infrared camera showed a strong overall linear correlation with ground truth measurements (R 2 = 0.85). In the soybean trial, the highest plot temperature was observed on the fast-wilting genotype. Additionally, regression models were developed to estimate key crop traits, including canopy height (CH) and leaf area index (LAI), demonstrating the platform’s quantitative sensing capability. Overall, this study demonstrates that automatic UAV systems enable high-temporal-resolution crop monitoring while substantially reducing operational cost. The results highlight their potential for precise crop management and scalable field phenotyping. Future work will focus on integrating automated data processing pipelines to support near-real-time analytics and decision-making.
Why it matches plant phenotyping methods自動UAVのRGB・熱画像センシング platform を圃場で系統的に評価し、温度・キャノピー被覆率・高さ・LAIなどの植物形質を定量化しているため、フェノタイピング手法が研究の中心である。
abstractThis study presents a systematic evaluation of an automatic UAV-based crop sensing platform through a season-long, multi-crop field experiment.
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
Handheld Mobile Laser Scanning (HMLS) is increasingly used for high resolution 3D mapping in construction, mining and natural environments. This study evaluates the strengths and limitations of HMLS for vegetation assessment in diverse tropical eco-systems across north Queensland, Australia, including rangelands, grasslands, man-groves and estuarine wetland forests. We assessed the accuracy of HMLS-derived point clouds against ground-truth measurements and compared performance with UAV SfM–MVS surveying. HMLS achieved centimeter-level accuracy for vegetation metrics, with mean absolute errors of 8.5 cm for Diameter at Breast Height (DBH) in rangeland forests and 6.7 cm for tussock height. The system consistently produced high-density point clouds, enabling detailed characterization of vertical structure, particularly understory vegetation often obscured in aerial surveys. HMLS proved operationally flexible across closed-canopy wetlands, mangroves, rangeland forests and open grasslands. Key limi-tations included restricted horizontal point cloud penetration in dense vegetation, compounded by access constraints and environmental conditions, and point cloud drift in areas with few geometric features, such as grasslands, which introduced uncertainty in vegetation metrics. High computational demands further constrained workflow effi-ciency. Overall, HMLS demonstrates strong potential as an accurate and versatile tool for vegetation mapping and structural analysis in complex tropical ecosystems.
Why it matches plant phenotyping methodsHMLSを用いた植物の3D形態・構造計測手法を開発的に評価し、地上真値およびUAV手法と比較検証しているため、植物フェノタイピング手法が中心である。
abstractThis study evaluates the strengths and limitations of HMLS for vegetation assessment
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.
Abstract Agroforestry systems (AFS) offer a promising strategy to address environmental challenges while supporting rising food demands. However, the complex interactions between trees and crops complicate research, particularly regarding their effects on crop yields. This study presents a methodological approach using multispectral unmanned aerial system (UAS) data to investigate a maize-cultivated alley cropping system in eastern Germany as a case study. Growth parameters, namely the Normalized Difference Vegetation Index (NDVI) and plant height, were derived as proxies for yield and analyzed in relation to the distance from tree stripes. Additionally, direction-dependent regression analyses were conducted to assess whether spatial variations in the field could be attributed to the trees. Two distinct patterns emerged: first, a pronounced increase in NDVI was observed at close proximity to the trees, correlated with tree height and schematically illustrated for two representative tree stripes; second, at greater distances, fluctuations in NDVI were associated with the trees but lacked consistent directional trends. Considerable inconsistencies were also observed in plant height variations. The discussion highlights potential drivers of the close-range NDVI increase, the applicability of UAS for AFS research, and limitations in generalizing findings from a single case study. Overall, the results demonstrate that tree effects on crop growth and vitality are detectable but marginal in terms of their influence on maize yields at this site, while showcasing the utility of UAS-based approaches for field-scale analysis of AFS.
Why it matches plant phenotyping methodsマルチスペクトルUASからNDVIと植物高を抽出し、樹木からの距離に伴う作物形質を解析する手法の実質的適用が研究の中心であり、単なるルーチン測定を超える。
abstractThis study presents a methodological approach using multispectral unmanned aerial system (UAS) data to investigate a maize-cultivated alley cropping system in eastern Germany as a case study.
To overcome the inefficiency and subjectivity of manual seedling surveys, this study presents a unsupervised framework for evaluating maize sowing quality and emergence uniformity via UAV-based remote sensing. Centimeter-level multispectral imagery was captured to reconstruct 3D point clouds using SfM and MVS techniques. At the algorithmic level, an improved unsupervised pipeline was developed: the Otsu method was employed for plant segmentation, followed by a Fourier Transform to extract 2D spatial frequency features for precise crop row identification and automated spacing measurement. Subsequently, the Combined Entropy Uniformity (CEU) index was developed using Shannon entropy, and a proxy for canopy closure (CCP) was derived using a porosity model, thereby enabling the simultaneous relative quantification of seedling height consistency, spatial distribution uniformity, and canopy geometric structure variability. At the application level, the framework was validated through field trials involving 19 precision planters of diverse configurations. Performance was assessed using indices such as qualified spacing, miss-sowing, and the Coefficient of Variation of Plant Spacing (PSCV). Results indicate that: (1) Vacuum-type planters exhibited optimal stability at speeds of 7–9 km/h, achieving an average qualified spacing rate of 76.7% and a PSCV of approximately 24%, whereas finger-pickup planters were more sensitive to seed size variation and mechanical vibration. (2) The results from the Generalized Additive Model (GAM) suggest a possible nonlinear relationship between seeding rate and certain uniformity indices, indicating that appropriately adjusting operational parameters could help balance operational efficiency and seeding quality; however, this trend requires further validation with larger sample sizes and repeated observations. (3) Point cloud CEU metrics and canopy structure proxies based on the Gap Fraction model showed statistical correlations with certain manually collected indicators, indicating that this method has the potential for rapid screening of seeding quality and relative evaluation of seedling population structure at the field scale under the current experimental conditions.
Why it matches plant phenotyping methodsUAV画像・3D点群から作物の出芽、草丈均一性、空間分布、群落構造を抽出する解析ワークフローを開発し、19種のプランターで検証しており、植物表現型取得法が中心である。
abstractthis study presents a unsupervised framework for evaluating maize sowing quality and emergence uniformity via UAV-based remote sensing.
ABSTRACT Accurate measurement of individual‐tree structural parameters is critical for forest inventory, carbon stock assessment, and urban ecosystem monitoring; however, low‐cost image‐based approaches often fail to capture under‐canopy structures due to canopy occlusion and limited trunk visibility. This study proposes a fully photogrammetric and cost‐effective workflow based on a single‐sensor unmanned aerial vehicle (UAV) using a hybrid flight geometry to improve trunk observability and automate individual tree parameter extraction. A double‐grid acquisition plan combining nadir (−90°) and oblique (−45°) imagery was applied over a semi‐dense forest plot including 120 reference trees. SfM–MVS processing of 918 images produced a high‐density point cloud (162.5 million points) with 1.54 cm/pixel ground sampling distance. Individual trees were delineated using a hybrid strategy integrating DBSCAN‐based coarse clustering, RANSAC‐validated trunk geometry, and trunk‐seeded 3D splitting. Tree height, diameter at breast height (DBH), and crown area were derived from segmented 3D tree models and validated against field measurements and differential GNSS observations. The proposed method successfully detected 108 of 120 reference trees (Precision = 1.00, Recall = 0.90, F1‐score = 0.95) and demonstrated high agreement with in situ measurements, achieving RMSE values of 0.79 m for tree height ( R 2 = 0.975), 3.02 cm for DBH ( R 2 = 0.931), and 3.51 m 2 for crown area. In addition, crown boundary modeling showed that α‐shape reconstruction provided a more realistic representation (IoU = 0.78) than Convex Hull methods by reducing systematic overestimation.
Why it matches plant phenotyping methodsUAV画像とSfM–MVS、点群分割を用いて個体樹木の樹高・DBH・樹冠面積を抽出し、現地測定で検証する方法開発・応用が研究の中心であるため。
abstractTree height, diameter at breast height (DBH), and crown area were derived from segmented 3D tree models and validated against field measurements and differential GNSS observations.
Abstract Mangroves play a critical role in coastal ecosystem services, particularly through their capacity to sequester large amounts of atmospheric carbon, contributing to climate change mitigation. Developing accurate mangrove carbon models is therefore essential for monitoring ecosystem condition and carbon stocks at relevant scales. This study aimed to estimate mangrove Above-Ground Carbon (AGC) in Baluran National Park by integrating field measurements and remote sensing data within a Machine Learning (ML) framework. The study utilised an extensive field data collection programme of 60 sampling plots of girth at breast height, canopy cover, tree height, and tree density. Mangrove AGC was estimated using allometric equations. AGC was also modelled by processing satellite images, conducting statistical analyses, developing models with five ML algorithms (Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), k-Nearest Neighbour (k-NN), and Gradient Boost (GB)), and checking accuracy using 5-fold cross-validation (CV) of Root Mean Square Error (RMSE). The RF model, using field-measured tree height, Ratio Vegetation Index (RVI), and Transformed Soil-Adjusted Vegetation Index (TSAVI), achieved the best performance ( R² training = 0.93, R² testing = 0.84, 5-fold CV RMSE = 12.20 Mg C ha⁻¹). Predicted AGC ranged from 5.39 to 57.18 Mg C ha⁻¹ (mean ± Standard Deviation (SD) = 30.43 ± 16.09 Mg C ha⁻¹) and showed improved accuracy compared to the global mangrove biomass dataset of (Simard et al., 2019). A key contribution of this study is the integration of field-measured tree height within a satellite-based ML framework, which enhances the accuracy and ecological relevance of AGC estimation compared to approaches relying solely on spectral predictors or remotely sensed canopy height products, offering a practical and cost-effective alternative for sites where UAV or LiDAR data are unavailable. This approach provides a practical method for regional mangrove carbon monitoring, national carbon accounting and supports climate change mitigation efforts.
Why it matches plant phenotyping methodsマングローブの樹高・樹冠情報と衛星データを統合し、機械学習で個体・プロットレベルの地上部炭素量という植物状態を推定する手法を開発・交差検証しており、単なる生態系測定ではなく表現型取得手法が中心である。
abstractAGC was also modelled by processing satellite images, conducting statistical analyses, developing models with five ML algorithms (Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), k-Nearest Neighbour (k-NN), and Gradient Boost (GB)), and checking accuracy using 5-fold cross-validation (CV) of Root Mean Square Error (RMSE).
Reproduction assets foundThe authors state that all analysis code (model development, hyperparameter configuration, diagnostics, accuracy assessment) is publicly available in their GitHub repository Mangroves-AGC-Baluran, which reproduces this paper's mangrove AGC machine-learning analysis.Code · publicThe Python codes were available on
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Mangroves-AGC-Baluran.git.Open asset ↗Mangroves-AGC-Baluran.git · Mangroves-AGC-Baluran.gitpdf-raw-page:17 lines:1-379Plant phenotyping relevance match · UnverifiedEurope PMC · Crossref · checked 5 Sept 2026
Introduction eaf area index (LAI) and leaf nitrogen accumulation (LNA) are key indicators of wheat growth and nitrogen nutritional status. However, existing prediction methods predominantly rely on single-modal information and single-output models, limiting their ability to characterize the complex structural and physiological traits of crops. This study aimed to develop a multimodal learning framework for the simultaneous and accurate prediction of wheat LAI and LNA. Methods Spectral, image, and canopy structural features were extracted from wheat canopies across different cultivars, nitrogen treatments, and growth stages. A canopy height correction-based preprocessing method was developed to improve the extraction of structural features. A Dual-Output Bayesian Neural Network (DO-BNN) was then constructed to simultaneously predict LAI and LNA. In addition, an Extreme Sample Mining (ESM) strategy and a joint loss function were introduced to strengthen the learning of complementary information across modalities and the intrinsic correlation between the two target variables. Results The DO-BNN achieved its best predictive performance when all feature modalities were fused. The coefficients of determination (R²) for LAI and LNA were 0.89 and 0.77, respectively, while the corresponding relative root mean square errors (RRMSEs) were 0.15 and 0.35. Compared with single-modal and conventional single-output approaches, the proposed method provided more accurate and robust predictions of both wheat growth parameters. Discussion The results demonstrate that integrating spectral, image, and structural information can improve the characterization of wheat canopy traits. By jointly modeling LAI and LNA, the DO-BNN effectively exploited the physiological relationship between crop growth and nitrogen accumulation. The proposed framework provides a promising approach for the high-accuracy, collaborative monitoring of wheat growth and nitrogen nutritional status.
Why it matches plant phenotyping methods小麦キャノピーのスペクトル・画像・構造情報からLAIと葉窒素蓄積を推定するマルチモーダル手法を開発し、前処理、ニューラルネットワーク、性能比較まで中心的に扱っているため。
abstractThis study aimed to develop a multimodal learning framework for the simultaneous and accurate prediction of wheat LAI and LNA.
Abstract Forest biometrics has evolved from a measurement-driven discipline focused on field efficiency and statistical rigor to a data-rich, technology-enabled science integrating multisensor information and advanced modeling approaches. This special issue, inspired by the Second North American Forest Mensurationists Conference held in 2022, highlights this transformation through nine studies that collectively span scales from individual branches to regional forest dynamics. Together, they emphasize a shift from identifying single optimal models to developing integrated, uncertainty-aware model systems that support operational decision-making. At the finest scale, advances in terrestrial laser scanning enable improved characterization of branch geometry under challenging conditions, yielding robust taper and form factor estimates for volume. At the tree level, extensive benchmarking of height–diameter relationships demonstrates that model form and stand origin strongly influence predictive performance, with generalized additive models often outperforming traditional approaches. Complementary work shows that calibration strategies are not universally transferable across model forms, underscoring the need for careful alignment of function choice and calibration design. Addressing biases in young stands, Bayesian model averaging offers a practical interim solution where traditional volume models trained on mature cohorts fail. At broader scales, studies demonstrate the operational potential of integrating public and low-cost remote sensing data. Freely available USGS 3DEP LiDAR supports highly accurate dominant height and site index estimation, while bias-corrected digital aerial photogrammetry provides a viable alternative in areas lacking LiDAR coverage. Landscape-level analyses using Landsat time series and permanent plots enable mapping of basal area growth, revealing spatial variability and temporal trends driven largely by stand dynamics. Collectively, these studies define a cohesive framework for modern forest biometrics: combining multiple data sources, selecting model families deliberately, applying light but effective calibration, and explicitly quantifying uncertainty. This integrated approach supports scalable, reliable predictions tailored to the needs of forest managers and policymakers. The special issue thus outlines a forward-looking research agenda that prioritizes resilient modeling systems over isolated solutions, enabling forestry to meet contemporary challenges across scales from tree components to landscapes.
Why it matches plant phenotyping methods森林の枝形状、樹高、林分指標などの植物形質を、レーザースキャン、航空写真、LiDAR、時系列衛星データ、統計モデルで推定・検証する方法群を中心に扱う特集概説であり、測定・推定手法が中心である。
abstractAt the finest scale, advances in terrestrial laser scanning enable improved characterization of branch geometry under challenging conditions, yielding robust taper and form factor estimates for volume.
Accurate stem-volume estimation is fundamental for urban tree inventory and management, but equations developed for forest-grown trees may not be directly suitable for open-grown urban trees with altered stem form and height–diameter relationships. This study developed species-specific, model-assisted stem-volume equations for four dominant urban broad-leaved species in Beijing, China: Quercus mongolica, Sophora japonica, Ginkgo biloba, and Populus davidiana. A total of 2679 standing trees from 535 plots were used for model development and evaluation. The diameter at breast height and ground diameter were field-measured, whereas tree height was obtained as a photogrammetry-derived non-destructive measurement using a handheld tree-measurement superstation. Bivariate DBH–height models, DBH-based linked models, and ground-diameter-based chained models were fitted using weighted nonlinear least squares. Model performance was assessed using validation statistics, 10-fold cross-validation, Monte Carlo uncertainty propagation, and an independent destructive reference dataset of 55 felled trees with section-measured stem volume. Across species, the bivariate models performed best, with mean percent standard errors of 8.68%–16.24%, compared with 9.76%–20.25% for DBH-based linked models and 15.13%–28.56% for ground-diameter-based models. Destructive reference validation showed acceptable agreement within the available validation dataset, with relative RMSE values of 2.30%–5.03% and relative bias values of 0.51%–2.51%. Monte Carlo simulation indicated species-specific propagation of photogrammetric height error, with the lowest average volume fluctuation in Ginkgo biloba. These results suggest that handheld photogrammetry combined with species-specific modelling provides a practical and uncertainty-aware basis for urban stem-volume estimation. This study directly estimates stem volume rather than biomass or carbon stock, and the equations may support future biomass- and carbon-related assessments when combined with appropriate conversion parameters.
Why it matches plant phenotyping methods携帯型フォトグラメトリによる樹高取得と、幹体積推定モデルの開発・交差検証・伐倒木による独立検証が研究の中心であり、樹木の形態形質を定量化する実質的なフェノタイピング手法である。
abstractThis study developed species-specific, model-assisted stem-volume equations for four dominant urban broad-leaved species in Beijing, China
Abstract Forest Landscape Restoration (FLR) has become an important strategy for reversing land degradation and improving ecosystem resilience in tropical drylands. However, quantitative evaluations of restoration effectiveness remain scarce in Timor-Leste, particularly those integrating satellite-based monitoring with field observations. This study assessed vegetation recovery following reforestation activities within a 148-ha restoration site in Balak, Manatuto, Timor-Leste, using multi-temporal Sentinel-2 imagery and field-based ecological measurements. Vegetation dynamics were evaluated using the Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 images acquired in May 2022 and May 2026. NDVI differencing was applied to quantify vegetation change, while field data collected from 29 monitoring plots were used to assess seedling survival and growth performance. Pearson correlation and linear regression analyses were employed to examine relationships between vegetation growth indicators and restoration performance. The results indicated substantial vegetation improvement across the restoration area. Mean NDVI increased from 0.288 in 2022 to 0.475 in 2026, representing a 65% increase in vegetation greenness. Approximately 77.6% of the sites experienced moderate to significant vegetation recovery based on ΔNDVI analysis, whereas only 2.3% showed vegetation decline. Field assessments revealed that 62.1% of monitoring plots were classified as high-recovery sites and only 6.9% as low-recovery sites. A significant positive relationship was observed between average plant height and growth percentage ( r = 0.423, R ² = 0.179, p = 0.022), indicating that vegetation structural development was associated with restoration performance. These findings demonstrate that the AFoCO-supported reforestation programme has effectively accelerated vegetation establishment and improved ecosystem condition within a degraded tropical dryland landscape. The integration of Sentinel-2-derived NDVI indicators with field measurements provides a practical, cost-effective, and scalable framework for monitoring FLR outcomes in data-limited regions and offers valuable evidence to support restoration planning and evaluation in Timor-Leste and comparable tropical dryland environments.
Why it matches plant phenotyping methodsSentinel-2 NDVIと圃場測定を統合し、植生回復・成長という植物状態を定量評価する監視フレームワークを中心的に適用しているため。
abstractusing multi-temporal Sentinel-2 imagery and field-based ecological measurements
Plants are geometrically and topologically complex objects, and methods and devices that produce plant point clouds often miss parts due to self occlusions, making further analysis, such as phenotypic trait extraction or 3D reconstruction, difficult. We introduce A-Occ-Plant , a novel method for point cloud completion. The first novelty of our algorithm is converting point clouds into a set of images, which are then completed using 2D amodal segmentation. The images are then converted into a complete point cloud by using view-consistent Gaussian splats. The second novelty is the use of a coarse-to-fine hierarchical Transformer with cross-scale attention. The completed soft masks are fused into a continuous 3D density field using Gaussian splatting, removing the need for external pose estimation or fixed-size inputs. We introduce a synthetic dataset using a procedural model and a real-world plant reconstruction benchmark with artificially generated occlusions. We further benchmark A-Occ-Plant against representative 3D point-cloud completion methods, demonstrate that it recovers downstream phenotypic traits (leaf count, leaf angle, plant height), and show that it generalizes to another crops (soybean). A-Occ-Plant achieves a 264.8% improvement in LPIPS and an 8.3% gain in SSIM compared to the current state of the art, while using only 2.3% of the parameters and running 39.4× faster. We release our code at https://github.com/JaeLee18/PlantPhenomics_Occlusion.
Why it matches plant phenotyping methods植物の遮蔽点群を補完し、葉数・葉角度・草丈という表現型形質を復元する手法を開発しており、データセット作成とベンチマーク検証も中心的に行っている。
abstractWe introduce A-Occ-Plant , a novel method for point cloud completion.
Reproduction assets foundThe paper explicitly releases authors' code and sample data (inference code, sample data for reproducing results) via a Google Drive project download and a GitHub repository, both with explicit availability statements and public URLs.Code · publicThe full code and data at https://github.com/JaeLee18/PlantPhenomics_Occlusion .Open asset ↗JaeLee18/PlantPhenomics_Occlusionlines:386-410Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Accurately monitoring alfalfa nutritional quality is essential for optimal pasture management. Yet, current UAV remote sensing methods rely on single-temporal imagery and empirical indices, limiting their ability to handle multi-stage growth dynamics, canopy spectral saturation, and canopy-to-whole-plant scale differences. Furthermore, small sample sizes often cause purely data-driven models to overfit correlations, yielding biologically unrealistic results. Overcoming these challenges, we designed a comprehensive quality estimation framework using 127 alfalfa core germplasms, combining high-dimensional spectral mining, a physics-informed network, and a 3D allometric transfer operator. After screening 14,960 spectral operators across original and log-transformed spaces, we applied a dual dimensionality reduction strategy to isolate optimal features. Four-band dual-difference structures proved highly sensitive to fiber components (ADF/NDF, |r| = 0.896), while logarithmic decoupling operators accurately isolated protein and nitrogen signals (CP/N, |r| = 0.868). We then engineered a Physics-Informed Sparse Shallow Network (PI-SSN). By leveraging temporal attention decoupling, it adaptively assigns growth-stage weights to different components and uses carbon-nitrogen metabolic constraints to maintain biological accuracy during multi-task retrieval. Multi-stage temporal data significantly boosted accuracy over single-period spectra. PI-SSN delivered exceptional test set coefficients of determination ( R2 ) of 0.812-0.848 and RPDs >2.0 for N, CP, ADF, and NDF, easily outperforming standard baselines. To bridge the canopy-only observation gap, we introduced a 3D allometric transfer operator that incorporates canopy coverage and plant height. This effectively corrected vertical stem-leaf observation biases, enhancing Relative Feed Value (RFV) predictions. Ultimately, this approach offers a powerful new framework for high-throughput forage phenotyping.
Why it matches plant phenotyping methodsUAVリモートセンシングと物理制約ネットワーク、3Dアロメトリック演算子を統合し、アルファルファの栄養品質を推定する手法を開発・検証しており、植物表現型取得が中心である。
abstractwe designed a comprehensive quality estimation framework using 127 alfalfa core germplasms, combining high-dimensional spectral mining, a physics-informed network, and a 3D allometric transfer operator.
Reproduction assets foundThe paper's authors publicly release the pre-trained PI-SSN model weights, inference code, and usage instructions on GitHub. The raw spectral and ground-truth quality datasets are not public and are available only on request, so they do not qualify as public assets.Code · publiceptualization, Resources, Supervision, Writing-review & editing. Dongyan Zhang: Conceptualization, Funding acquisition, Project Administration, Supervision, Writing-original draft, Writing-review & editing.
Data and code availability
The pre-trained model weights, inference code, and usage instructions are publicly available at https://github.com/AeroPheno/PI-SSN.git . The raw spectral data and ground-truth quality data used in this study are not publicly available due to ongoing collaborative projects, but are available from the corresponding author on reasonable request.
Funding
This work was supported by the 2023 Hohhot to introduce high-level innovative and entrepreneurial talents (teamOpen asset ↗AeroPheno/PI-SSNlines:243-301Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Accurate measurement of carbon is stored in plants is essential for evaluating strategies to reduce greenhouse gas emissions. The macauba palm (Acrocomia aculeata) is a native species of South America with high potential for oil production, ecological restoration, and carbon sequestration. Traditional methods to estimate carbon stocks, such as cutting down trees and performing laboratory analysis, are destructive, expensive, and impractical on a large scale. This study tested an alternative approach that combines UAV imagery, computer vision, and artificial intelligence to estimate carbon in macauba palms without damaging the plants. High-resolution aerial images were collected at four sites in Brazil, and a deep learning model (YOLOv8s-seg) was trained to automatically detect and measure palm crowns, achieving a mean Average Precision (mAP@50) of 0.956, precision of 0.946, and recall of 0.944. Plant height was estimated from canopy height models derived from photogrammetric processing of the UAV imagery. Crown diameter and height estimates showed strong correlations with field measurements (r = 0.81 and r = 0.82, respectively), with mean absolute errors of 0.26 m for diameter and 0.61 m for height. Both parameters were used in allometric equations to calculate carbon stock. Carbon estimates ranged from less than 1 ton per hectare in young plantations to more than 190 tonnes per hectare in older stands. These results demonstrate that artificial intelligence and UAV imagery provide a fast and scalable approach for estimating structural variables of macauba palms. However, carbon stock estimates remain sensitive to the allometric equations used and should be interpreted with caution, particularly at the individual level. Therefore, the proposed approach should be understood as a non-destructive monitoring framework rather than a fully generalized carbon prediction model, while still offering a valuable tool to support sustainable agriculture, carbon markets, and land-use policies.
Why it matches plant phenotyping methodsUAV画像、コンピュータビジョン、深層学習を用いてヤシの樹冠径・樹高を自動推定し、現地測定と比較検証している。植物の構造形質取得と炭素量推定の技術が研究の中心である。
abstractThis study tested an alternative approach that combines UAV imagery, computer vision, and artificial intelligence to estimate carbon in macauba palms without damaging the plants.
Cultivars of strawberry (Fragaria × ananassa) differ in photoperiodic responses, which influence the balance between vegetative and reproductive growth, shaping canopy development, biomass production, and water use efficiency (WUE). Using 3D point-cloud phenotyping, this study compared the canopy structure and WUE of the short-day cultivar ‘Sonata’ and long-day cultivar ‘Favori’ grown under identical greenhouse conditions. Cultivar-specific growth and water use traits were quantified using daily non-destructive 3D point cloud phenotyping combined with continuous whole-plant gravimetry, supported by manual and destructive measurements. Non-destructive estimates of plant height and digital biomass corresponded moderately to measurements (height: R2 = 0.628; biomass: R2 = 0.579; mean absolute percentage error (MAPE) = 13.86%). Growth analysis indicated similar relative growth rates between the two cultivars, whereas the crop growth rate was higher in ‘Sonata’ than in ‘Favori’. Integration of growth estimates with gravimetric records revealed higher period average WUE in ‘Sonata’ (3.1 mg g−1) than in ‘Favori’ (2.5 mg g−1). These results highlight the distinctive growth strategies of a canopy-driven pattern in ‘Sonata’ and a reproduction-driven pattern in ‘Favori’. The combined 3D phenotyping–gravimetry framework provides a high-resolution, non-destructive approach to quantify cultivar-specific growth and water use traits.
Why it matches plant phenotyping methods3D点群による非破壊フェノタイピングと連続重量計測を組み合わせ、植物形態・バイオマス・水利用形質を定量化し、測定精度も検証しているため、手法が研究の中心である。
abstractUsing 3D point-cloud phenotyping, this study compared the canopy structure and WUE
Soil salinization has become a critical factor limiting global agricultural production. Characterizing the growth and developmental responses of okra to salt stress and developing efficient and accurate salt-stress phenotyping techniques can provide an important methodological reference for okra cultivation in saline lands and future multi-cultivar salt-stress phenotyping studies. Traditional manual measurement of plant phenotypic parameters suffers from low efficiency and insufficient detection accuracy, making it difficult to achieve rapid and non-destructive analysis of plant phenotypic traits under salt stress. Therefore, this study proposes a computational phenotyping parameter extraction method based on the CSP-MSG Net model. Using dual-view feature fusion, we constructed a dedicated dataset. On the basis of PointNet++-MSG, the original MLP layers were replaced with C2F modules, and the SGE attention mechanism was integrated to enhance morphological feature extraction, thereby constructing a lightweight CSP-MSG Net architecture adapted to okra seedling point clouds for semantic segmentation of okra point clouds combined with DBSCAN clustering to complete instance segmentation, phenotypic parameters including plant height, stem diameter and canopy width were further calculated. This scheme enables high-throughput data acquisition, improves measurement accuracy, effectively reduces model parameters and computational overhead, and realizes lightweight operational performance. The results show that okra seedlings can still grow with increasing salt stress concentration, while the growth rates of the three measured traits are all inhibited, indicating that high-concentration salt stress impairs the growth activity of okra seedlings. To verify the calculation accuracy of the model, the phenotypic parameters predicted by the model were compared with manually measured values. The coefficients of determination for stem diameter, canopy width and plant height of okra seedlings reached 0.96, 0.99 and 0.99, respectively. These results strongly demonstrate the excellent reliability and effectiveness of the proposed method, providing methodological support for non-destructive and accurate phenotypic detection of okra seedlings under salt stress.
Why it matches plant phenotyping methodsオクラ幼苗の点群から草丈・茎径・樹冠幅を抽出する計算フェノタイピング手法を開発し、手動測定との比較で精度検証しているため、方法が研究の中心である。
abstractthis study proposes a computational phenotyping parameter extraction method based on the CSP-MSG Net model.
In this study, the diameter and height of Pinus brutia Ten. trees were measured using orthomosaic data obtained from unmanned aerial vehicle (UAV) imagery, and the stem volumes were estimated using machine learning (ML) techniques. The research was conducted in southwestern Türkiye within the brutian pine stands managed by the Isparta Regional Directorate of Forestry. A total of 175 trees were measured for height and diameter at breast height (d1.3), and these measurements used to estimate volume. The accuracy of these estimations predictions was examined, with volume estimation values serving as dependent variables in various ML algorithms. The performance of nine ML algorithms - AdaBoost Regression, Artificial Neural Network, Deep Neural Network, Decision Tree Regression, Gradient Boosting Regression, Linear Regression, Random Forest Regression, Support Vector Regression, and eXtreme Gradient Boosting Regression - were compared. The results indicated that using only the diameter values (max. correlation 0.984) produced better results than using only the height values (max. correlation 0.932), while combining diameter and height variables (max. correlation 0.987) produced the most accurate results. Among the all algorithms, Random Forest Regression achieved the highest average correlation (0.968), whereas Decision Tree Regression had the lowest (0.906). All algorithms produced correlations exceeding 0.90. These findings demonstrate that ML models can effectively estimate stem volume from UAV-derived diameter and height data under field conditions similar to those in southwestern Türkiye. The integration of remote sensing and ML may therefore offer a viable approach for stem volume estimation in structurally comparable forest environments.
Why it matches plant phenotyping methodsUAV画像から樹木の直径・樹高を取得し、機械学習で幹材積を推定する手法を比較評価しており、植物形質の取得・推定が研究の中心である。
abstractthe diameter and height of Pinus brutia Ten. trees were measured using orthomosaic data obtained from unmanned aerial vehicle (UAV) imagery, and the stem volumes were estimated using machine learning (ML) techniques.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Plant height during the early growth stage of rice is a key indicator reflecting canopy establishment rate, tillering potential, and overall growth vigor, all of which critically determine final yield formation. Conventional manual measurements fail to capture high-frequency, continuous, and non-destructive monitoring of plant height dynamics, limiting the understanding of early growth vigor and its genetic mechanisms. In this study, a UAV-based LiDAR system was employed to acquire canopy point clouds of 211 rice accessions across ten time points within 40 days after transplanting. High-resolution canopy height models (CHMs) were generated, and continuous plant height trajectories H(t) were reconstructed using piecewise cubic Hermite interpolation (PCHIP). The first derivative V(t) quantified growth rate dynamics and identified the timing of maximum growth (T max ), enabling precise differentiation of early growth patterns among geno-types. Genome-wide association analysis (GWAS) using a mixed linear model (MLM, Q+K) detected 604 significant SNPs, among which 33 were stably expressed across environments. Five candidate genes were identified within ±200 kb windows, mainly encoding proteins related to cell elongation, hormone signaling, and photosynthetic metabolism. The results highlight that LiDAR-based dynamic monitoring of plant height, coupled with genomic association analysis, provides a robust framework for quantifying rice early growth vigor and elucidating its molecular basis, offering valuable guidance for breeding high-vigor “early-establishing” rice cultivars.
Why it matches plant phenotyping methodsUAV-LiDARによるイネ草丈の時系列取得・CHM生成・成長率推定が研究の中心であり、GWASはその測定形質の応用分析。
abstractConventional manual measurements fail to capture high-frequency, continuous, and non-destructive monitoring of plant height dynamics
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 6 Sept 2026
Abstract Rapid and accurate quantification of crop biomass using multisource UAV imagery–derived features, such as plant height, vegetation indices, and texture indices demonstrates strong potential for soybean high-throughput phenotyping. The indeterminate growth habit of soybean, alongside extensive nodulation and intense inter-plant competition, necessitates individual plant level (IPL) monitoring to quantify plant-specific nitrogen fixation and competitive vigor. However, most studies aggregate measurements at multi-plants at the plot level, thereby masking these soybean-specific traits. This study aims to develop and evaluate a UAV imagery-based framework for estimating soybean biomass at the IPL, with the objective of characterizing high-resolution spatial variability and supporting high-throughput phenotyping. Regions of interest (ROIs) for IPL data acquisition were defined as rectangular plots based on planting density and were generated from early-stage imagery before canopy overlap occurred. Using these ROIs, structural information (SIs) including plant height (PH) and vegetation fraction (VF)), vegetation indices (VIs), and texture indices (TIs) were derived for each individual plant from RGB and multispectral imagery and organized into sensor-specific feature groups. Recursive feature elimination was applied to select optimal features, which were then used as inputs for machine learning architectures including support vector regression (SVR) with a linear kernel, Random Forest (RF), and XGBoost (XGB). Among them, the SVR model using fused multisource features (SIs + VIs + Tis) achieved the best performance, with R² = 0.88, RMSE = 55.34 g, and rRMSE = 8.50% on an independent test dataset. The results show that: (1) tree-based models, including XGB and RF may suffer from overfitting due to limited sample size and feature redundancy, whereas linear SVR showed better generalization; (2) fusing RGB and multispectral features consistently improved biomass estimation accuracy. Inparticular, near-infrared and red-edge-based indices such as RECI and NDRE, along with VF and PH, were identified as important predictors, while texture indices were not selected as significant features; and (3) the proposed framework enabled spatially explicit IPL biomass mapping and time-series analysis, revealing variability in growth conditions and distinct growth trajectories. The framework provides a reliable solution for IPL soybean biomass estimation with practical potential for UAV-based agricultural decision-making.
Why it matches plant phenotyping methodsUAV画像由来の特徴量と機械学習により個体レベルのダイズ biomass を推定・評価する枠組みが研究の中心であり、植物表現型の取得・抽出手法に該当する。
abstractThis study aims to develop and evaluate a UAV imagery-based framework for estimating soybean biomass at the IPL
Cucumber is a core cultivated facility vegetable in China. Drought stress at the seedling stage severely inhibits its growth and development. The regulatory mechanism and optimal application concentration of SiO 2 nanoparticles in alleviating drought stress in cucumber seedlings remain unclear. Moreover, traditional manual measurement and classic point cloud segmentation models struggle to achieve high-throughput accurate detection of cucumber seedling phenotypes under drought stress. To address these issues, this study focused on phenotypic detection under drought stress and analysis of the regulatory effects of SiO 2 nanoparticles. An improved compact and low-redundancy segmentation model, DDCANet, was proposed based on PointNet++-SSG. Combined with 3D point cloud technology and the Euclidean clustering algorithm, it enables automatic extraction of phenotypic parameters from cucumber seedlings treated with SiO 2 nanoparticles under drought stress. In this study, Trailing cucumber seedlings were used as experimental materials. 3D point cloud data of cucumber seedlings were collected under treatments with different concentrations of SiO 2 nanoparticles and PEG-simulated drought stress. A dataset containing 70 valid samples was constructed and labeled into two categories: Stem and Leaf. The core optimizations of the DDCANet model are as follows: Firstly, an Adaptive Density-Aware Feature Enhancement (ADFE) module is embedded to accurately capture point cloud density heterogeneity induced by SiO 2 ;Secondly, a Channel Attention and Normalization-enhanced SA Layer (CANL) is designed to strengthen the coupling of local and global drought-related phenotypic features. Thirdly, a Drought-Aware Hybrid Loss (DHL) function is constructed to alleviate the class imbalance of seedling stem and leaf point clouds under drought stress. Results show that the DDCANet model achieves a mean Intersection over Union (mIoU) of 89.01 ± 0.32% and a Stem IoU of 83.6 ± 0.45%, representing improvements of 6.55% and 9.6% respectively compared with the baseline PointNet++-SSG model, and a 30.5% improvement in stem segmentation accuracy compared with the classic PointNet model. It thus enables high-throughput, non-destructive detection of drought phenotypes in cucumber seedlings under SiO 2 nanoparticle treatment. Ablation experiments verified the positive contributions of the ADFE, CANL, and DHL modules. Furthermore, instance segmentation and phenotype extraction were completed using the Euclidean clustering algorithm to analyze the drought-alleviating effects of SiO 2 nanoparticles under PEG-simulated drought stress. Results indicate that a low concentration of 20 mg/L exhibits a weak alleviating effect, medium concentrations of 40-60 mg/L show bidirectional regulatory characteristics, and a high concentration of 100 mg/L causes negative physiological effects. The optimal application concentration is 80 mg/L, which comprehensively improves key phenotypes such as seedling height and volume under drought stress and exerts a positive regulatory effect on seedling growth under drought conditions. The DDCANet model constructed in this study provides an efficient technical tool for the accurate phenotypic detection of crop seedlings treated with SiO 2 nanoparticles under drought stress. It clarifies the optimal application concentration of SiO 2 nanoparticles, offers a precise concentration threshold and theoretical support for the scientific application of SiO 2 nanoparticles in drought-stressed cultivation of protected cucumber, and establishes a novel methodological reference for the research on phenotypic regulation of crops under drought stress via nano-agricultural technology.
Why it matches plant phenotyping methods3D点群分割モデルを開発・検証し、キュウリ幼苗の茎葉分離と形質抽出を自動化することが研究の中心であるため、植物フェノタイピング手法論文として採用。
abstracttraditional manual measurement and classic point cloud segmentation models struggle to achieve high-throughput accurate detection of cucumber seedling phenotypes under drought stress.
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
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
Introduction: Root anatomical traits and spatial architecture play a critical role in crop water acquisition and utilization, directly impacting drought tolerance. However, comprehensive studies examining the synergistic effects of deep root configuration and cortical tissue organization under drought stress during the seedling stage remain scarce. Additionally, the underlying physiological mechanisms are not yet well understood. Methods: In this study, we utilized a high-throughput, paper-based phenotyping platform to simulate drought stress using 10% PEG. An efficient, multi-trait evaluation framework was employed to classify the 28 tested genotypes into five drought tolerance categories. Results: This approach enabled the identification of drought-tolerant cultivars "Ruichun 1," "Ningchun 11," and "Ningchun 57," as well as the drought-sensitive cultivar "Dingxi 48." Root traits, including maximum depth, convex hull area, and plant height, demonstrated strong explanatory power and could serve as valuable phenotypic indicators for seedling stage screening. Our findings suggest that drought adaptation in spring wheat involves a strategic coupling in which specific cortical configurations facilitate the development of deep root architecture. While previous studies have often focused on individual parameters, we show that drought-tolerant genotypes optimize root growth in deeper segments of the growth medium by adjusting cortical tissue proportions, potentially minimizing metabolic costs. Discussion: This integrated perspective offers a detailed physiological framework for understanding drought resilience and moves toward a mechanism-based interpretation of resource reallocation. However, it is important to note that these results were obtained using a paper-based phenotyping platform under PEG-induced osmotic stress, reflecting the genotypic potential at the seedling stage rather than actual field drought tolerance. In conclusion, combining the paper-based high-throughput phenotyping platform with a multi-trait evaluation framework allows for the accurate classification of drought tolerance types and the efficient identification of representative spring wheat cultivars. The findings emphasize the importance of deep root configuration and optimized cortical allocation as fundamental components of the root structural basis for drought adaptation in spring wheat. These results provide clear phenotypic targets for early-stage screening, which should be further validated at later developmental stages and under field conditions before being applied in breeding programs.
Why it matches plant phenotyping methods紙ベースのハイスループット表現型解析プラットフォームと多形質評価フレームワークが、根形態を用いた耐乾性分類の中心的手法として明示されているため。
abstractwe utilized a high-throughput, paper-based phenotyping platform
High-throughput phenotyping is essential for resolving genotype-by-environment interactions and accelerating crop breeding. In greenhouse potted-plant systems, narrow aisles, global navigation satellite system (GNSS)-denied operation, variable pot layouts, and plant-level data traceability constrain repeatable automated phenotyping. This study presents PhenoRob-P, a modular autonomous robotic system designed for potted crops in structured facility environments. The system integrates a compact two-wheel differential chassis, a LiDAR–vision fusion framework for row-level navigation, pot-level target identification and local alignment, a six-degree-of-freedom robotic arm with inverse-kinematics-based real-time pose compensation for repeatable multi-view close-range imaging, and a three-tier User–Cloud–Robot platform for task scheduling, remote monitoring, and closed-loop data management. Greenhouse validation showed throughputs of 520 pots/h in continuous scanning mode and 187 pots/h in multi-view fine inspection mode. At travel speeds of 0.2–0.3 m/s, mean terminal positioning errors remained within 30 mm, and approximately 87% of lateral and longitudinal errors fell within ±30 mm. Biological validation demonstrated time-resolved stress phenotyping in wheat, with color indices capturing drought progression and rewatering recovery. For maize, multi-view three-dimensional reconstruction estimated plant height and stem diameter with R 2 values of 0.940 and 0.845, respectively, relative to manual measurements. These results show that PhenoRob-P provides an integrated perception-localization-acquisition-analysis workflow for high-throughput, traceable, and time-resolved phenotyping of potted crops.
Why it matches plant phenotyping methods植物形質の取得を中核とする自律ロボット型ハイスループット表現型解析プラットフォームを開発・検証しており、画像取得、3D再構成、ストレス・形態形質の推定性能も評価している。
abstractThis study presents PhenoRob-P, a modular autonomous robotic system designed for potted crops in structured facility environments.
Reproduction assets foundThe paper's Data availability statement explicitly deposits authors' source code and sample datasets in a public GitHub repository, matching the allowed URL.Code · publicThe source code and sample datasets supporting the findings of this study are openly available at the following GitHub repository: https://github.com/Sunniersy/PhenoRob-P .Open asset ↗https://github.com/Sunniersy/PhenoRob-P · Sunniersy/PhenoRob-Plines:388-431Plant phenotyping relevance match · UnverifiedbioRxiv · checked 11 Sept 2026
Cassava is a major staple crop in tropical regions, particularly in Sub-Saharan Africa, yet its productivity remains constrained by genetic and agronomic limitations. A major bottleneck in cassava breeding is the difficulty of accurately phenotyping agronomic traits under field conditions using conventional, labor-intensive methods. Here, we evaluated the potential of uncrewed aerial vehicle (UAV)-based phenotyping to quantify canopy growth traits and assess their genetic relevance under realistic field conditions. For this, multi-temporal UAV imagery was collected over two growing seasons (2018-2019 and 2019-2020) in a panel of 46 cassava genotypes planted in fields of the International Institute for Tropical Agriculture (IITA), Nigeria. Canopy height, canopy volume, and their relative growth rates (RGRh and RGRv) were extracted at the plot-level, and their seasonal dynamics and canopy-yield relationships were further assessed across developmental stages and environmental conditions. Repeatability (R) and broad-sense heritability (H2) were estimated using a linear mixed model (LMM) that partitioned genetic, genotype-by-year, and residual variance components, enabling the evaluation of both measurement reliability and genetic signal. Overall, UAV-derived growth dynamics were found to exhibit comparable patterns across genotypes, reflecting shared seasonal growth trajectories, while canopy-yield relationships varied with developmental stage and environmental conditions. In terms of genetic metrics, R was high for all UAV-derived traits (R = 0.68-0.69), indicating reliable genotype-level assessment across replicates and seasons. In contrast, H2 differed substantially among traits. Canopy volume (H2 = 0.64) and canopy height (H2 = 0.58) exhibited moderate-to-high heritability, reflecting strong genotype effects and comparatively moderate genotype-by-year interactions. However, their relative growth rates showed near-zero H2 values, driven primarily by genotype-by-year interaction, indicating a dominant environmental influence. These results demonstrate that UAV-derived canopy height and volume provide a consistent basis for genetic differentiation of cassava genotypes across environments, supporting their use in selection, whereas growth-rate traits are better suited for characterizing growth plasticity and genotype-by-environment interactions.
Why it matches plant phenotyping methodsUAV画像からキャッサバの樹冠高さ・体積・相対成長率を抽出し、再現性と遺伝率を検証することが研究の中心であり、実質的な植物フェノタイピング手法の評価に該当する。
abstractwe evaluated the potential of uncrewed aerial vehicle (UAV)-based phenotyping to quantify canopy growth traits and assess their genetic relevance under realistic field conditions.
BarleyCommon beanCowpeaGrowth chamberMesh / voxelLiDAR / point cloudMultispectral / hyperspectralLeafWhole plant / canopy / plot / fieldAnnotation / quality control
Abstract High-throughput 3D multispectral plant phenotyping platforms generate large volumes of point cloud files, but trait extraction is typically performed by sensor-bundled software whose internal algorithms are not publicly documented, which limits reproducibility and integration into custom research pipelines. Here we present PhytoScan3D, an open-source Python pipeline that extracts morphological and spectral phenotypic traits, spanning plant height, 3D leaf area, digital biomass, convex hull volume, leaf inclination, canopy geometry, NDVI, hue, and vegetation indices, from both PLY and PCD point cloud files generated by Phenospex PlantEye F500 and F600 sensors, and is portable to point clouds from any acquisition platform. PhytoScan3D was validated against HortControl (PhenoSpex) ground-truth measurements on 936 barley ( Hordeum vulgare ) pot-date observations from the growth chamber trial (20 Norwegian cultivars, 12 scan dates, Septemenr 2025 to January 2026), achieving Pearson r = 0.913 to 0.999 and ratio approximately 1.000 for Plant Height Max, 3D Leaf Area, and NDVI Average. A vectorised mesh face filtering implementation achieved a 120x speed improvement, increasing valid 3D Leaf Area coverage from 0.6% to 100% of files. Cross-format validation on 223 PlantEye F600 PCD files from the ICRISAT LeasyScan platform (four legume species: mungbean, cowpea, lima bean, and common bean; 1,523 plant observations) yielded r = 0.884 against independent cuboid annotation heights. The systematic positive bias (mean +27.2 mm, ratio = 1.44) is attributable to PhytoScan3D computing height from raw point cloud Z-range while cuboid annotations are fitted to segmented plant points only, with the offset consistent across all four species (per-species r = 0.880 to 0.888). Cross-dataset processing of 1,180 PLY files from the Crops3D benchmark (8 species, 3 acquisition methods) confirmed zero extraction errors. PhytoScan3D is available at “github.com/kovimallik/phytoscan3d” under the MIT licence and processes 1,651 files across three independent datasets in under 12 minutes on GPU hardware. Highlights PhytoScan3D is the first open-source Python pipeline for batch extraction of phenotypic traits, including plant height, 3D leaf area, digital biomass, convex hull volume, leaf inclination, NDVI, and excess green index, from both PLY and PCD point cloud files generated by Phenospex PlantEye sensors. Primary validation against HortControl ground-truth measurements on 936 barley pot-date observations achieved Pearson r = 0.913-0.999 for Plant Height Max, 3D Leaf Area, and NDVI Average. A 120x computational speedup in mesh face filtering (vectorised NumPy vs. set-based loop) increased the coverage of valid 3D Leaf Area extraction from 0.6% to 100% of files. Cross-format validation on 223 PlantEye F600 PCD files from ICRISAT LeasyScan (four legume species, 1,523 plants) achieved r = 0.884 against independent cuboid annotation heights. The systematic +27.2 mm bias reflects a methodological difference (raw Z-range vs. soil-segmented annotations), is consistent and predictable across all four species (per-species r = 0.880-0.888), and is correctable by a single linear factor. Cross-dataset processing of 1,180 PLY files from the Crops3D benchmark (8 species, 3 acquisition methods) confirmed zero extraction errors. Significant scan-unit variation was detected for Plant Height Max (F = 5.71, p < 0.001, η 2 = 0.138) and Canopy Width X (F = 6.32, p < 0.001, η 2 = 0.150), demonstrating the biological utility of extracted traits.
Why it matches plant phenotyping methods植物の3D点群・マルチスペクトルデータから形態・スペクトル形質を抽出するオープンソース手法を開発し、複数データセットで技術検証・ベンチマークしているため、植物フェノタイピング手法が中心である。
abstractHere we present PhytoScan3D, an open-source Python pipeline that extracts morphological and spectral phenotypic traits
Reproduction assets foundThe paper's own analysis code (PhytoScan3D pipeline) is publicly released on GitHub under the MIT licence, and the two external 3D point cloud datasets used for validation (Crops3D and ICRISAT LeasyScan) are publicly available on figshare. The primary barley PLY dataset is not yet public (to be deposited in NVA upon).Code · publicditing, Funding acquisition.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal
relationships that could have appeared to influence the work reported in this paper.
Data Availability
PhytoScan3D source code, documentation, and example datasets are available at
https://github.com/kovimallik/phytoscan3d under the MIT licence. The barley PLY dataset
will be deposited in the Norwegian Research Information Repository (NVA) upon acceptance.
The Crops3D benchmark dataset is publicly available at
https://doi.org/10.6084/m9.figshare.27313272 (Zhu et al. 2024). The ICRISAT LeasyScan
dataset is publicly available at https://doi.org/10Open asset ↗github.com/kovimallik/phytoscan3dpdf-raw-page:15 lines:1-36Dataset · publicData Availability
PhytoScan3D source code, documentation, and example datasets are available at
https://github.com/kovimallik/phytoscan3d under the MIT licence. The barley PLY dataset
will be deposited in the Norwegian Research Information Repository (NVA) upon acceptance.
The Crops3D benchmark dataset is publicly available at
https://doi.org/10.6084/m9.figshare.27313272 (Zhu et al. 2024). The ICRISAT LeasyScan
dataset is publicly available at https://doi.org/10.6084/m9.figshare.28270742 (Galba et al.
2025).
Acknowledgements
This work was supported by the PheNo, DLT-Farming and Soil2Milk from Research Council
of Norway and TWIN-NUE from Norwegian University of Life Sciences (NMBU). The
authoOpen asset ↗figshare · 10.6084/m9.figshare.27313272pdf-raw-page:15 lines:1-36Dataset · publicimallik/phytoscan3d under the MIT licence. The barley PLY dataset
will be deposited in the Norwegian Research Information Repository (NVA) upon acceptance.
The Crops3D benchmark dataset is publicly available at
https://doi.org/10.6084/m9.figshare.27313272 (Zhu et al. 2024). The ICRISAT LeasyScan
dataset is publicly available at https://doi.org/10.6084/m9.figshare.28270742 (Galba et al.
2025).
Acknowledgements
This work was supported by the PheNo, DLT-Farming and Soil2Milk from Research Council
of Norway and TWIN-NUE from Norwegian University of Life Sciences (NMBU). The
authors thank Sara Catarina Costa Laranjeira, Min Lin and other NMBU growth facility staff
for plant care and scanning operOpen asset ↗figshare · 10.6084/m9.figshare.28270742pdf-raw-page:15 lines:1-36Plant phenotyping relevance match · UnverifiedOpenAlex · checked 13 Sept 2026
Accurate mapping of forest canopy height is fundamental to modern forestry, providing essential structural data for biomass estimation and monitoring forest health. This study evaluates the broad usability of global (25 m) and high-resolution (1 m) Canopy Height Models (CHMs) by comparing them against temporally aligned Airborne Laser Scanning (ALS) reference layers from 2018 and 2024. At the 25 m scale, we evaluated four global products: Global Forest Canopy Height (GFCH), Global Map of Tree Canopy Height (GMTCH), High-Resolution Canopy Height model of Earth (HRCH), and Europe Temporal Canopy Height (EUCH). These satellite-derived models exhibit significant height-dependent limitations, systematically underestimating mature forest canopies (>30 m) by more than 15 m due to signal saturation, though EUCH and GMTCH performed moderately better. Transitioning to 1 m high-resolution data revealed a dramatic recovery in structural fidelity. A photogrammetrically derived model (PALS) achieved an RMSE of 4.89 m and a Mean Error (ME) of 1.86 m, demonstrating remarkable vertical stability across complex topography, even on slopes >25°. While coniferous stands produced higher absolute errors (RMSE = 6.75 m) than deciduous stands (RMSE = 6.19 m) due to spire-like architectures, PALS effectively captured fine-scale canopy textures. Experimental deep learning architectures, specifically the ArcGIS Living Atlas model, showed promise with an RMSE of 8.90 m, though out-of-the-box implementations struggle without local calibration. For forest disturbance monitoring, a distinct performance trade-off emerged. High-resolution photogrammetry (PALS) provided the highest overall precision for identifying clear-cuts (F1 = 0.353) but was conservative, capturing only 51% of the reference area. In contrast, the global HRCH model captured the total spatial footprint (103.9% of area) despite its geometric inaccuracies. The Living Atlas deep learning model offered the most balanced sensitivity, detecting 118.6% of the area with a competitive F1 score of 0.326. Ultimately, digital aerial photogrammetry provides a cost-effective solution for frequent operational updates, such as the two-year national mapping cycle in the Czech Republic.
Why it matches plant phenotyping methods森林キャノピー高という植物構造形質を対象に、複数の衛星・航空・深層学習モデルをALS基準と比較評価しており、測定手法の技術的検証が中心である。
abstractThis study evaluates the broad usability of global (25 m) and high-resolution (1 m) Canopy Height Models (CHMs) by comparing them against temporally aligned Airborne Laser Scanning (ALS) reference layers from 2018 and 2024.
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.
This study developed an integrated diagnostic system for tebuthiuron-induced soil ecotoxicity based on morphophysiological indicators of Mucuna pruriens, using the germination index (GI) of Lactuca sativa as a sensitive ecotoxicological validation endpoint. The experiment was conducted under greenhouse conditions using a completely randomized design with 12 treatments and 360 individual pots (independent samples evaluated via destructive sampling), which were distributed across five evaluation periods at 14, 28, 42, 56, and 70 days after sowing. Morphophysiological variables, including plant height, root length, shoot and root dry mass, chlorophyll content, nodule number, and visual phytotoxicity, were quantified and integrated with multivariate and probabilistic modeling approaches. Given the multifactorial nature of the germination index, Principal Component Analysis (PCA) was applied to identify ecological and physiological gradients associated with plant vigor, stress, and symbiotic functioning. The PCA outputs were subsequently used as inputs for Probabilistic Neural Networks (PNNs), enabling the classification and prediction of bioindicator-based ecotoxicological levels using mathematically defined low, medium, and high GI classes. Model performance was internally assessed using training and validation datasets, confusion matrices, overall accuracy, sensitivity, specificity, and ROC curves. Because no independent external dataset was available, the predictive performance should be interpreted as evidence of internal consistency rather than definitive generalizability across different soils, climates, herbicide doses, or field conditions. Multivariate analyses revealed that ecotoxicological attenuation trajectories in tebuthiuron-contaminated soils are inherently nonlinear, being structured by coordinated shifts in morphophysiological traits rather than isolated responses of individual variables. The integrated PCA-PNN framework demonstrated that aboveground traits. Particularly plant height, chlorophyll content, and shoot dry mass, were more sensitive indicators of tebuthiuron-induced stress than root traits alone. Higher GI values were associated with PCA regions characterized by increased shoot biomass, greater plant height, reduced phytotoxicity, and improved physiological performance, whereas lower GI classes corresponded to suppressed growth and multidimensional stress signatures. The progressive convergence between plant vigor and GI across evaluation periods suggests a gradual mitigation of ecotoxicological stress signals on the indicator plants, indicating transitions from acute injury to physiological adaptation states. These findings confirm that M. pruriens functions as an effective bioindicator for diagnosing soil ecotoxicological status and monitoring tebuthiuron-induced impacts. However, as tebuthiuron residues were not chemically quantified, these responses should not be interpreted as direct evidence of herbicide degradation, dissipation, or removal. These findings confirm that M. pruriens functions as an effective bioindicator for diagnosing soil ecotoxicological status and monitoring tebuthiuron-induced impacts. However, as tebuthiuron residues were not chemically quantified, the observed improvements should be interpreted as evidence of physiological adaptation and/or ecological attenuation rather than definitive proof of herbicide degradation or removal. Overall, this approach provides a robust framework for early detection of soil contamination and supports its application in monitoring and guiding soil rehabilitation processes, with potential for future validation under field conditions.
Why it matches plant phenotyping methods植物の形態・生理形質を統合し、PCA-PNNで植物ストレスおよび土壌生態毒性レベルを診断する手法の開発・内部検証が中心であり、単なる生物学的測定ではない。
abstractThis study developed an integrated diagnostic system for tebuthiuron-induced soil ecotoxicity based on morphophysiological indicators of Mucuna pruriens
Interest in light detection and ranging (LiDAR) for the precise monitoring of vegetative growth of grain crops has increased. The study was conducted to estimate wheat size and plant distance using LiDAR and the convex hull method (CHM) compared to the voxel grid method (VGM). A commercial LiDAR system was used for data collection in the middle and late growth stages using static and dynamic scanning. A small number (ten) of data frames, consisting of a region of interest (ROI) of 1 m × 0.9 m for each frame, were selected as data samples. The data processing workflow consisted of data conversion, targeted data frame selection, visualization, region of interest (ROI) segmentation, outlier and untargeted point removal, downsampling, denoising, voxelization, preparation of the convex hull, and 3D PCD density map. To estimate the plant size and distance of wheat, the results obtained using CHM and VGM were compared with measured data results, and both methods were applied for the middle and late growth stages of wheat. The relative accuracy of LiDAR-estimated plant height, canopy volume, plant spacing, and row distances with respect to the measured results were 94%, 87%, 94%, and 87%, respectively, using CHM, and 76%, 72%, 62%, and 71% by VGM for static data scanning; for dynamic scanning, the estimated relative accuracy percentages were 87%, 91%, 94%, and 93%, respectively, using CHM, and 77%, 74%, 75%, and 74%, respectively, using VGM. The same methods were applied to the late growth stage data sets. Between the two methods, CHM provided higher accuracy for static and dynamic data-scanning approaches in the middle and late growth stages because the complex geometry of plants, thin and sparse leaf area, and structure complicated voxelization. Despite several challenges in PCD collection and processing, this study supports size and distance estimation for wheat and similar grains as non-destructive methods.
Why it matches plant phenotyping methodsLiDARと3D点群処理によりコムギの草丈、キャノピー体積、株間・畝間距離を推定し、凸包法とボクセル法を実測値と比較検証している。表現型取得・抽出手法が研究の中心である。
abstractThe study was conducted to estimate wheat size and plant distance using LiDAR and the convex hull method (CHM) compared to the voxel grid method (VGM).
High-quality, real-time three-dimensional (3D) reconstruction and phenotyping of field crops are essential for advancing digital farm management. However, challenges such as diverse crop postures and fluctuating illumination in open-field environments significantly hinder reconstruction accuracy. To address these issues, this study proposes AgriGaussian, a novel 3D phenotyping method based on 3D Gaussian Splatting (3DGS), designed to enable low-cost, high-fidelity reconstruction and phenotypic analysis of agricultural fields, with chili pepper plants as an example. Specifically, a dataset encompassing three distinct growth stages of chili pepper plants was collected and preprocessed using a phenotyping robot (PR) equipped with oblique-view imaging. The AgriGaussian method integrates three core components: (1) a Depth-Supervised Strategy (DSS) for adaptively enhancing phenotypic features based on gradient variations; (2) a Crop Appearance Extraction (CAE) module that suppresses the effects of illumination and environmental interference while preserving fine phenotypic details; and (3) a Multi-scale Chunking Training (MCT) strategy that enables scalable reconstruction of large agricultural scenes. Experimental results demonstrate that AgriGaussian achieves high-fidelity reconstruction, with peak signal-to-noise ratio (PSNR) of 26.85 dB, structural similarity index (SSIM) of 0.86, and learned perceptual image patch similarity (LPIPS) of 0.24—surpassing existing baseline algorithms. Compared to manual measurements, the reconstructed canopy height and volume exhibit strong agreement, with coefficients of determination (R2) reaching 0.90 and 0.86, respectively, and average absolute percentage errors as low as 5.1%. Furthermore, the method accurately reconstructs fine-grained features such as foliar lesions and surface textures. In summary, AgriGaussian enables large-scale, high-fidelity reconstruction of field crop phenotypes and provides a promising foundation for advancing 3D phenotyping and digital twin technologies in precision agriculture.
Why it matches plant phenotyping methods植物の3D形態・構造を抽出する低コスト画像ベース表現型解析法を開発し、手動測定との一致性も検証しているため、表現型取得法が研究の中心である。
abstractthis study proposes AgriGaussian, a novel 3D phenotyping method based on 3D Gaussian Splatting (3DGS)
This study suggests a novel extraction pipeline based on terrestrial laser scanning across multiple growth stages to address the current deficiency of three-dimensional (3D) phenotypic traits for wheat populations derived from 3D point clouds. This study presents 3D Wheat Point-seg Net (3D WP-seg Net), a novel 3D point cloud segmentation network that incorporates an SA-CrossAttention module to address the difficulties presented by complex structures, background noise, non-uniform point distributions, and scale variations in plot-level wheat point cloud data. Plot height, canopy area, and volume are examples of common phenotypic parameters that are successfully extracted using this technique. Additionally, two new phenotypic parameters: plot extension distance and lodging angle are suggested by fusing the centroid and slice-skeletonization algorithms. A software platform called 3D Trait Analysis was created to facilitate multi-sensor 3D data processing and trait extraction. A genome-wide association study (GWAS) was then conducted using the extracted population-level traits to find potential genes linked to these new phenotypes. While the segmentation accuracies of 3D WP-seg Net achieved 93.1%, 88.3%, and 92.5% under various sensor systems, the results showed a strong correlation between the predicted and measured plot heights (R 2 = 0.954). Furthermore, four candidate genes linked to extension distance were found on chromosomes 1A, 2A, and 4A, and five putative genes controlling plot lodging angle were found on chromosomes 2D, 3A, and 7A. The multi-stage 3D phenotyping and analysis framework for wheat populations established by this study improves the accuracy of point cloud segmentation and trait quantification while offering a new and efficient method for the genetic analysis of important population-level traits.
Why it matches plant phenotyping methodsLiDAR点群の分割、3D形質抽出、検証、ソフトウェア基盤の開発が研究の中心であり、コムギの形態・倒伏関連形質を定量化しているため。
abstractThis study presents 3D Wheat Point-seg Net (3D WP-seg Net), a novel 3D point cloud segmentation network
Reproduction assets foundThe paper's data availability statement explicitly deposits the authors' source code (3D WP-seg Net segmentation pipeline and 3D Trait Analysis software), testing data, and supporting datasets in a public GitHub repository, directly supporting this paper's wheat 3D phenotyping and segmentation analysis.Code · publicThe source code, testing data, and other datasets supporting the results presented here are available at https://github.com/AI-PhenoLab/3D-WP-seg-Net .Open asset ↗AI-PhenoLab/3D-WP-seg-Netlines:511-575Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
P henotyping remains a critical bottleneck in sorghum ( Sorghum bicolor L. Moench) breeding programs, limiting rates of genetic gain due to labor-intensive yield estimation methods. To address this concern, this study investigates the potential of integrating remote sensing data with machine learning (ML) and deep learning (DL) models to improve sorghum grain yield predictions. Unmanned aircraft systems (UAS)-based imagery was collected across multiple field trials, extracting standard vegetation indices, canopy height features, and panicle traits using a YOLOv11-based object detection model, "YOLO-SORG." Six ML models-including ridge regression (RR), elastic net (EN), LASSO regression (LR), support vector regression (SVR), random forest (RF), and XGBoost (XGB)-were trained to predict plot-level yield using three distinct feature sets: panicle traits, canopy traits, and a combination of both. Results indicate that models relying solely or partially on canopy-derived features provided the most consistent and accurate yield estimates (R 2 ≈ 0.74-0.76), whereas models relying solely on panicle traits performed poorly (R 2 ≈ 0.28-0.42), indicating nadir-derived panicle metrics were potentially being indirectly captured with the canopy traits. Traditional regression models outperformed tree-based ensemble methods in variance partitioning and repeatability ( R ≈ 0.59-0.60), making them more suitable for many breeding applications. These findings highlight the promise of UAS-driven ML pipelines for non-destructive yield prediction but underscore potential limitations of nadir imagery for capturing panicle morphology and use in a robust yield prediction model. Future research should explore the inclusion of multi-temporal imaging, refined feature extraction approaches, and use of oblique, non-nadir imagery to enhance predictive accuracy in sorghum breeding programs.
Why it matches plant phenotyping methodsUAS画像からキャノピー高、穂形質、植生指数を抽出し、機械学習でソルガムのプロット収量を推定するパイプラインが研究の中心であり、形質取得・推定手法の評価も行っている。
abstractthis study investigates the potential of integrating remote sensing data with machine learning (ML) and deep learning (DL) models to improve sorghum grain yield predictions.
Reproduction assets foundThe authors explicitly state that the tabular data and code used in this sorghum yield prediction study are publicly available in their GitHub repository, which is a paper-specific asset containing the analysis code and phenotype data.Code · publicThe tabular data and code used in this study can be found in the following GitHub repository: https://github.com/AcePugh/Sorghum_Yield_Prediction_2025/Open asset ↗AcePugh/Sorghum_Yield_Prediction_2025lines:137-139Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Tree height is a fundamental attribute in ecological research and commercial forestry, serving as a key indicator of site productivity. Unmanned Aerial Vehicles (UAVs) equipped with RGB cameras and Light Detection and Ranging (LiDAR) sensors offer a cost- and time-efficient alternative to traditional field-based, satellite, and manned aircraft methods for tree height measurement. The objectives of this study are: (1) to evaluate the effects of UAV flight speed and image overlap on data quality, mission efficiency, and processing requirements; (2) to compare the performance of Structure-from-Motion (SfM) photogrammetry and LiDAR in generating canopy height models (CHMs); and (3) to quantify the accuracy of UAV-derived tree height estimates relative to field measurements and identify optimal flight configurations. Three flight speeds (10, 15, and 20 mph) and four forward/side overlap levels (50%, 60%, 70%, and 80%) were tested, with UAV-derived height estimates validated against 920 field-measured trees. Both datasets were processed in ArcGIS Pro to produce canopy height models (CHMs), RGB imagery via Structure-from-Motion (SfM) photogrammetry, and LiDAR data from laser-scanned point clouds. Orthomosaic quality was assessed using tie point density, reprojection error, ground resolution, image georeferencing deviation, Global Positioning System (GPS) root mean squared error (RMSE), and block adjustment success, while LiDAR point cloud quality was evaluated using point density (pts/m 2 ) and height percentiles (P25, P50, P75, P95). Height estimation accuracy for both sensors was quantified using the coefficient of determination (R 2 ), RMSE, and Bias. Results indicate that image overlap exerted a stronger and more consistent influence than flight speed across all dimensions of mission efficiency, data volume, and processing time. Flight duration more than doubled and image counts increased sixfold when overlap increased from 50:50 to 80:80. Higher overlaps improved orthomosaic continuity, tie point density, reprojection accuracy, and CHM quality, though at the cost of longer processing times, greater storage demands, and increased computational requirements. LiDAR point density similarly increased with overlap, yielding smoother CHMs at ≥70% overlap, while height percentiles remained stable across configurations. In terms of accuracy, UAV imagery at 10 mph with 80:80 overlap achieved the best photogrammetric performance (R 2 ≈ 0.60, RMSE = 4.5 m, Bias = 4.3 m), though all imagery-derived estimates exhibited systematic height underestimation. LiDAR-derived heights were substantially more robust across all flight configurations, with the best performance at 10 mph and 80:80 overlap (R 2 ≈ 0.89, RMSE < 1.5 m, Bias < 0.5 m). These findings demonstrate that higher overlap, particularly at moderate flight speeds, substantially enhances data quality and tree height estimation accuracy, offering practical guidance for optimizing UAV-based forest inventory workflows.
Why it matches plant phenotyping methodsUAV画像・LiDARによる個体樹高推定を中心に、飛行条件、SfMとLiDARの比較、920本の実測木による精度検証を行っており、植物形質取得手法の技術評価が主目的である。
abstractThe objectives of this study are: (1) to evaluate the effects of UAV flight speed and image overlap on data quality, mission efficiency, and processing requirements; (2) to compare the performance of Structure-from-Motion (SfM) photogrammetry and LiDAR in generating canopy height models (CHMs); and (3) to quantify the accuracy of UAV-derived tree height estimates relative to field measurements and identify optimal flight configurations.
Urban forest carbon sequestration is vital for environmental health, climate change mitigation, and enhancing the quality of life in urban areas. This paper presents a framework for high density point clouds production by integrating point data from aerial photogrammetry, UAV-based LiDAR and terrestrial LiDAR for individual tree measurements and carbon storage estimation. The aerial photos, UAV-based LiDAR and terrestrial LiDAR were observed based on common ground control points and combined using Iterative Closest Point (ICP) algorithm. The combined point cloud was iltered to separate ground points and normalized based on Digital Terrain Model (DTM). The normalized point cloud was used for individual tree segmentation from which individual tree measurements such as, tree height, Diameter at Breast Height (DBH) and crown diameter were estimated. The estimated tree parameters were used for individual carbon estimation. The results show that the individual tree segmentation method signi icantly underestimated the number of trees. The estimation of DBH, tree height, and crown diameter achieved the Root Mean Square Error (RMSE) value of 0.107m, 1.385m and 2.650m respectively. However, in general the estimates experience underestimation as shown by Mean Bias Error (MBE) with 0.003m, -0.636m and 0.001m for DBH, tree height and crown diameter respectively. The estimated values for each individual tree were used for individual tree biomass and carbon storage recording the Root Mean Square Error (RMSE) at 1970.236 kg and 886.606 kgC respectively while attaining the Mean Bias Error (MBE) measure of 140.019 kg and 63.009 kgC each. The proposed framework showed promising results for individual tree carbon estimation. Nonetheless, further attention should be given on individual tree delineation process.
Why it matches plant phenotyping methods航空写真、UAV・地上LiDARを統合し、個体樹木の分離と樹高・DBH・樹冠径を推定して精度評価する手法が中心であり、植物形質計測の技術的検証に該当する。
abstractThis paper presents a framework for high density point clouds production by integrating point data from aerial photogrammetry, UAV-based LiDAR and terrestrial LiDAR for individual tree measurements and carbon storage estimation.
Abstract. Unmanned Aerial Vehicles (UAVs) have become valuable tools for high-resolution ecological monitoring, particularly in complex environments such as mangrove forests. This study investigates the impact of flight altitude on the accuracy of tree height estimation in the Melgonze mangrove forest, in southern Iran. Two UAV flights were conducted at altitudes of 100 meters and 150 meters using a DJI Phantom 4 Pro, and photogrammetric processing was performed using Agisoft Metashape. A total of 16 mangrove trees were measured in the field to provide ground-truth reference data. Canopy height models (CHMs) were generated from both UAV datasets and compared to the field measurements. Preliminary results indicate that the 100-meter flight achieved higher accuracy, with a lower root mean square error (RMSE =21.2 cm), Mean Absolute Error (18.94 cm), and a higher coefficient of determination (R² = 0.97) compared to the 150-meter flight (43.3 cm, 35 cm, and 0.92, respectively). These findings underscore the significance of flight altitude in UAV-based assessments of forest structure and offer practical guidelines for optimizing data acquisition in future mangrove mapping applications.
Why it matches plant phenotyping methodsUAV画像と写真測量によるマングローブ樹高推定を対象に、飛行高度が推定精度へ与える影響を実測値と比較検証しており、植物形質取得法の技術評価が中心である。
abstractThis study investigates the impact of flight altitude on the accuracy of tree height estimation in the Melgonze mangrove forest
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.
UAV-based phenotyping enables efficient high-throughput measurement of field crops. Phenotypic monitoring of ramie is critical for its cultivation management and variety breeding. However, ramie exhibits characteristics including multiple annual harvests, short growth cycles and rapid dynamic growth change, all of which increase the difficulty of growth monitoring and yield estimation. This study aims to utilize UAV-based multispectral remote sensing to estimate ramie plant height (PH), leaf area index (LAI), and above-ground biomass (AGB) over multiple time series, and to assess the influence of seasonal effects and different data processing strategies on the accuracy of ramie digital phenotyping. Over three ramie growth cycles, a total of 15 UAV flights were conducted over an experimental field consisting of 72 plots. The structure from motion (SfM) algorithm was applied to estimate PH. Remote sensing features derived from UAV imagery were used with background segmentation and machine learning to estimate LAI. The AGB was estimated by combining remote sensing-derived PH, LAI, and climate data. The results showed that the estimated and measured phenotypes were highly correlated, with optimal coefficients of determination of 0.961 for PH and 0.873 for LAI. Background segmentation improved LAI accuracy. Integrating climate data, remote sensing-derived PH and LAI significantly enhanced the accuracy of AGB estimation. In conclusion, this study provides a feasible method for extracting ramie phenotypes from UAV remote sensing imagery, providing methodological support for large-scale management of the crop industry and intelligent, precise monitoring of crop growth.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像、SfM、背景分割、機械学習を用いてラムーの草丈・LAI・地上部バイオマスを推定し、精度評価まで行う手法研究であり、表現型取得・抽出が中心である。
abstractThis study aims to utilize UAV-based multispectral remote sensing to estimate ramie plant height (PH), leaf area index (LAI), and above-ground biomass (AGB) over multiple time series, and to assess the influence of seasonal effects and different data processing strategies on the accuracy of ramie digital phenotyping.
The vertical heterogeneity of rice canopy structure limits the accuracy of inverting leaf physicochemical parameters using traditional radiative transfer models, while LiDAR-based 3D reconstruction remains costly for large-scale applications. To address these challenges, this study proposes a method for constructing 3D rice canopy scenes using "Precision Mode" and "Rapid Mode" strategies. The Precision Mode builds detailed structural models based on measured morphological parameters, validated via the LESS 3D radiative transfer model. To overcome the limitations of obtaining detailed morphology via UAV remote sensing, the Rapid Mode employs machine learning algorithms-specifically Support Vector Machine (SVM), Random Forest (RF), and XGBoost-to map easily accessible parameters (LAI, Above-ground Biomass, Plant Height, and Transplanting Date) to detailed 3D structural parameters. Results indicate that XGBoost achieves the highest accuracy in the Rapid Mode. Furthermore, simulated spectra under both modes showed high consistency with measured spectra, yielding average RMSE values of 0.0104 (R 2 = 0.9965) for the Precision Mode and 0.0307 (R 2 = 0.9694) for the Rapid Mode. Although the spectral accuracy of the Rapid Mode is slightly lower, its modeling efficiency is significantly enhanced, retaining a strong capability to reproduce spectral response characteristics across growth stages. This approach provides an effective tool for analyzing vertical spectral response mechanisms and offers an efficient data simulation scheme for UAV remote sensing parameter inversion based on 3D radiative transfer models.
Why it matches plant phenotyping methodsイネ群落の3D構造を構築・推定する手法を開発し、放射伝達モデルと実測スペクトルで検証しており、植物形質の取得・再現が研究の中心です。
abstractthis study proposes a method for constructing 3D rice canopy scenes using "Precision Mode" and "Rapid Mode" strategies.
Reproduction assets foundThe paper's Data Availability statement says the collected phenotype/structural/spectral data are publicly available on the authors' GitHub repository (allowed URL), while the analysis code is only available from the corresponding author upon request (request_only, no public URL).Dataset · publicThe data collected and used in this study are publicly available at: https://github.com/baijc4095-code/2024data . The code used for analysis can be obtained from the corresponding author upon reasonable request.Open asset ↗baijc4095-code/2024datalines:240-256Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
Climate change poses increasing challenges to Chinese cabbage ( Brassica rapa L. ssp. pekinensis ) production through unpredictable weather patterns that induce premature bolting and physiological disorders. Traditional breeding programs rely on labor-intensive visual assessment that cannot capture continuous developmental dynamics or precisely quantify stress responses across variable environments. This study validated and applied an automated high-throughput phenotyping system for evaluating seasonal adaptation in 134 Chinese cabbage genotypes across contrasting autumn (favorable) and spring (stressful) seasons in Taiwan. The system, based on a FieldScan gantry platform equipped with multispectral 3D scanners, operated autonomously 2-3 times daily, continuously monitoring morphological parameters (3D leaf area, digital biomass, plant height) and spectral indices (NDVI, PSRI) throughout the growth cycle. The system’s automated components -continuous data acquisition and real-time parameter extraction – generated approximately 100,000 data points from 63 morphological, spectral, and structural parameters during 6-week pre-harvest period. Subsequent quality and statistical analysis enabled objective genotype classification and breeding decisions. Automated measurements showed season-dependent associations with visual assessment scores (R² = 0.37-0.56 in autumn; R² = 0.73-0.80 in spring), with spring models substantially outperforming autumn models due to enhanced physiological differentiation under stress. Spring cultivation induced severe stress responses, evidenced by 71% increase in PSRI (0.12 vs. 0.07) and 26% increase in plant height, with bolting resistance emerging as the critical determinant of adaptation. A quantile-based multi-dimensional classification framework integrating seasonal composite scores and Euclidean distances stratified germplasm into actionable breeding categories: stable genotypes (3.7%), spring-specific types (0.7%), poor performers (13.4%), and intermediate materials (82.1%). Continuous temporal monitoring enabled early stress detection, with binned PSRI measurements predicting subsequent morphological development one week in advance (R² = 0.62). This integrated phenotyping framework provides efficient tools for accelerating climate-resilient breeding through objective genotype classification, early stress detection, and data-driven decision support, with potential adaptation to other vegetable crops and integration with IoT-based collaborative breeding network.
Why it matches plant phenotyping methods高スループット3D・マルチスペクトル表現型計測プラットフォームの検証と応用が研究の中心であり、形態・スペクトル形質の自動取得、抽出、予測性能、遺伝子型分類を評価している。
abstractThis study validated and applied an automated high-throughput phenotyping system for evaluating seasonal adaptation in 134 Chinese cabbage genotypes
Black locust (Robinia pseudoacacia L.) is a key tree species globally and in Hungary, valued for its economic benefits, adaptability, and ecosystem services. Despite its invasiveness and susceptibility to frost damage, its high-quality timber and significant nectar production make it economically important. This research, conducted as a collaboration between the Hungarian Forest Research Institute and the University of Debrecen, aimed to evaluate the applicability of remote sensing technologies in supporting black locust (Robinia pseudoacacia L.) research and monitoring efforts. A clonal trial established in 2020 in eastern Hungary aimed to assess the performance of newly bred black locust clones. Tree height was measured using both conventional ground-based methods and photogrammetric analysis of unmanned aerial system (UAS) data, enabling comparison between the two approaches. Tree vitality was evaluated through UAS-based multispectral analysis using vegetation indices, including NDVI, GNDVI, NDRE, and LCI. Our findings revealed no significant differences (p>0.05) between UAS-based and traditional height measurements, confirming UAS as a reliable tool. Clones »NK2« and »PL251« showed superior growth (height of 7.6 m and 7.4 m) and health, while »Üllői« cultivar performed the weakest (5.3 m). Strong correlations were found between some vegetation indices (NDRE and LCI) and tree heights (r=0.593 and r=0.587), emphasizing the potential of remote sensing in efficient forest management. This study highlights the value of integrating UAS technology in forestry, offering cost-effective, accurate and comprehensive data for improving black locust cultivation practices.
Why it matches plant phenotyping methodsUASの写真測量・マルチスペクトル解析により樹高と樹体活力を推定し、地上測定との比較検証を行っており、植物表現型取得手法が中心的です。
abstractTree height was measured using both conventional ground-based methods and photogrammetric analysis of unmanned aerial system (UAS) data, enabling comparison between the two approaches.
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.
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methods柑橘樹の樹高という植物形質の推定精度・正確度を、手測定、LiDAR、SLAM、AIフォトグラメトリで体系的に比較検証する研究であり、フェノタイピング手法の技術評価が中心です。
titlePrecision and accuracy of tree height estimation in citrus orchards: a systematic investigation of manual, airborne LiDAR, SLAM LiDAR, AI-driven photogrammetry
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 5 Sept 2026
Introduction With the continuous advancement of smart agriculture, multi-modal remote sensing based on unmanned aerial vehicles (UAVs) offers new technical approaches for monitoring and managing crop moisture in fields. However, significant challenges remain in developing high-precision field-scale crop Plant Moisture Content (PMC) prediction models and translating them into actionable irrigation strategies. Methods This study focuses on winter wheat, employing field experiments with PMC and water use efficiency (WUE) as indicators of crop water status. Vegetation indices (VIs) derived from UAV data were used to construct a leaf area index (LAI) inversion model. Crop Height was extracted from oblique photogrammetry point cloud data. By combining the Penman-Monteith equation with dual crop coefficients, an improved evapotranspiration (ET) model was developed, utilizing multispectral data from UAVs, thermal infrared data, point cloud-derived plant height, and LAI inversion results. Further utilizing VIs, temperature indices (TIs), and machine learning algorithms (Random Forest Regression (RFR), Back Propagation Neural Network (BPNN), Partial Least Squares Regression (PLSR), and Support Vector Regression (SVR), we established PMC prediction models for winter wheat at different growth stages. These models, integrated with WUE, form the basis for an irrigation scheduling optimization framework at the field scale. Results Results indicate that VIs, the difference between canopy temperature and air temperature (ΔT), Crop Water Stress Index (CWSI), and ET exhibit varying correlations with PMC during three critical growth stages of winter wheat, with ET showing the highest correlation during the jointing and heading stages (absolute correlation coefficient |r| ≥ 0.639). Compared to PMC prediction models constructed with different combinations of VIs, ET, VIs+ET, and VIs+TIs, the model employing the RFR algorithm with multimodal inputs (VIS+TIs+ET) demonstrated the best performance. The model’s predictive accuracy gradually improved across all growth stages, peaking during the grain-filling stage, with the coefficient of determination(R 2 ) of 0.900 and a normalized root mean square error (nRMSE) of 2.688%. Optimal WUE varied across growth stages under different irrigation treatments. The highest values were achieved at the jointing stage under treatment W3 (PMC = 81.8%), and at the heading and grain-filling stages under treatment W1 (PMC = 76.8% and 64.0%, respectively). Discussion The study suggests that stage-specific irrigation scheduling based on PMC thresholds can improve overall water use efficiency. This study shows that integrating multi-modal UAV data with machine learning and an improved ET model enables high-precision PMC monitoring, supporting data-driven irrigation scheduling in precision agriculture.
Why it matches plant phenotyping methodsUAVマルチモーダルデータと機械学習により、作物水分状態（PMC）、LAI、草高、蒸発散量を推定する手法を開発・評価しており、フェノタイピング手法が研究の中心である。
abstractCrop Height was extracted from oblique photogrammetry point cloud data.
Unmanned aerial vehicles (UAVs) are broadly used for high-throughput plant phenotyping, yet their long-term use in public-sector research is increasingly challenged by regulatory restrictions and reliance on proprietary platforms. This study presented a regulation-compliant, modular multi-sensor unmanned aerial system (UAS) designed to deliver flexible, high-quality phenotyping data without dependence on restricted ecosystems. A dual-mount, open-architecture payload integrated RGB, multispectral, and thermal sensors, enabling simultaneous acquisition of structural, spectral, and thermal information within a unified workflow. Field validation in a lantana (Lantana camara) breeding trial demonstrated high-precision multi-sensor data fusion and reliable trait extraction. Spatial co-registration achieved centimeter-level accuracy, with alignment errors of 0.88 cm (multispectral) and 3.23 cm (thermal) relative to the RGB reference. UAV-derived canopy height closely matched ground measurements (R2 up to 0.98; RMSE as low as 1.57 cm), while canopy coverage estimates showed consistency across sensing modalities (R2 = 0.99; RMSE = 0.02 m2). Calibrated thermal orthomosaics provided robust canopy temperature estimation (RMSE = 3.13 °C), supporting a quantitative assessment of plant physiological status. Together, these results demonstrate that a regulation-compliant, open-architecture UAV platform can achieve high accuracy in multi-modal phenotyping while maintaining flexibility and cost efficiency. This work demonstrates a scalable and sustainable framework for UAV-based phenotyping, enabling researchers to adapt to evolving regulations while advancing data-driven crop improvement.
Why it matches plant phenotyping methods植物フェノタイピング用のマルチセンサーUAVプラットフォームを設計・検証し、植物形質の抽出精度を評価しているため、方法が中心的である。
abstractThis study presented a regulation-compliant, modular multi-sensor unmanned aerial system (UAS) designed to deliver flexible, high-quality phenotyping data without dependence on restricted ecosystems.
Crop attribute detection, as a key component of intelligent agricultural harvesting machinery, plays a crucial role in harvesting efficiency, loss reduction, and autonomous operation control. Compared with existing reviews on artificial intelligence and sensing technologies in agriculture, this review focuses on crop attribute detection scenarios oriented toward the intelligent decision-making and control requirements of agricultural harvesting machinery. It mainly analyzes crop attributes that affect harvesting operations, as well as the sensors and algorithms involved in detecting these attributes, and further clarifies the relationship between detection methods and control decisions in agricultural harvesting machinery. For grain crops, the key attributes relevant to harvesting operations include plant height, plant density, spike number, crop lodging, canopy structure, and crop position. For fruit and vegetable crops, the key attributes relevant to harvesting operations include maturity, position, and quality. From the perspectives of multi-source data acquisition, data analysis, and attribute detection algorithms, the key technologies in the field of crop attribute detection are systematically summarized and analyzed, including sensors used in crop attribute detection, such as RGB, spectral, near-infrared, and LiDAR sensors, as well as data analysis and recognition approaches, such as image classification, object detection, and point cloud analysis. The complexity of field environments and the dynamics of machine operation are analyzed, highlighting the technical bottlenecks of current detection systems in environmental adaptability, real-time responsiveness, and resistance to interference. To address these challenges, feasible optimization directions were proposed, including multi-sensor fusion, weakly supervised learning, and few-shot learning. This review aims to provide systematic references and theoretical support for the coordinated development of crop detection and control decision-making in intelligent agricultural harvesting systems.
Why it matches plant phenotyping methods収穫機械向けではあるが、草丈・密度・穂数・倒伏・群落構造・成熟度など植物の形態・状態を検出するセンサーと解析手法を体系的にレビューしており、表現型取得法が中心である。
titleA Review of Crop Attribute Detection for Agricultural Harvesting Machinery
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).
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
Product demand and climate variability are progressively increasing the need for real-time, scalable crop monitoring to support varietal selection and in-season input optimisation. However, producers still have limited information on the temporal and spatial variability of cotton health and performance beyond point-scale field surveying. In addition, given cotton's high phenotypic plasticity, near real-time derived metrics are essential to improve input efficiency and strengthen long-term sustainability of the cotton industry in Australia. Therefore, we proposed a functional integrated predictive sensing framework to estimate and predict cotton canopy morphological (i.e., height) and productivity traits (i.e., dry matter and lint yield) across large plots (12m × 6m). Scalability was validated by applying the proposed framework to estimate and map cotton yield across commercial fields. To do this, we explored the accuracy of high-resolution multispectral imagery from two platforms (unmanned aerial vehicle (UAV) and PlanetScope (PS)) collected across two 144-plot trials for two cotton seasons. These were designed with a large range in nitrogen rates (N), shading, and two growth-regulator doses, thus, creating variable environments. Sensing metrics were obtained from UAV imagery (1.3-1.6 cm pixel size) acquired once in 2022/23 and eight times in 2023/24, while PS composites (3 m pixel size) provided near-daily coverage in both seasons. Time-series gaps were imputed using Savitzky-Golay smoothing in thermal time (GDD), enabling extraction of growth dynamic metrics (GDMs) as single-date (SD; e.g., peak canopy) and multi-date (MD; e.g., daily average growth rate) metrics. After reducing collinearity and dimensionality, random forest (RF), support vector regression (SVR), and gaussian process regression (GPR) were trained and interpreted with SHAP, for feature contribution. UAV single-date models (SD_ UAV) achieved strong accuracy for height (R 2 = 0.77), biomass (R 2 = 0.73), and yield (R 2 = 0.81). Incorporating UAV time-series metrics (MD_UAV) improved the performance R 2 = 0.87, 0.86, and 0.85 for height, biomass and yield, respectively. Application of the derived models using high resolution satellite data (MD_PS) for different farming systems showed highly significant accuracy (R 2 = 0.67) to predict cotton yield at aggregated field scale. As such, enabling the detailed spatial prediction of cotton yield within a field. It is anticipated that the proposed functional sensing framework will improve the estimation of key cotton production traits, supporting field- and within-field decision-making, ultimately contributing to more resilient and sustainable cotton production in Australia.
Why it matches plant phenotyping methodsUAV・衛星時系列画像から綿花の形態・生産性形質を推定するセンシング／予測フレームワークを開発・検証し、商業圃場へ適用しており、表現型取得手法が中心である。
abstractwe proposed a functional integrated predictive sensing framework to estimate and predict cotton canopy morphological (i.e., height) and productivity traits (i.e., dry matter and lint yield) across large plots (12m × 6m).
Thinning is a cornerstone of sustainable forest management (SFM), yet decisions are often constrained by subjective experience and a lack of quantitative spatial data. To address this, we developed a precision thinning framework for Cunninghamia lanceolata plantations by fusing terrestrial and aerial LiDAR data. Instead of relying on manual selection, this study established a quantitative mechanism for identifying harvesting targets. We employed a layer-stacking seed-point algorithm to accurately segment individual trees and extracted key parameters (tree height, DBH, and crown width) with high precision (Overall R2 ≥ 0.85). Furthermore, an objective multi-criteria weighting method (AHP – CRITIC) was constructed to prioritize thinning targets based on stand stability and growth status. Results indicated that the proposed framework achieved an individual tree segmentation F1-score of 89.24%. Simulated thinning based on the calculated weights significantly optimized the stand spatial structure: canopy openness increased by 11.8%, and spatial indices converged toward optimal ranges. Compared with conventional practices, the proposed approach effectively reduced growth dispersion and enhanced population coordination. These findings demonstrate that integrating multi-platform LiDAR with objective weighting algorithms offers a scientifically rigorous pathway for precision forestry, promoting both productivity and the long-term sustainability of plantation ecosystems.
Why it matches plant phenotyping methodsLiDAR融合による個体木セグメンテーションと樹高・DBH・樹冠幅の抽出、および精度評価が枯間伐フレームワークの中心的な技術要素であるため、植物表現型計測手法として採用する。
abstractWe employed a layer-stacking seed-point algorithm to accurately segment individual trees and extracted key parameters (tree height, DBH, and crown width) with high precision (Overall R2 ≥ 0.85).
Traditional field surveys of urban park biodiversity lack efficiency and scale, whereas LiDAR offers precise 3D vegetation quantification. This study investigates how 3D vegetation structural complexity impacts urban park plant diversity. We integrated Unmanned Aerial Vehicle (UAV) and handheld LiDAR data with ground-based quadrat surveys to capture comprehensive vegetation structures. Using six key 3D structural metrics, we modeled their relationship with plant diversity via Random Forest. Results indicate canopy height standard deviation (Hstd) primarily influences cultivated plant diversity, while the vegetation density index (VDI) drives spontaneous diversity. The model predicted species richness better (30.63% variance explained) than the Shannon index (7.63%). These drivers exhibited significant non-linear effects and potential ecological thresholds. A strong synergy emerged: when the vertical structure is complex and the 3D spatial density is high, the predicted plant diversity initially exhibits a trend of saturation and stabilization. Ultimately, multi-dimensional 3D vegetation structure proves to be a robust indicator of plant diversity. Our proposed multi-platform LiDAR fusion framework enables rapid, precise ecological assessments, providing methodological references and support for the transition from 2D to 3D green quality evaluation in the fine-scale management of similar cities.
Why it matches plant phenotyping methodsUAV・ハンドヘルドLiDARを融合し、植物群落の3D構造指標を抽出して植物多様性を推定する測定・解析フレームワークが中心であり、植物状態の定量的評価に該当する。
abstractWe integrated Unmanned Aerial Vehicle (UAV) and handheld LiDAR data with ground-based quadrat surveys to capture comprehensive vegetation structures.
Introduction Addressing the core bottleneck in traditional crop models-the disconnect between morphology and physiological function at the organ scale and their limited dynamic response to environmental changes-this study aimed to construct a multi-source data fusion maize growth model for simultaneous organ-scale simulation. Methods We developed a closed-loop Environment-Driven-Functional Response-Morphological Feedback (EDFM) architecture. By integrating environmental time-series data, RGB images, and 3D point clouds, we created a multimodal fusion model based on a gated attention network. This approach adaptively weights multi-source features and pioneers a bidirectional morphology-physiology feedback loop based on physiological development time (PDT) and NURBS surfaces. The WOFOST moisture response function was also improved. Results The model significantly enhanced the simulation accuracy of organ-scale growth, reducing the root mean square error (RMSE) for plant height by 74.6% through a morphology-physiology dynamic weighting mechanism. More fundamentally, it resolved the disconnect between morphological and physiological processes. The improved plant height prediction validates the model's effectiveness at the organ scale. Discussion The pioneering "physiology-morphology" parallel simulation architecture provides an interpretable theoretical model and robust quantitative tools for designing high-photosynthetic-efficiency plant architecture and enabling precision water-fertilizer management.
Why it matches plant phenotyping methodsRGB画像・3D点群・環境データを統合し、器官スケールの形態と生長をシミュレーションする手法を開発しており、植物形質（草丈など）の推定が中心的な技術貢献である。
abstractWe developed a closed-loop Environment-Driven-Functional Response-Morphological Feedback (EDFM) architecture.
This study evaluates the performance of LiDAR sensing and UAV photogrammetry for three-dimensional canopy reconstruction and structural parameter estimation in precision agriculture. Experiments were conducted in Sicily (Italy) on Moringa oleifera Lam. and Ficus macrophylla subsp. columnaris, representing contrasting canopy architectures. LiDAR and UAV data were used to generate canopy models and estimate canopy height, volume, and vegetation density. A voxel-based approach was applied to LiDAR point clouds to analyze internal canopy structure. LiDAR significantly outperformed UAV photogrammetry, achieving lower errors in canopy height estimation (RMSE = 0.19–0.21 m vs. 0.52–0.60 m) and canopy volume (3.5–4.2% vs. 13.7–16.1%). UAV photogrammetry provided reliable estimates of canopy surface but underestimated structural parameters in dense vegetation due to occlusion effects. Differences were more pronounced in Ficus macrophylla than in Moringa oleifera, highlighting the influence of canopy complexity. These findings demonstrate that LiDAR-derived structural metrics can improve canopy characterization and support precision agriculture applications such as biomass estimation, irrigation planning, and canopy management in Mediterranean cropping systems.
Why it matches plant phenotyping methodsLiDARとUAVフォトグラメトリによる樹冠の3次元再構成と、樹冠高・体積・密度推定を比較検証しており、植物形質取得手法が研究の中心です。
abstractThis study evaluates the performance of LiDAR sensing and UAV photogrammetry for three-dimensional canopy reconstruction and structural parameter estimation in precision agriculture.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Accurate seed-yield prediction is essential for optimizing nitrogen (N) management in buffalograss seed production. However, current UAV-based approaches often rely directly on vegetation indices (VIs), which provide limited physiological insight and not transfer well across growing seasons. To address this limitation, we developed a trait-based yield prediction methold that integrates UAV-derived plant height (PH) and canopy nitrogen concentration (CNC), representing crop structural and physiological status, respectively. Field experiments were conducted from 2022 to 2024 under seven N application rates. Using data from 2022 and 2023, we calibrated a quadratic PH-CNC model and then evaluated its predictive performance with an independent 2024 dataset. We also compared this framework with a conventional direct VI-based model. The trait-based model explained 89% of the variation in seed yield during calibration and showed better cross-year predictive performance than the VI-based model (R 2 = 0.70, NRMSE = 17% versus R 2 = 0.52, NRMSE = 22%). In addition, the model captured the decline in seed yield under excessive N input, indicating that it reflected biologically meaningful crop responses. These results demonstrated that combining structural and physiological traits can provide a more robust and interpretable alternative to conventional VI-based methods for UAV-based yield prediction. This framework has practical potential for improving precise and sustainable N management in buffalograss seed production.
Why it matches plant phenotyping methodsUAV由来の草高と群落窒素濃度を統合した形質ベース予測法を開発し、独立年データで検証・従来法と比較しており、表現型取得と解析手法が中心である。
abstractwe developed a trait-based yield prediction methold that integrates UAV-derived plant height (PH) and canopy nitrogen concentration (CNC)
Field / plotWhole plant / canopy / plot / fieldMorphology / geometry measurementPhysiological trait estimationBiomass / plant weightGrowth / development / phenologyPlant / canopy heightWater status / transpiration
This study aims to evaluate the potential of Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) based on signal-to-noise ratio (SNR) analysis for monitoring crop structure and moisture. Data were collected using a GNSS antenna placed within an experimental meadow located in NW Italy. GNSS-IR exploits the interference between direct and ground-reflected signals to derive physical parameters such as the vegetation phase center height and soil moisture. In this work, by analyzing and modeling the oscillations in SNR time series, the sensitivity to crop growth dynamics was assessed. Vegetation height and dielectric parameters were compared against corresponding ground-surveyed values collected using a ruler and buried soil moisture sensors. Results suggest that GNSS-IR can detect canopy height with a high degree of consistency (Pearson’s r = 0.89, MAPE = 18%). Results also show that changes in the amplitude and phase of the interference pattern are sensitive to biomass density and dielectric properties of the reflecting surface (r = −0.81 and r = 0.86 respectively). GNSS-IR observables were analyzed across four representative measurement campaigns capturing distinct seasonal stages of meadow development. Despite the limited temporal sampling (n = 4), the selected observations correspond to contrasting vegetation and soil moisture conditions, allowing the identification of systematic variations in crop biophysical properties. These findings open promising perspectives for the development of innovative monitoring strategies in precision agriculture, leveraging existing GNSS infrastructure to obtain key biophysical parameters with minimal additional equipment and operational complexity.
Why it matches plant phenotyping methodsGNSS-IRによる作物構造・水分の推定手法が研究の中心であり、植生高やバイオマス密度などの植物形質を地上測定と比較して技術検証している。
abstractThis study aims to evaluate the potential of Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) based on signal-to-noise ratio (SNR) analysis for monitoring crop structure and moisture.
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.
ABSTRACT Lodging is a major contributor to decreased yield in tef, a staple cereal crop in Ethiopia. Semidwarf varieties have been developed with a goal to increase yield through reduced lodging, but studying lodging susceptibility currently requires a labor‐intensive, imprecise, manual scoring method. Here we present workflows for analyzing tef stand height from UAS sensors across time to both predict lodging later in the season with early height and to measure the severity of lodging after a storm event. We compare 3D point clouds generated by photogrammetry from RGB images with those generated from LiDAR to estimate height, demonstrating that they produce similar results, despite differences in cost. Stand height and lodging can both be accurately measured with low‐cost UAS, reducing the need for manual measurements and increasing precision and temporal resolution in plant breeding programs.
Why it matches plant phenotyping methodsUAS画像・LiDARによるテフの草高と倒伏程度の推定ワークフローを開発・比較し、育種での測定精度向上を示す中心的な表現型計測研究。
abstractHere we present workflows for analyzing tef stand height from UAS sensors across time to both predict lodging later in the season with early height and to measure the severity of lodging after a storm event.
Reproduction assets foundThe paper's Data Availability Statement and Methods sections point to a public GitHub repository containing the authors' analysis code and associated data (including PheNode sensor data), plus the PlantCV-Geospatial package used for the RGB/LiDAR height and lodging analysis.Code · publicthe USDA NIFA AFRI (Grant Number
2022-
67021-
36467 to N.F.), and by the Bellwether Foundation.
Conflicts of Interest
Getu Beyene has patent “Lodging resistance in Eragrostis tef” pending
to Donald Danforth Plant Science Center.
Data Availability Statement
Code and data associated with this manuscript are available on GitHub
(https://github.com/danforthcenter/teff-manuscript).References
Abebe, Y., A. Bogale, K. Michael Hambidge, B. J. Stoecker, and R. S.
Gibson. 2007. “Phytate, Zinc, Iron and Calcium Content of Selected Raw
and Prepared Foods Consumed in Rural Sidama, Southern Ethiopia,
and Implications for Bioavailability.” Journal of Food Composition and
Analysis 20, no. 3: 161–168.
AssOpen asset ↗danforthcenter/teff-manuscriptpdf-raw-page:8 lines:1-98Code · publicyzing images of plants (Gehan
et al. 2017; Schuhl et al. 2026) that provides a framework for
measuring and storing observations extracted per object within
each image. All code associated with these analyses is available
on GitHub (https://github.com/danforthcenter/teff-manuscript),
as well as the PlantCV-
Geospatial package (https://github.com/danforthcenter/plantcv-geospatial). As observed in the ortho-
mosaic (Figure 1A), tef plots were planted under power lines in
the field, which could not be flown under due to UAS safety re-
strictions. Pixels belonging to powerlines needed to be removed
to measure plot heights. During import, PlantCV-
Geospatial
was used with a height percentile tOpen asset ↗danforthcenter/plantcv-geospatialpdf-raw-page:4 lines:1-107Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · checked 14 Sept 2026
Manual phenotyping of photosynthesis-related traits in rice is labor-intensive and limits the scale and temporal resolution of genetic analysis under field conditions. Here, we integrated unmanned aerial vehicle (UAV)-based high-throughput phenotyping (HTP) with genome-wide association studies (GWAS) to dissect the diversity and genetic architecture of photosynthesis-related traits in a large indica rice diversity panel (>300 accessions) evaluated across three dry seasons. A total of 45 traits, including UAV-derived NDVI, canopy height, and canopy temperature, together with leaf gas-exchange, stomatal, anatomical, and agronomic traits, were quantified. UAV-derived traits captured temporal growth and senescence dynamics and showed strong and consistent correlations with leaf photosynthetic rate, stomatal conductance, flowering time, biomass, and grain yield. GWAS identified multiple QTLs for photosynthetic and HTP traits, including a cross-year stable transpiration-rate QTL (qTRMMOL-2-2) and a photosynthetic-rate QTL (qPHOTO-1-2). Haplotype analyses revealed that the wall-associated receptor-like kinase gene OsWAK6 and the potassium transporter gene OsHAK1 were strongly associated with variation in photosynthetic rate and transpiration, respectively. Several elite accessions with consistently high photosynthetic performance carried superior haplotypes at multiple qPHOTO loci, suggesting their potential value for breeding. Together, our results demonstrate that UAV-based HTP provides reliable field-scale proxies for physiological performance, and that integrating HTP with GWAS can enable the identification of genetic targets for improving photosynthesis, water use, and yield potential in rice. • Forty-five traits, including HTP, photosynthesis, and leaf morphology, were measured across three dry seasons in diverse Indica rice. • GWAS identified genes linked to photosynthesis and stomatal density, aiding in breeding resilient, high-yield rice. • UAV-based HTP data effectively tracked plant growth and senescence, correlating with photosynthetic rate. • GWAS co-localization revealed shared QTLs, suggesting multi-trait regulation by common genes.
Why it matches plant phenotyping methodsUAVベースのHTPによる植物形質取得と生理性能の推定が研究の中心であり、45形質を大規模・反復的に測定し、信頼性や他の生理形質との相関も評価している。
abstractwe integrated unmanned aerial vehicle (UAV)-based high-throughput phenotyping (HTP) with genome-wide association studies (GWAS)
Maize ( Zea mays L.) is a globally significant crop that plays a crucial role in feeding the growing global population. Among its various traits, plant height is particularly important as it affects yield, lodging resistance, ecological adaptability, and other important factors. Traditional methods for measuring plant height often lack cost-efficiency and accuracy. In this study, we employed a light detection and ranging (LiDAR) sensor mounted on an unmanned aerial vehicle (UAV) to collect point cloud data from 270 doubled haploid (DH) lines. This innovative application of UAV-based LiDAR technology was explored for high-throughput phenotyping in maize breeding. We constructed high-density genetic maps and assessed plant height at both single-plant and row scales across multiple developmental stages and genetic backgrounds. Our findings revealed that for many varieties and small areas, single-plant-scale estimation accuracy was superior to row-scale estimation, with an R 2 of 0.67 versus 0.56 and an RMSE of 0.12 m vs . 0.17 m, respectively. Two high-density genetic maps were constructed based on SNP markers. In Sanya and Xinxiang, the F 1 DH and F 2 DH populations identified 12 and 20 QTLs (quantitative trait loci) for plant height, respectively. The study successfully identified and validated QTLs associated with plant height, revealing novel genetic loci and candidate genes. This research highlights the potential of UAV-based remote sensing to advance precision agriculture by enabling efficient, large-scale phenotyping and gene discovery in maize breeding programs.
Why it matches plant phenotyping methodsUAV搭載LiDARによるトウモロコシ草丈の高スループット推定を開発・評価し、単個体と列スケールの精度比較を行っているため、表現型取得法が研究の中心です。
abstractwe employed a light detection and ranging (LiDAR) sensor mounted on an unmanned aerial vehicle (UAV) to collect point cloud data from 270 doubled haploid (DH) lines
ABSTRACT Aims Natural woodland expansion into former agricultural land contributes to conservation and global reforestation goals. However, tree colonisation and woodland persistence depend on interactions among vegetation, land‐use history and suitable microclimatic conditions. Understanding these drivers is essential for anticipating woodland expansion outcomes over large spatial and temporal scales. Methods We combined airborne photogrammetry with field measurements to assess the long‐term success of Juniperus thurifera woodlands—a Natura 2000 priority habitat—across a 20‐km 2 region of central Spain. A canopy height model, calibrated with field biomass data, was used to map juniper biomass, while time series land‐cover maps estimated woodland age. This novel approach enabled evaluation of colonisation success using a space‐for‐time substitution along the expansion frontier. Results Over the past 34 years, land cover has changed markedly, with agricultural land declining by 75% and open woodland tripling. Juniper stands have expanded from steep slopes onto flatter terrain and into areas with lower irradiance. Increasing dwarf‐shrub density within stands reduced juniper biomass by up to 25% in the oldest woodlands. Higher solar exposure promoted faster biomass accumulation through time but limited biomass in younger stands. Contrarily, new stands under lower insolation showed greater biomass for their age, suggesting positive land‐use legacies where drought stress was reduced. Conclusion Overall, J. thurifera woodlands have expanded substantially over recent decades, yet growth constraints differ across the expansion front. With our innovative framework integrating high‐resolution photogrammetry and field‐based biomass models, our study underscores the interplay between local competition and abiotic factors in shaping woodland dynamics across space and time. This framework offers a valuable, scalable and transferable tool for monitoring and managing long‐term ecosystem dynamics across larger spatial and temporal scales and informing habitat restoration and conservation planning under changing environmental conditions.
Why it matches plant phenotyping methods航空写真測量とフィールド biomass データで樹木バイオマスを推定・地図化する手法が研究の中心であり、植物群落の形質を大規模に測定する実質的なフェノタイピング応用である。
abstractA canopy height model, calibrated with field biomass data, was used to map juniper biomass
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.
Rice plant height is a key indicator of crop growth and phenology, yet continuous daily estimation remains challenging under limited field observations. This study proposes an interpretable Bayesian LUT-based framework to estimate rice plant height from time-series, satellite-derived GCVI, and sparse in situ measurements. Daily plant height was estimated as a posterior-weighted ensemble of multiple LUT-derived heights, together with uncertainty reflecting ambiguity among plausible growth trajectories. Applied to rice paddies in Ryugasaki City, Japan, using Harmonized Landsat–Sentinel-2 data from the 2025 growing season, the method achieved R2=0.85 and RMSE = 7.08 cm on the validation dataset, outperforming simple baseline approaches. The estimated daily height time series also enabled evaluation of the timing at which plant height reached 70 cm, revealing clear spatial variability among fields and an associated uncertainty of approximately 10 days. Although this threshold was discussed with reference to previous studies on L-band SAR sensitivity, the present study relied solely on optical observations. Overall, the proposed framework provides a data-efficient and explainable approach for daily, spatially explicit rice growth monitoring, while current limitations include the single-region, single-year LUT construction and the simplified statistical assumptions used in the Bayesian weighting framework.
Why it matches plant phenotyping methods衛星由来時系列データからイネの草丈を推定する手法を開発し、検証データで性能評価しているため、植物フェノタイピング手法が中心です。
abstractThis study proposes an interpretable Bayesian LUT-based framework to estimate rice plant height from time-series, satellite-derived GCVI, and sparse in situ measurements.
This paper proposes a method based on UAV low-altitude photogrammetry and deep learning algorithms for corn crop growth monitoring. During the shooting process, a unified UAV photogrammetry strategy is set to ensure that the obtained images have high spatial resolution, and after pre-processing the original images, a convolutional neural network (CNN) model is utilized to extract features from the images and improve the accuracy of the CNN with the help of the idea of transfer learning. In addition, multi-scale feature fusion and attention mechanism are introduced to allow the model to focus on important location information, and weighted multi-task loss function is used to jointly optimize the multi-objective values such as plant height, leaf area index, and biomass. Experiments show that the method has good real-time performance and scalability while maintaining high prediction accuracy, providing an effective solution for crop monitoring in precision agriculture.
Why it matches plant phenotyping methodsUAV画像と深層学習を用いてトウモロコシの草丈、葉面積指数、バイオマスを推定する手法自体が研究の中心であり、植物形質推定の方法開発・応用に該当する。
abstractThis paper proposes a method based on UAV low-altitude photogrammetry and deep learning algorithms for corn crop growth monitoring.
Abstract. Effective crop monitoring is essential for optimizing agricultural practices and promoting sustainable production. This study explores the use of temporal Unmanned Aerial Vehicle (UAV) imagery to assess variations in crop growth dynamics across different developmental stages. UAV images were captured at five-day intervals, enabling the analysis of temporal changes in phenological parameters and plant health. Key vegetation indices and canopy height were derived at multiple time points and statistically evaluated to determine their effectiveness in monitoring crop development. The multi-temporal analysis identified the most informative vegetation indices and image processing techniques for assessing crop conditions. Results demonstrate that UAV-based temporal imaging offers valuable insights into crop growth patterns that are difficult to obtain through conventional monitoring approaches. The findings highlight the potential of UAV imagery as a practical tool for improving crop management by enabling timely and informed decision-making, ultimately contributing to enhanced yield and resource use efficiency.
Why it matches plant phenotyping methodsUAV画像を用いて作物の生育動態を時系列で評価し、植生指数と群落高を抽出・比較することが研究の中心であり、植物形質の取得手法として実質的です。
abstractUAV images were captured at five-day intervals, enabling the analysis of temporal changes in phenological parameters and plant health.
The article presents the developed algorithm and software for automated monitoring of strawberry plant growth using neural network technologies. The YOLO11x and YOLOx-seg models, pre-trained by transfer learning, are adapted to recognize and classify plants (plant class), leaves (leaf class), and a reference marker (ref_obj class) of a known size. Segmentation of strawberry leaves using the YOLO11x-seg model makes it possible to analyze the morphometric parameters of individual leaf plates (area, perimeter, roundness, aspect ratio). A set of RGB images (2000 pieces) obtained using a GoPro HERO11 camera under controlled laboratory conditions was formed and annotated, followed by augmentation to increase the model's resistance to variations in shooting conditions. The developed algorithm converts the coordinates of the bounding boxes and segmentation masks of recognized objects into metric units using calibration coefficients calculated from a marker of known size (100×100 mm). The software implemented using PyQt5, TensorFlow, Keras, and OpenCV libraries provides not only visualization of results but also data storage in a local SQLite database with the ability to export to JSON and Excel formats. Validation of the model showed high accuracy in detecting plant bounding boxes (mAP50 = 0.906) and leaf segmentation (mAP50 -mask = 0.625). The average processing speed was 20.3 ms/frame for detection and 34.5 ms/frame for segmentation. The measurement error was less than 3.5 % for the overall parameters of the plant and 5.2 % for the morphometric parameters of the leaves, confirming the effectiveness of the method for assessing the height, width and area of plants, as well as the analysis of the leaf apparatus. The research results show the promise of an approach for automating plant phenotyping in real time.
Why it matches plant phenotyping methods植物の成長・葉形態を画像から自動抽出するアルゴリズム、ソフトウェア、データセットを開発し、精度・処理速度・測定誤差を検証しているため、植物フェノタイピング手法が中心である。
abstractThe article presents the developed algorithm and software for automated monitoring of strawberry plant growth using neural network technologies.
Introduction. The projected growth of the global population poses a significant challenge in ensuring sufficient food production. Crop genetic improvement, essential to meet this demand, relies on advanced technologies to accelerate field phenotyping processes. Objective. To predict plant height and biomass yield in sorghum using photogrammetry and multispectral data acquired through small unmanned aircraft system (sUAS) flights. Materials and methods. Six sorghum genotypes were evaluated in Cañas, Guanacaste, Costa Rica, using a completely randomized design with eight replications per genotype. Multispectral sensor flights were conducted at selected phenological stages to generate vegetation indices, digital terrain models (DTMs), and digital surface models (DSMs). Manual plant height measurements were used for correlation and simple linear regression analyses, while biomass was predicted using random forest regression. Results. The DTMs and DSMs enabled reliable estimation of plant height during early growth stage (R² = 0.53) and achieved higher accuracy at later stages (R²= 0.76; RMSE = 0.13 m). Biomass prediction was most accurate at the booting stage (r= 0.72; RMSE = 1.40 t·ha-¹), with NDRE (Normalized Difference Red-Edge Index) and IKAW (Kawashima Index) identified as the most relevant spectral indices. Conclusions. The DTMs and DSMs derived from multispectral imagery accurately predicted plant height during later growth stages but were less accurate in early stages. Incorporating plant height alongside spectral indices into predictive models enhanced biomass yield prediction. The findings demonstrate that sUAS-mounted sensors and multispectral indices are valuable tools for phenotyping in sorghum breeding programs in Costa Rica.
Why it matches plant phenotyping methodssUASマルチスペクトル画像とフォトグラメトリから草丈・バイオマスを推定し、実測値との相関・回帰および予測精度を評価する手法中心の研究である。
abstractObjective. To predict plant height and biomass yield in sorghum using photogrammetry and multispectral data acquired through small unmanned aircraft system (sUAS) flights.
Monitoring the growth dynamics in field-grown cabbage is critically important for ensuring stable vegetable production and advancing precision agricultural management. However, conventional two-dimensional (2D) image-based monitoring approaches are limited to planar projection information and lack representations of spatial structural characteristics, rendering them inadequate for supporting high-precision, full-cycle phenotypic monitoring of cabbage under open-field conditions. In this study, a high-precision three-dimensional (3D) point cloud dataset covering the period from the seedling stage to maturity was constructed using depth cameras in conjunction with multi-view spatial registration techniques. Building on this dataset, an adaptive point cloud segmentation network designed for the whole-cycle growth monitoring was proposed, incorporating a Head Refinement Module (HRM), a Leaf Instance Segmentation Module (LISM), and Cross Module Interaction (CMI) to address leaf adhesion and head boundary delineation. Experimental results demonstrated that the proposed method consistently outperformed state-of-the-art models in both semantic and instance segmentation tasks. For semantic segmentation, the mean Intersection over Union (mIoU) reached 0.767, with a point classification accuracy of 94.8%. The model comprises 54.25 million parameters and achieves an average response time of 0.76 s. For instance segmentation, the Average Precision (AP) improved by 2.3% for cabbage heads and 3.8% for leaves, while the Average Recall (AR) increased by 6.9%. Growth parameters, including plant height and canopy spread, extracted from the segmentation results showed strong agreement with ground-truth measurements, with correlation of coefficients (R 2 ) exceeding 0.9 for plant height, canopy length, and canopy width. Leveraging these multidimensional phenotypic descriptors, the temporal dynamics of cabbage growth throughout the entire growth cycle were systematically characterized. Overall, this study enables dynamic monitoring of cabbage phenotypes across the full growth cycle, providing a novel technical pathway for extending 3D phenotyping from controlled environments to open-field applications and offering important support for precise crop monitoring and the development of digital twin agriculture.
Why it matches plant phenotyping methods3D点群データセット、セグメンテーションネットワーク、形質抽出を開発・検証し、圃場キャベツの草高や冠幅を定量化する植物フェノタイピング手法が研究の中心である。
abstracta high-precision three-dimensional (3D) point cloud dataset covering the period from the seedling stage to maturity was constructed using depth cameras in conjunction with multi-view spatial registration techniques.
Reproduction assets foundThe paper's authors publicly release their improved OneFormer3D point cloud segmentation code on GitHub; the cabbage 3D point cloud dataset is only available upon request.Code · publicThe code is available at https://github.com/PandaDalin/improve_oneformer3d. The data of this study are available from the corresponding author upon request.Open asset ↗PandaDalin/improve_oneformer3dhtml-lines:449-475Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Published25 Apr 2026ISPRS Journal of Photogrammetry and Remote SensingCited by 0 · OpenAlex ↗
This study compared 3D point cloud data derived from Structure-from-Motion (SfM) in 2021 and lidar in 2022 acquired using remotely piloted aircraft systems (RPAS). The overall objective was to develop and compare optical and active point cloud methods for deriving vegetation structures commonly measured in the field to quantify wildfire fuel distribution. The outcomes of the modelling framework were then applied to examine the impacts of mountain pine beetle (MPB) on canopy fuel load volumes in Jasper National Park prior to a high intensity wildfire in 2024. Tree species were classified using geographic object-based image analysis (GEOBIA) with an overall accuracy of ∼ 90%, with higher performance in relatively open canopies with minimal shadow. Photogrammetric and lidar point clouds resolved accurate individual tree height (R 2 = 0.96; 0.99, respectively) when compared to field measurements. Crown base height derived using a windowed point density approach improved agreement with field data (R 2 = 0.76; 0.91, respectively) and improved relative to previously reported methods. Across sites with varying MPB-induced tree mortality, plots dominated by dead conifers showed a redistribution of canopy fuels towards the ground compared to plots of mostly live conifers. This structural shift suggests increased ladder fuel development, reduced canopy continuity, and a heightened likelihood of surface to crown fire transition. The results demonstrate that RPAS point clouds can effectively characterize tree structure and improve crown base height estimation, supporting more accurate assessment of canopy bulk density. These measurements provide a viable alternative to labour-intensive field surveys and can then be used as calibration and validation data for broad-area forest assessment fuel modelling using airborne and satellite remotely sensed data.
Why it matches plant phenotyping methodsRPASのSfMおよびLiDAR点群を用いて樹高、樹冠基部高、林冠燃料構造を抽出し、現地測定と比較・検証している。個体・林分の植物構造測定法が研究の中心である。
abstractThe overall objective was to develop and compare optical and active point cloud methods for deriving vegetation structures commonly measured in the field to quantify wildfire fuel distribution.
Optimizing olive orchard management requires timely, per-tree data to enhance productivity and sustainability. Unoccupied aerial vehicle (UAV)-based red, green, and blue (RGB) imagery offers a low-cost solution for acquiring high-resolution spatiotemporal insights for orchard management, which are not yet common in Tunisia. This study monitored tree structural parameters, leaf area index (LAI), and leaf nitrogen content (%N DW) in two Tunisian olive orchards during 2022 and 2023. UAV-derived imagery was photogrammetrically processed into 3D point clouds and analyzed using an automated approach. Target variables of the automated approach included tree-wise estimates of height, projected crown area, and crown volume, as well as raster cell counts of the canopy cloud and spectral indices such as the normalized green-red difference index (NGRDI) and green leaf index (GLI). In addition, the estimated parameters per tree were used to model LAI and leaf nitrogen content. Analyses were conducted separately for trees represented by a high and a low number of points in the dense point cloud. Outcomes were compared to reference data collected in the field on dates close to the UAV flights. The findings showed strong relationships for the projected crown area (R2 = 0.82 and 0.91) and tree height (R2 = 0.89 and 0.88) when compared to reference values. Linear regression models for LAI (R2 = 0.73 and 0.68) and crown volume (R2 = 0.85 and 0.91) estimation also show strong relationships. However, leaf nitrogen estimation was not feasible from RGB spectral index values, as it showed a weak relationship (R2 = 0.34). A dataset with multispectral imagery could overcome this limitation but would increase costs, making it less suitable for the low-budget approach required in price-sensitive farming contexts, particularly in low-income regions.
Why it matches plant phenotyping methodsUAV画像から樹体形状・LAIなどの植物形質を自動推定し、圃場基準値との比較検証を行う手法が研究の中心である。
abstractUAV-derived imagery was photogrammetrically processed into 3D point clouds and analyzed using an automated approach.
Effective high-throughput phenotyping is crucial for modern plant breeding, yet the optimal image acquisition parameters for UAV-based systems in forage crops remain poorly defined. We optimized UAV-based phenotyping methods for a Megathyrsus maximus biparental population, examining how ground sampling distance (GSD), environment, and harvest date affect the accuracy of RGB-derived digital traits in predicting yield and canopy height. Machine learning algorithms and mixed model analyses were applied to evaluate predictive power and heritability. Pixel count and Haralick's entropy showed strong correlations with conventional yield measurements, particularly in Environment 2, while most vegetative indices were poor predictors. Integrating machine learning substantially enhanced predictive power for green and dry matter yield (r > 0.80). For canopy height, machine learning models achieved correlations of 0.71 with ground truth measurements despite weak pairwise correlations. Mixed model analysis revealed high broad-sense heritability (0.7 < H 2 < 0.87) for yield traits, pixel count, and entropy, while vegetative indices and canopy height showed greater environmental susceptibility. Moderate GSD resolutions (0.5–1.0 cm) consistently outperformed both very high (0.27 cm) and very low (1.5 cm) resolutions. Coincidence index analysis demonstrated 80% correspondence between top genotypes ranked by pixel count and conventionally measured dry matter yield. This study provides an optimized framework for UAV-based phenotyping in M. maximus , demonstrating that combining advanced digital traits with machine learning accurately predicts key agronomic traits and significantly enhances genotype selection efficiency in forage breeding programs.
Why it matches plant phenotyping methodsUAV画像取得条件、RGBデジタル形質、機械学習による収量・草高推定を最適化・検証する研究であり、植物表現型取得法が中心的です。
abstractWe optimized UAV-based phenotyping methods for a Megathyrsus maximus biparental population, examining how ground sampling distance (GSD), environment, and harvest date affect the accuracy of RGB-derived digital traits in predicting yield and canopy height.
Reproduction assets foundThe paper's data availability statement points to a public Mendeley Data repository containing the study's UAV-derived digital phenotyping and conventional trait datasets. No author analysis code repository is explicitly deposited.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://data.mendeley.com/datasets/jrrb76x82h/1 .Open asset ↗jrrb76x82h/1lines:435-487Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 15 Sept 2026
Introduction Herbicide dose-response assays are routinely implemented to compare herbicide resistance among weed biotypes, which requires plant biomass to estimate the dose that reduces growth by 50% relative to untreated plants (GR 50 ). The Phenospex TraitFinder is a high-throughput, non-destructive, digital phenotyping system that collects data from 7 spectral parameters and 13 morphological parameters, including Digital Biomass (DB), which offers the opportunity for researchers to eliminate the time and labor associated with manual biomass collection. However, DB is the product of 3D Leaf Area and Plant Height (PH) Mean, making it a measurement of plant volume and an indirect indicator of biomass. While DB is highly correlated with true biomass, digitally collected plant volume data has not been implemented for dose-response assays or assessed for accuracy relative to true biomass data. Additionally, inaccurate PH measurements could impact the accuracy of DB measurements. Methods This study sought to assess the accuracy and utility of DB and the 19 remaining parameters in dose-response assays by comparing dose-response curves and GR 50 estimates generated from digital data and fresh biomass (FB) data. Accuracy of PH measurements were also assessed by comparing digital and manual measurements with the paired t-test. Pre-emergence dose-response assays using fomesafen and atrazine were implemented with common lambsquarters ( Chenopodium album L.). At 21 days after treatment, manual measurements of FB and PH were collected following digital data collection. Results Consistently strong correlations ( r = 0.97, P < 0.05) were observed between digitally collected data and their equivalent manual measurements. Comparisons of the dose-response curves indicated that only 3D Leaf Area, DB, Convex Hull Area, Projected Leaf Area, and Voxel Volume Total generated highly similar curves and GR 50 estimates relative to FB data, indicating that any one or all of these parameters could be utilized instead of FB. Small differences (approximately 1.06 to 1.77 mm) between manual and digital PH measurements were identified with the paired t-test, but since DB consistently produced similar dose-response curves and GR 50 estimates relative to FB, these differences did not impact the accuracy of DB measurements. Discussion Without requiring manual biomass collection, turnaround time for dose-response and other phenotyping assays decreases and allows faster sharing of research. Furthermore, herbicide-resistant plants can be preserved for phenotyping at later growth stages, tissue collection, and to produce progeny for future experiments.
Why it matches plant phenotyping methodsデジタル表現型システムで植物体積・草丈などを取得し、手作業の生体重測定との精度比較および除草剤用量反応曲線への有用性を検証しており、表現型取得法が中心です。
abstractThe Phenospex TraitFinder is a high-throughput, non-destructive, digital phenotyping system that collects data from 7 spectral parameters and 13 morphological parameters
Reproduction assets foundThe paper's digital phenotyping dose-response datasets are publicly deposited: the data availability statement names Ag Data Commons DOI 10.15482/USDA.ADC/29815082 and a figshare link, both paper-specific. No author analysis code repository is explicitly stated.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: 10.15482/USDA.ADC/29815082 or https://figshare.com/s/64d1bbac59a95c4721f1 .Open asset ↗figshare · 10.15482/USDA.ADC/29815082lines:548-573Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
ABSTRACT Aims Natural woodland expansion into former agricultural land contributes to conservation and global reforestation goals. However, tree colonisation and woodland persistence depend on interactions among vegetation, land‐use history and suitable microclimatic conditions. Understanding these drivers is essential for anticipating woodland expansion outcomes over large spatial and temporal scales. Methods We combined airborne photogrammetry with field measurements to assess the long‐term success of Juniperus thurifera woodlands—a Natura 2000 priority habitat—across a 20‐km 2 region of central Spain. A canopy height model, calibrated with field biomass data, was used to map juniper biomass, while time series land‐cover maps estimated woodland age. This novel approach enabled evaluation of colonisation success using a space‐for‐time substitution along the expansion frontier. Results Over the past 34 years, land cover has changed markedly, with agricultural land declining by 75% and open woodland tripling. Juniper stands have expanded from steep slopes onto flatter terrain and into areas with lower irradiance. Increasing dwarf‐shrub density within stands reduced juniper biomass by up to 25% in the oldest woodlands. Higher solar exposure promoted faster biomass accumulation through time but limited biomass in younger stands. Contrarily, new stands under lower insolation showed greater biomass for their age, suggesting positive land‐use legacies where drought stress was reduced. Conclusion Overall, J. thurifera woodlands have expanded substantially over recent decades, yet growth constraints differ across the expansion front. With our innovative framework integrating high‐resolution photogrammetry and field‐based biomass models, our study underscores the interplay between local competition and abiotic factors in shaping woodland dynamics across space and time. This framework offers a valuable, scalable and transferable tool for monitoring and managing long‐term ecosystem dynamics across larger spatial and temporal scales and informing habitat restoration and conservation planning under changing environmental conditions.
Why it matches plant phenotyping methods航空写真測量と現地 biomass データで樹冠高モデルを較正し、ジュニパー biomass を広域推定する手法が研究の中心であり、植物群落の形態・生長形質を測定する実質的なフェノタイピング手法に該当する。
abstractA canopy height model, calibrated with field biomass data, was used to map juniper biomass
Biological control represents a valuable tool for the sustainable management of soil-borne diseases in tomato cultivation and relies on the availability of effective microbial solutions. Digital technologies can support to the ecodesign stage by accelerating the screening and selection of high-performing microbial biocontrol agents. In this study, a collection of eleven endophytic bacteria strains recruited from the tomato root endosphere and proved to be compatible with Trichoderma spp. (non-target effect), was characterized for antagonistic and biofertilization/biostimulant traits, and evaluated in planta against two major tomato pathogens: Fusarium oxysporum f. sp. lycopersici and Sclerotium rolfsii. Plant phenomics realized using the PlantEye 500 multispectral dual scanner, was used to screen the effectiveness of microbial agents determining plant performances under both infection and healthy conditions. Multivariate analysis of 20 digitally computed phenotypic traits helped the detection of Peribacillus sp. C5NA and Neobacillus sp. TR12 as highly effective against wilt, and capable of counteracting the reduction in leaf angle surface and chlorophyll: typical tracheofusariosis symptoms. On the other hand, Peribacillus strains TR2 and C6 treatments caused partial phenotypic recovery in plants affected by Sclerotium rot. Interestingly, Microbacterium sp. TR9, appeared to be multifaceted. It showed mild multisuppressivity against both pathogens coherently with the exhibited N-acetyl-b-glucosaminidase, polysaccharide breaking and in vitro antifungal activities. In addition, it also acted as a putative biostimulant in the absence of pathogens, increasing digital biomass, plant height, and NDVI, in line with its proven strong ability to produce ammonia, fix nitrogen, solubilize phosphates, and release indoleacetic acid. Overall, the integration of phenomics supported the high-resolution detection of plant responses and supported the identification of multifunctional microbial strains with biocontrol and biofertilization potential for sustainable tomato production.
Why it matches plant phenotyping methodsPlantEye 500によるマルチスペクトル表現型取得と20形質のデジタル解析が、微生物資材のスクリーニングおよび植物応答評価の中心的手法として用いられているため。
abstractPlant phenomics realized using the PlantEye 500 multispectral dual scanner, was used to screen the effectiveness of microbial agents determining plant performances under both infection and healthy conditions.
In this study, we addressed agricultural labor shortages by developing a smart farming sensor module that integrated low-cost environmental sensors with a multipoint soil moisture sensor to predict broccoli growth, plant height ( PH ), and leaf count (L n ). Multivariable regression confirmed that integrated solar radiation ( S ) was the most dominant factor, although broccoli growth involved a complex interplay of solar radiation, optimal temperature, humidity, and soil moisture. More importantly, the analysis revealed that the middle layer soil moisture (u m ) exhibited the strongest positive contribution to PH . This finding indicated that water availability in the main root zone was essential for vertical growth and highlighted the indispensability of multipoint sensing over conventional single-depth measurements to accurately model the intricate relationship between soil moisture and crop development. Moving forward, we aim to leverage the superiority of multipoint data to construct a sophisticated growth prediction model, thereby contributing to the optimization of irrigation and temperature management in smart farming systems.
Why it matches plant phenotyping methods環境・土壌水分センサーを統合した測定モジュールを開発し、植物高や葉数などの作物形質を予測する手法が研究の中心である。
abstractdeveloping a smart farming sensor module that integrated low-cost environmental sensors with a multipoint soil moisture sensor to predict broccoli growth, plant height ( PH ), and leaf count (L n ).
Unmanned aerial vehicles (UAVs) have become indispensable tools in precision agriculture and plant phenotyping, enabling the rapid, non-destructive assessment of crop traits across space and time. Equipped with RGB, multispectral, thermal, and other sensors, UAVs provide detailed information on canopy structure, physiology, and stress responses that can guide management decisions and accelerate breeding programs. Despite these advances, the downstream processing of UAV imagery remains technically demanding. Converting orthomosaics into standardized, biologically meaningful data often requires a combination of photogrammetry, geospatial analysis, and custom scripting, which can limit reproducibility and accessibility across research groups. We present drone2report, an open-source python-based software that processes orthomosaics from UAV flights to generate vegetation indices, summary statistics, derived subimages, and text (html) reports, supporting both research and applied crop breeding needs. Alongside the basic structure and functioning of drone2report, we also present five case studies that illustrate practical applications common in UAV-/drone-phenotyping of plants: (i) thresholding to remove background noise and highlight regions of interest; (ii) monitoring plant phenotypes over time; (iii) extracting information on plant height to detect events like lodging or the falling over of spikes; (iv) integrating multiple sensors (cameras) to construct and optimize new synthetic indices; (v) integrate a trained deep learning network to implement a classification task. These examples demonstrate the tool’s ability to automate analysis, integrate heterogeneous data and models, and support reproducible computation of agronomically relevant traits. drone2report streamlines orthorectified UAV-image processing for precision agriculture by linking orthomosaics to standardized, plot-level outputs. Its modular, configuration-driven design allows transparent workflows, easy customization, and integration of multiple sensors within a unified analytical framework. By facilitating reproducible, multi-modal image analysis, drone2report lowers technical barriers to UAV-based phenotyping and opens the way to robust, data-driven crop monitoring and breeding applications.
Why it matches plant phenotyping methods植物表現型取得のためのUAV画像処理ソフトウェアを開発し、植物高・倒伏などの形質抽出、マルチセンサー統合、再現可能な解析ワークフローを中心的に提示している。
abstractWe present drone2report, an open-source python-based software that processes orthomosaics from UAV flights to generate vegetation indices, summary statistics, derived subimages, and text (html) reports
Reproduction assets foundThe paper explicitly states that the code and data to reproduce its five case studies (thresholding, temporal vegetation indices, height analysis, multi-sensor index optimization, deep learning classification) are publicly available in the authors' GitHub repository, and the DRONE2REPORT software itself is released as Code · publicThe code and data to reproduce these case studies
can be found at https://github.com/ne1s0n/paper-drone2report (accessed on 13 April
2026).Open asset ↗ne1s0n/paper-drone2reportpdf-page:6 lines:1-59Plant phenotyping relevance match · UnverifiedbioRxiv · checked 15 Sept 2026
Background and AimsClimate gradients influence seed morphology, emergence, and early life-history traits with cumulative impacts to individual fitness. For ex situ seed collections, which represent an invaluable repository of potential trait information for species management and conservation, climate data can guide preservation of adaptive variation and inform deployment strategies for restoration. Here we leverage a range-wide ex situ seed collection of critically endangered black ash seeds (Fraxinus nigra) to evaluate how climatic gradients shape variation in morphology and early life-history. MethodsTo test how climate of origin, seed morphology, and early life-history interact to impact first year fitness, high-throughput X-ray imaging and neural network-based segmentation were used to quantify variation in seed morphology for 701 maternal lineages spanning 76 populations across the range of F. nigra. Following this, a subset of seeds were used to establish a common garden experiment and quantify variation in emergence, early life-history transitions, and their cumulative impact to first-year survival and growth. ResultsOn average, differences within-population explained [~]43% of the variability in seed morphology, while among-population differences explained [~]14%. This suggests that substantial genetic variation exists within populations for natural selection to act upon and differences have evolved among populations. Climate associations indicated warmer and drier environments predicted heavier seeds with faster developmental transitions and increased first-year height. Together, climate of origin, seed mass, and timing of developmental transitions best predicted cumulative fitness, with populations from more continental environments exhibiting greater survival and first-year height accumulation on average. ConclusionsOverall, these results highlight the importance of climate of origin, seed traits, and early developmental transitions to first-year fitness in a perennial tree species. This work demonstrates how ex situ collections can be used to identify climatically structured trait variation and guide conservation strategies aimed at maintaining adaptive potential under environmental change.
Why it matches plant phenotyping methods701系統の種子形態を高スループットX線画像とニューラルネットワーク分割で定量しており、形態表現型の取得・抽出が研究の主要な技術基盤である。
abstracthigh-throughput X-ray imaging and neural network-based segmentation were used to quantify variation in seed morphology for 701 maternal lineages spanning 76 populations across the range of F. nigra
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 · UnverifiedOpenAlex · checked 15 Sept 2026
Unmanned aerial vehicle (UAV)-based remote sensing is useful to understand crop growth conditions or grain yield potential, and monitoring forage maize (Zea mays L.) is particularly advantageous because its tall canopy makes manual measurements time-consuming. This study aimed to identify the optimal timing for effectively detecting maize growth variability using aerial photogrammetry with a UAV. We conducted weekly aerial photography of a maize field under variable nitrogen conditions to produce artificial growth differences. The results showed that the crop surface model (CSM) could effectively visualize maize growth differences after exceeding approximately 1.0 m, which corresponded to the internode elongation stage. Moreover, the determination coefficient between temporal CSM values and grain yield reached a peak of 0.7 approximately one week before silking. These results suggest that approximately one week before silking is the optimal time for CSM-based observations and the early detection of within-field maize growth differences and yield variability.
Why it matches plant phenotyping methodsUAV空撮とフォトグラメトリによる作物表面モデル（CSM）を用いたトウモロコシ生育差・収量変動の検出時期を検証しており、表現型取得法が研究の中心である。
abstractThis study aimed to identify the optimal timing for effectively detecting maize growth variability using aerial photogrammetry with a UAV.
Unmanned aerial vehicle (UAV)-based remote sensing is useful to understand crop growth conditions or grain yield potential, and monitoring forage maize (Zea mays L.) is particularly advantageous because its tall canopy makes manual measurements time-consuming. This study aimed to identify the optimal timing for effectively detecting maize growth variability using aerial photogrammetry with a UAV. We conducted weekly aerial photography of a maize field under variable nitrogen conditions to produce artificial growth differences. The results showed that the crop surface model (CSM) could effectively visualize maize growth differences after exceeding approximately 1.0 m, which corresponded to the internode elongation stage. Moreover, the determination coefficient between temporal CSM values and grain yield reached a peak of 0.7 approximately one week before silking. These results suggest that approximately one week before silking is the optimal time for CSM-based observations and the early detection of within-field maize growth differences and yield variability.
Why it matches plant phenotyping methodsUAV航空写真から作成した作物表面モデル（CSM）によるトウモロコシの生育差・収量変動の検出時期を評価しており、植物表現型の取得方法の技術的適用と評価が中心である。
abstractThis study aimed to identify the optimal timing for effectively detecting maize growth variability using aerial photogrammetry with a UAV.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Introduction High-throughput field phenotyping (HTFP) holds great potential for elucidating the relationship between genomes and phenotypes. However, obtaining high-quality three-dimensional point cloud data of field populations and achieving single-plant phenotypic analysis remain challenging. Methods This study develops an integrated framework for field crop reconstruction based on 3D Gaussian splatting, incorporating a geometry-aware dynamic constraint algorithm to achieve instance segmentation and extract key phenotypic traits of individual plants. Using 3D Gaussian splatting technology, field-scale cotton population modeling is accomplished, generating dense 3D point clouds for regions of interest. Furthermore, the concept of a crop localization domain is proposed, establishing a longitudinal mapping that associates plant positional coordinates with long-term phenotypic attributes. Finally, through a dynamic spatial constraint mechanism, the accuracy and computational efficiency of instance segmentation for crop population point clouds are significantly improved, enabling rapid extraction of individual plant traits such as cotyledon node height, plant height, and leaf area. Results The results demonstrate that PhenotypeAI successfully reconstructed nine cotton populations with PSNR exceeding 30.0 dB. It successfully extracted regions of interest from 403 cotton plants, achieving an average F-score of 91.32% for instance segmentation and an average accuracy of 91.35%. The extracted traits—cotyledon node height, plant height, and leaf area—exhibited strong correlations with manual measurements, with coefficients of determination ( R 2 ) of 0.90, 0.91, and 0.91, respectively. Discussion The proposed method provides a low-cost solution for high-throughput field phenotypic analysis of field cotton and improves the efficiency of cotton breeding.
Why it matches plant phenotyping methods3D再構成と動的空間制約による個体セグメンテーションおよび形質抽出が研究の中心で、綿花の草丈・葉面積などを検証しているため。
abstractThis study develops an integrated framework for field crop reconstruction based on 3D Gaussian splatting, incorporating a geometry-aware dynamic constraint algorithm to achieve instance segmentation and extract key phenotypic traits of individual plants.
Abstract Maize（Zea mays L.） is an important crop, and improving its productivity is required even under challenging conditions such as labor shortages and uncertain climate fluctuations. One approach to enhancing yield is utilizing crop data for cultivation management and yield prediction. However, efficient acquisition of such data remains constrained by various limitations. In this study, we developed a non-contact and labor-efficient method for crop data acquisition by generating 3D models of maize at the ripening stage using Neural Radiance Fields (NeRF). Segmentation was performed on the obtained point clouds to estimate plant height, leaf area, and leaf angle. The coefficients of determination (R2) were 0.903, 0.954, and -0.521, respectively, demonstrating high accuracy for plant height and leaf area even at the ripening stage, while reducing the time required for data acquisition by 93% compared to manual measurements. Nevertheless, some manual operations−such as removing kernels and separating overlapping leaves−were still necessary, and full automation was not achieved. The main sources of error were identified as reconstruction errors in the base during scale adjustment, excessive removal of leaf sheaths, and the curvature of individual plants. Furthermore, we examined how measurement accuracy was influenced by factors such as the time of day and cultivar. The proposed method is expected to contribute to the practical implementation of a labor -saving 3D measurement technique that supports yield prediction and growth diagnosis in maize.
Why it matches plant phenotyping methodsNeRFによる3D再構成と点群セグメンテーションを開発し、トウモロコシの草丈・葉面積・葉角度を推定して精度と誤差要因を検証しており、表現型取得法が研究の中心である。
abstractwe developed a non-contact and labor-efficient method for crop data acquisition by generating 3D models of maize at the ripening stage using Neural Radiance Fields (NeRF).
Abstract Modern agriculture faces the dual challenge of sustainably increasing food production while mitigating the environmental impact of intensive monocultures. Mixed cropping, which is the cultivation of multiple species or varieties, may provide ecological benefits that address productivity and environmental sustainability challenges. However, evaluating its multifunctionality in conventional agricultural field experiments is costly and labour-intensive, and small sample sizes and high spatial variability often make it difficult to detect the statistical significance of mixed cropping effects. This study aims to introduce and validate a high-throughput field phenotyping (HTP) framework that integrates aerial imagery obtained from unmanned aerial vehicles (UAVs) to efficiently assess the multifunctionality of mixed cropping systems. We conducted a field experiment comparing monocultures of oat, rye, and barley; intraspecific mixed cropping combining three oat varieties; and interspecific mixed cropping combining oat, rye, and barley. Using UAV-derived data across the entire field, including vegetation cover, plant height, and the normalised difference vegetation index, we evaluated five multifunctionalities (biomass production, spatial variability in biomass production, early canopy closure, lodging resistance, and lodging resilience). This framework reveals that mixed cropping outperforms monocropping in several key ecological functions. The proposed UAV-based HTP approach enables cost-effective, robust, and scalable evaluation of mixed cropping systems, facilitating their optimisation for multifunctionality and contributing to the advancement of sustainable agriculture.
Why it matches plant phenotyping methodsUAV画像を用いた高スループット圃場フェノタイピング枠組みを導入・検証し、植生被覆、草丈、NDVIから複数の植物形質・状態を抽出しており、フェノタイピング手法が中心的です。
abstractThis study aims to introduce and validate a high-throughput field phenotyping (HTP) framework that integrates aerial imagery obtained from unmanned aerial vehicles (UAVs)
Reproduction assets foundThe paper's Data availability statement explicitly deposits the datasets generated and analysed (UAV-derived phenotyping measurements) in a public Zenodo repository with a DOI matching an allowed URL.Dataset · publicThe datasets generated and analysed during the current study are available in the Zenodo repository, https://doi.org/10.5281/zenodo.17042273.Open asset ↗Zenodo · 10.5281/zenodo.17042273lines:197-235Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
Recent technological advancements employ imaging techniques to examine the morphological, physiological, and genetic differences among plant accessions, enhancing precision and productivity. High-throughput phenotyping serves as an essential method for selecting traits and reducing errors tied to manual data collection. However, the effects of camera-to-object distance in imaging acquisition for plant phenomics have received less attention. We analyzed the imaging parameters that define well the morphological characteristics of pepper and the effects of camera-to-object distance on the imaging of plant growth, leaf dimensions, fruit, and seed characteristics. Three camera-to-object distances (0.8, 1.0, and 1.2 m) were studied for the vegetative stage, and six camera-to-object distances (0.35-0.85 m) were used for the reproductive stages. The results demonstrated that imaging parameters such as Major (the longest line that can be drawn within the leaf) and Minor (the shortest line perpendicular to the major axis) are more effective for assessing canopy spread, while imaging Height provides a strong correlation (r = 0.9) for actual plant height measurement. An optimal camera-to-object distance of 0.8 m yielded better correlations for vegetative traits across all pepper genotypes, likely due to resolution factors at different growth stages. For fruit and seed traits, shorter distances of 0.55 m and 0.65 m were suitable. Additionally, the weights of fresh and dry fruit correlated highly with image area (r = 0.94 and 0.89, respectively, at 0.55 m). The studied pepper genotypes exhibited distinct seed characteristics, including variations in Roundness, Solidity, and Circularity. The imaging approach can accurately capture various plant characteristics and has the potential to replace traditional methods for assessing plants.
Why it matches plant phenotyping methodsRGB画像による植物形質取得を中心に、カメラ距離と撮像パラメータを最適化・検証しており、方法開発および技術検証に該当する。
titleDigitalised phenotyping of pepper (Capsicum spp.) using effective RGB imaging and optimised camera positioning.
Unmanned aerial vehicle (UAV)-based remote sensing is useful to understand crop growth conditions or grain yield potential, and monitoring forage maize (Zea mays L.) is particularly advantageous because its tall canopy makes manual measurements time-consuming. This study aimed to identify the optimal timing for effectively detecting maize growth variability using aerial photogrammetry with a UAV. We conducted weekly aerial photography of a maize field under variable nitrogen conditions to produce artificial growth differences. The results showed that the crop surface model (CSM) could effectively visualize maize growth differences after exceeding approximately 1.0 m, which corresponded to the internode elongation stage. Moreover, the determination coefficient between temporal CSM values and grain yield reached a peak of 0.7 approximately one week before silking. These results suggest that approximately one week before silking is the optimal time for CSM-based observations and the early detection of within-field maize growth differences and yield variability.
Why it matches plant phenotyping methodsUAV航空写真から作成した作物表面モデル（CSM）によるトウモロコシの生育差・収量変動検出について、観測時期と技術性能を評価しており、植物表現型取得法が中心である。
abstractThis study aimed to identify the optimal timing for effectively detecting maize growth variability using aerial photogrammetry with a UAV.
Key message The metabolomic landscape of dynamic heterosis for plant height was displayed in rice. Heterosis-associated pathways across most developmental stages were developed into robust pathway biomarkers. The development of robust biomarkers enables accurate prediction of complex phenotypes, which contribute to the advancement of precision breeding. However, the dynamic nature of biomarkers is often underestimated, as their quantitative changes during development are directly connected to phenotypic transformations, influencing crop agronomic traits. Here, we performed comparative metabolomic analyses to investigate dynamic heterosis for plant height in rice, which is an agronomic trait varying throughout development and is a key determinant of yield heterosis. We found that the levels of pyruvaldehyde were predictive of height heterosis specifically at the seedling stage, while 4-hydroxycinnamic acid positively correlated with height heterosis across four developmental stages. We identified metabolic pathways associated with height heterosis and found that metabolomic changes during the elongation stage had a greater impact than those in other stages. Finally, 11 heterosis-associated pathways were developed into pathway biomarkers using random forest analysis, enabling the prediction of height heterosis in an independent population under different growth conditions. We elucidate the metabolomic landscape of dynamic height heterosis in rice through the identification of heterosis-associated analytes and pathways across stages. Our findings provide a strategy to develop robust biomarkers for heterosis of important agronomic traits by integrating metabolic pathways involved in heterosis across most developmental stages, contributing to the establishment of precision pairing in hybrid crop breeding.
Why it matches plant phenotyping methods植物高の異型接合優勢という明示的な植物形質を、代謝経路バイオマーカーとランダムフォレストで予測する手法を開発し、独立集団・異なる栽培条件で検証しており、単なる代謝測定ではなく形質予測法が中心である。
abstractFinally, 11 heterosis-associated pathways were developed into pathway biomarkers using random forest analysis, enabling the prediction of height heterosis in an independent population under different growth conditions.
Accurate segmentation of fine-scale organs from 3D point clouds poses a substantial challenge in high-throughput plant phenotyping (HTP), where existing methods are hindered by the loss of topological features and the resulting low accuracy. To address this, we introduce CotSkNet, a semantic segmentation network based on structured representation learning. This approach is the first to define plant architecture as a knowledge-based intermediate representation that integrates geometric, topological, and hierarchical relationships, thereby incorporating topological information into the segmentation process. CotSkNet features an innovative Topological Geometric Feature Fusion Extractor and a Two-Way Reinforcement Module to efficiently extract and enhance salient features. Furthermore, the proposed Topological Attention Aggregator enables dynamic focus on key branch points. On a dataset of 403 field-grown cotton plants, our method achieved mean intersection over union values of 91.55%, 92.37%, and 98.21% for the main stem, fruiting branches, and leaves, respectively, far surpassing those of mainstream methods. Moreover, automatically extracted phenotypic parameters, such as plant height and fruiting branch length showed excellent consistency with manual measurements (R 2 > 0.91, root mean square error (RMSE) < 0.14). This study confirms that structured representation learning excels at capturing fine-organ phenotypes, providing an innovative analytical pathway for HTP in complex field crops.
Why it matches plant phenotyping methods3D点群の器官セグメンテーションと形質抽出手法を開発し、圃場データで精度検証しているため、植物フェノタイピング手法が研究の中心である。
abstractwe introduce CotSkNet, a semantic segmentation network based on structured representation learning.
Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 15 Sept 2026
Computer vision and Internet of Things (IoT) technologies offer robust solutions for plant phenotyping, but traditional mainstream segmentation methods often fail in high-density plantings with overlapping foliage. This study introduces an integrated phenotyping system combining automated data capture and high-temporal RGB-D imaging using off-the-shelf hardware (Intel RealSense D435 and Raspberry Pi) to generate 3D point clouds of lettuce under controlled greenhouse conditions. While recent agricultural applications have shown limited success and required domain-specific adaptations, Segment Anything Model (SAM) and FastSAM were demonstrated to achieve exceptional zero-shot segmentation performance for individual lettuce plants in high-density arrangements without additional training. This capability effectively addresses the traditional challenges of species-specific parameter tuning and extensive training data requirements and fine-tuning. By mapping 2D segmentation masks to corresponding 3D point clouds, the system accurately extracted key phenotypic traits, namely plant height, length, and width, from which area and volume were subsequently estimated, showing strong correlations with manual measurements for Rex and Rouxai lettuce cultivars. This high-temporal, non-destructive monitoring provided unique insights into plant growth dynamics. The study highlights distinct growth patterns among these cultivars, underscoring the importance of tailored phenotyping approaches to optimise crop management strategies. By addressing the limitations of existing phenotyping methods, this work advances precision agriculture technologies, offering a cost-effective and efficient solution for monitoring dynamic crop growth with potential applications across various crops and growing conditions.
Why it matches plant phenotyping methodsRGB-D撮像、3D点群、セグメンテーションを統合した植物表現型取得システムを開発し、草丈・長さ・幅などを抽出して手測定と検証しているため、方法が中心的である。
abstractThis study introduces an integrated phenotyping system combining automated data capture and high-temporal RGB-D imaging using off-the-shelf hardware (Intel RealSense D435 and Raspberry Pi) to generate 3D point clouds of lettuce under controlled greenhouse conditions.
PURPOSE: The leaf area index (LAI) is a crucial parameter for crop growth management. While UAV remote sensing has been utilized to estimate LAI at the plot scale, its application to complex farmland environments—characterized by heterogeneous backgrounds (e.g., soil, residue, and weeds)— has been less explored. METHOD: This study employed UAV-mounted hyperspectral and RGB sensors to gather data from both experimental plots and farmland environments. Data from diverse rapeseed cultivars and growth stages were used as the calibration dataset, while farmland-level data validated the models. The study compared three models: the PROSAIL model, an empirical model incorporating canopy spectral and morphological parameters without differentiating canopy cover types, and the proposed canopy morphological parameters (CMP) model. The CMP model estimated LAI using fractional vegetation cover (FVC) for sparse canopies and canopy height for closed canopies. RESULT: Despite challenges such as UAV image resolution and the limited availability of spatial data, the CMP model showed strong performance, with an R² of 0.779 and RMSE of 0.732. Although its R² was similar to that of the empirical spectral–morphological (ESM) model (R² = 0.780), the CMP approach achieved a notably lower RMSE (0.732 vs. 0.814). This improvement stems from its canopy-aware design, which adaptively uses fractional vegetation cover for sparse canopies and canopy height for closed canopies. Such differentiation enhances model stability and generalization in heterogeneous farmland scenes—conditions in which background interference and structural variability often degrade empirical models. In comparison, the PROSAIL model performed less accurately (R² = 0.618, RMSE = 1.094). CONCLUSION: These results highlight that the CMP model provides a robust and cost-effective solution for LAI estimation, supporting crop growth assessment and management in real farmland.
Why it matches plant phenotyping methodsUAV画像・センサーから rapeseed のLAIを推定するモデルを開発・比較し、異なる圃場条件で検証しているため、植物形質取得手法が研究の中心です。
abstractThe CMP model estimated LAI using fractional vegetation cover (FVC) for sparse canopies and canopy height for closed canopies.
Computer vision and Internet of Things (IoT) technologies offer robust solutions for plant phenotyping, but traditional mainstream segmentation methods often fail in high-density plantings with overlapping foliage. This study introduces an integrated phenotyping system combining automated data capture and high-temporal RGB-D imaging using off-the-shelf hardware (Intel RealSense D435 and Raspberry Pi) to generate 3D point clouds of lettuce under controlled greenhouse conditions. While recent agricultural applications have shown limited success and required domain-specific adaptations, Segment Anything Model (SAM) and FastSAM were demonstrated to achieve exceptional zero-shot segmentation performance for individual lettuce plants in high-density arrangements without additional training. This capability effectively addresses the traditional challenges of species-specific parameter tuning and extensive training data requirements and fine-tuning. By mapping 2D segmentation masks to corresponding 3D point clouds, the system accurately extracted key phenotypic traits, namely plant height, length, and width, from which area and volume were subsequently estimated, showing strong correlations with manual measurements for Rex and Rouxai lettuce cultivars. This high-temporal, non-destructive monitoring provided unique insights into plant growth dynamics. The study highlights distinct growth patterns among these cultivars, underscoring the importance of tailored phenotyping approaches to optimise crop management strategies. By addressing the limitations of existing phenotyping methods, this work advances precision agriculture technologies, offering a cost-effective and efficient solution for monitoring dynamic crop growth with potential applications across various crops and growing conditions.
Why it matches plant phenotyping methodsRGB-D画像、3D点群、セグメンテーションを統合して植物形質を抽出するフェノタイピングシステムの開発・評価が中心であり、手動測定との相関検証も行っているため。
abstractThis study introduces an integrated phenotyping system combining automated data capture and high-temporal RGB-D imaging using off-the-shelf hardware (Intel RealSense D435 and Raspberry Pi) to generate 3D point clouds of lettuce under controlled greenhouse conditions.
Agrophotovoltaic (APV) systems provide a unique opportunity for improving agricultural land-use efficiency by combining solar energy capture via photovoltaic panels with crop production. However, in-depth information on plant growth patterns within the spatially heterogenous microclimate created by the intermittent shading of APVs is largely missing. In the present study, we implement a customized robot-mounted 3D-multispectral imaging system to closely monitor the growth and spectral reflectance patterns of a conventional soybean cultivar “Eiko” (EK) and a chlorophyll-deficient mutant variety MinnGold (MG) under an APV system. Weekly trends in canopy morphometric features revealed significant variations in plant height, 3D leaf area, light penetration, and canopy volume across the APV field depending on the proximity with the overhead solar panels for both EK and MG, with plants receiving adequate rainfall and intermittent shade performing the best. Furthermore, although spectral indices exhibited variations between EK and MG due to intrinsic differences in pigmentation, symptoms of stress could be detected for both genotypes within rain-shaded areas of the APV plot. Hence, the present investigation depicts the potential for complementary usage of robotics and machine vision for high-precision high-throughput crop monitoring under APVs, which would enable better crop management within such non-homogenous cultivation systems.
Why it matches plant phenotyping methodsロボット搭載の3D・マルチスペクトル画像システムを構築・適用し、作物の形態形質とストレス状態を高精度・高スループットに取得しており、表現型取得手法が研究の中心である。
abstractwe implement a customized robot-mounted 3D-multispectral imaging system to closely monitor the growth and spectral reflectance patterns
Plant breeding has been used for over 10,000 years to adapt crops like wheat to different environments. Continuous breeding efforts have led to high-yielding and resilient wheat varieties, with Canada being the 6th largest producer in 2024-25. However, challenges like leaf rust, caused by Puccinia triticina Erikss., still impact global wheat production. Rapid pathogen evolution requires ongoing identification and deployment of novel resistance sources. Accurate phenotyping of key traits like disease resistance, winter survival, and plant height is a major bottleneck in wheat breeding. Advances in high-throughput (HTP) genotyping and phenotyping offer new opportunities to enhance genetic gain. This study integrated genetic mapping and UAV-based HTP approaches to evaluate leaf rust resistance and key agronomic traits in winter wheat. A doubled-haploid (DH) population (n = 130) developed from the cross W538/Emerson was evaluated for leaf rust resistance at the seedling and adult plant stages. Genotyping was performed using a 25K Infinium SNP array, and linkage and QTL analyses mapped resistance genes. Seedling-stage resistance was associated with a locus on chromosome 1B, while adult plant resistance was governed by multiple QTL, including QLr.umb-1B, QLr.umb-2A, QLr.umb-3B, and QLr.umb-4D. Lines carrying multiple resistance QTL exhibited enhanced leaf rust resistance, highlighting the importance of QTL stacking for durable resistance. UAV-based HTP methods were evaluated for assessing spring stand and plant height in winter wheat breeding nurseries. Manual ratings were compared with RGB and multispectral-based UAV metrics, including relative plant pixel area and vegetation indices like NDVI and EPVI. NDVI was the most robust method for spring stand assessment, with four times higher heritability than manual ratings. Manual measurements were more accurate than UAV-based methods for plant height, but SfM and LiDAR had comparable performance. This study highlights the complementary value of genetic mapping and UAV-based HTP in wheat breeding, emphasizing multi-QTL resistance for leaf rust and the potential of UAVs to improve phenotyping efficiency for key agronomic traits.
Why it matches plant phenotyping methodsUAV画像・マルチスペクトル・SfM・LiDARによる春季スタンドと草丈の測定を、手動評価と比較・検証しており、植物表現型取得法が研究の実質的な構成要素である。
abstractUAV-based HTP methods were evaluated for assessing spring stand and plant height in winter wheat breeding nurseries.
Unmanned aerial systems (UAS) equipped with multispectral sensors enable within-season crop phenotyping; however, conventional vegetation index–based approaches often lack accuracy in predicting crop growth and yield. This study evaluated the performance of three machine learning (ML) algorithms, support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), for predicting maize leaf area index (LAI), relative chlorophyll content (SPAD), aboveground biomass (AGB), plant height, nitrogen (N) uptake, and grain yield, and compared their performance with stepwise multiple linear regression (SMLR). Multispectral imagery (five spectral bands) was collected across multiple growth stages during the 2021 and 2022 growing seasons near College Station, Texas, under varying split N-fertilizer applications. Band reflectance and eight vegetation indices were used as model inputs. All ML models outperformed SMLR in predicting LAI, SPAD, AGB, N uptake, and plant height. SVM, RF, and XGBoost showed comparable performance for LAI (R² = 0.82–0.83), AGB (R² = 0.86–0.92), and plant height (R² = 0.95–0.97). However, XGBoost exhibited overfitting, resulting in lower validation accuracy for SPAD (R² = 0.54) and N uptake (R² = 0.68) compared with SVM (R² = 0.73 and 0.80, respectively). Grain yield prediction accuracy increased with crop maturity, with reproductive-stage imagery producing the highest accuracy across models (R² = 0.88–0.98), where all ML models outperformed SMLR. However, at the V6 growth stage, SVM and RF did not perform as well as SMLR. Overall, integrating ML with UAS-based multispectral imagery improved prediction accuracy across key maize phenotypic and nutrient traits, providing a robust framework for non-destructive, high-throughput phenotyping and precision N management.
Why it matches plant phenotyping methodsUASマルチスペクトル画像と機械学習により、作物の形態・生理・収量関連形質を推定する方法を比較評価しており、フェノタイピング手法が研究の中心である。
abstractThis study evaluated the performance of three machine learning (ML) algorithms, support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), for predicting maize leaf area index (LAI), relative chlorophyll content (SPAD), aboveground biomass (AGB), plant height, nitrogen (N) uptake, and grain yield
Mangrove forests are important coastal ecosystems under increasing anthropogenic threat, necessitating accurate and efficient monitoring, particularly on resource-limited small islands like Bangkobangkoang Island in Pangkajene and Islands Regency, South Sulawesi. This study aims to fill the gap in detailed inventory data by using Unmanned Aerial Vehicle (UAV) photogrammetry along with field surveys and Geographic Information Systems (GIS) to map out where the mangroves are and assess their height on the island, considering its unique coastal shape. The study found six types of mangroves: Rhizophora stylosa, Rhizophora apiculata, Rhizophora mucronata, Sonneratia alba, Avicennia marina, and Lumnitzera racemosa. Mapping the area showed that the most common mangroves were Rhizophora stylosa, covering 5.79 hectares, and Rhizophora apiculata, covering 4.06 ha, with Sonneratia alba covering 0.34 ha. A structural assessment showed that the height of the mangroves changed across the island, with taller stands in the north. The tallest Rhizophora apiculata trees were 14.94 meters tall, while the tallest Rhizophora stylosa and Sonneratia alba trees were 5.88 meters and 4.84 meters tall, respectively. The average heights of Rhizophora apiculata, Rhizophora stylosa, and Sonneratia alba are 5.65 meters, 3.12 meters, and 2.26 meters, respectively. Using detailed data from high-resolution UAVs gives us precise and complete information about the area, creating a strong foundation for conservation and management plans for small island mangrove ecosystems.
Why it matches plant phenotyping methodsUAVフォトグラメトリを中心に、マングローブの樹高という植物形態形質を取得・評価しており、単なる生態調査の補助測定ではなく、植物状態の空間的測定手法の実質的な適用である。
abstractusing Unmanned Aerial Vehicle (UAV) photogrammetry along with field surveys and Geographic Information Systems (GIS) to map out where the mangroves are and assess their height on the island
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · Crossref · checked 5 Sept 2026
Abstract Fitness costs of plant disease defence are often subtle and difficult to quantify. In this study, we therefore used comparative high-throughput phenotyping in two independent facilities to assess growth, morphology and physiology of potato (cv. Désirée) with high time-resolution monitoring different defence mechanisms under pathogen-free conditions. Plants were either treated weekly with the resistance inducers β-aminobutyric acid (BABA; 10 mM) or potassium phosphite (KPhi; 36 mM) or comprised six transgenic lines expressing late blight resistance genes (single Rpi genes or a three-gene stack) or reduced jasmonate perception (StCOI1-RNAi). Over four weeks, image-derived traits revealed consistent cross-facility effects for plant height and colour: BABA treatment increased plant height but reduced canopy area and induced a paler greenness signature, whereas KPhi caused minimal and transient growth effects. Chlorophyll fluorescence at the NaPPI facility indicated reduced vitality (Rfd_Lss) in BABA-treated plants and increased Rfd_Lss following KPhi, while maximum PSII efficiency was largely unchanged. Several transgenic lines showed somewhat reduced above-ground biomass. Enzyme activity profiling produced distinct treatment and genotype signatures, but was strongly modulated by facility conditions that overrode these specificities. Overall, high-throughput phenotyping robustly detected subtle growth–defence trade-offs across platforms. Highlight High-throughput optical phenotyping validated across two independent research facilities reveals that stacked resistance genes and resistance inducers in potato trigger subtle growth trade-offs. Graphical abstracts Experimental timeline for high-throughput plant phenotyping platforms. Created in BioRender. Poque, S. (2026) https://BioRender.com/nmkve7g
Why it matches plant phenotyping methods二つの独立施設で高スループット光学フェノタイピングを比較・検証し、画像由来形質と蛍光指標の再現性を評価しており、フェノタイピング手法が研究の中心である。
titleComparative high-throughput phenotyping across two facilities reveals differential impact of defence mechanisms on plant growth and development
Effective monitoring of planted and natural forests is critical for assessing stand development and ensuring long-term ecological and economic success. However, traditional field-based inventories are labor-intensive and costly, limiting their applicability across large or inaccessible areas. Although unmanned aerial vehicles (UAVs) photogrammetry provides a scalable alternative, accurately delineating individual tree crowns in diverse and complex stand structures remains a significant challenge. We introduce and validate a cost-effective framework for automated individual tree inventory by integrating high-resolution imagery from a consumer-grade UAV with a two-stage deep learning pipeline. The framework employs a YOLO-based object detection model to localize individual trees, subsequently using these detections to prompt the Segment Anything Model 2 for precise, zero-shot tree crown segmentation. The framework was validated across diverse subtropical forests, including orchards, plantations, and natural forests. The deep learning models achieved high accuracy in detection (mAP50 = 0.881) and segmentation (mIoU = 0.854). The framework demonstrated robust performance in estimating horizontal structural parameters, especially in managed stands (R2=0.83 for orchards; R2>0.75 for plantations), and robust accuracy for tree height (R2>0.59). This fusion of consumer UAVs and foundation models offers a powerful, scalable tool for individual-tree-level inventory, with significant implications for precision silviculture and monitoring in subtropical forests.
Why it matches plant phenotyping methodsUAV画像と深層学習による個体樹冠 segmentation・樹木位置検出・樹高および構造パラメータ推定が研究の中心で、森林植物の形態形質を技術的に開発・検証している。
abstractWe introduce and validate a cost-effective framework for automated individual tree inventory by integrating high-resolution imagery from a consumer-grade UAV with a two-stage deep learning pipeline.
Global plant maps of plant traits, such as leaf nitrogen or plant height, are essential for understanding ecosystem processes, including the carbon and energy cycles of the Earth system. However, existing trait maps remain limited by the high cost and sparse geographic coverage of field-based measurements. Citizen science initiatives offer a largely untapped resource to overcome these limitations, with over 50 million geotagged plant photographs worldwide capturing valuable visual information on plant morphology and physiology. In this study, we introduce PlantTraitNet, a multi-modal, multi-task uncertainty-aware deep learning framework that predicts four key plant traits (plant height, leaf area, specific leaf area, and nitrogen content) from citizen science photos using weak supervision. By aggregating individual trait predictions across space, we generate global maps of trait distributions. We validate these maps against independent vegetation survey data (sPlotOpen) and benchmark them against leading global trait products. Our results show that PlantTraitNet consistently outperforms existing trait maps across all evaluated traits, demonstrating that citizen science imagery, when integrated with computer vision and geospatial AI, enables not only scalable but also more accurate global trait mapping. This approach offers a powerful new pathway for ecological research and Earth system modeling.
Why it matches plant phenotyping methods植物画像から複数の形態・生理形質を推定する深層学習フレームワークを開発し、独立データで検証・既存手法と比較しており、表現型取得・推定手法が研究の中心である。
abstractwe introduce PlantTraitNet, a multi-modal, multi-task uncertainty-aware deep learning framework that predicts four key plant traits (plant height, leaf area, specific leaf area, and nitrogen content) from citizen science photos using weak supervision.
Rice undergoes rapid growth and exhibits a high demand for nutrients during the tillering and booting stages. SPAD readings, which reflect relative leaf chlorophyll status, and leaf nitrogen content (LNC) are key indicators of plant nutritional status, directly influencing photosynthetic efficiency and biomass accumulation, while plant height (PH) reflects canopy structure and nutrient availability. Establishing quantitative relationships among these traits at key growth stages is essential for stage-specific precision rice management. In this study, Unmanned Aerial Vehicle (UAV) hyperspectral imagery and ground-truth measurements of SPAD, LNC, and PH were collected from rice fields in Qingbaijiang District, Chengdu, China. Twelve vegetation indices (VIs) were calculated, and three machine learning algorithms—partial least squares regression (PLSR), support vector regression (SVR), and random forest regression (RFR)—were employed to develop stage-specific retrieval models. A stage-specific modeling framework integrating PH with hyperspectral data was developed to statistically enhance estimation accuracy at the tillering and booting stages. The optimal models for SPAD readings and LNC achieved R2 values of 0.916 and 0.936, respectively. The results indicate that integrating canopy structural information with hyperspectral features can improve the estimation accuracy of SPAD-related chlorophyll indicators and nitrogen status in rice. Under the controlled field conditions of this study, the proposed framework provides a plot-scale proof-of-concept demonstration for UAV-based stage-specific nitrogen monitoring.
Why it matches plant phenotyping methodsUAVハイパースペクトル画像と機械学習を統合し、イネのクロロフィル指標・窒素状態を推定する段階別フェノタイピング手法を開発・評価しており、方法が研究の中心である。
abstractA stage-specific modeling framework integrating PH with hyperspectral data was developed to statistically enhance estimation accuracy at the tillering and booting stages.
Abstract Mobile laser scanning (MLS) provides detailed point cloud reconstructions of forest environments and has potential for operational forest sample-plot surveying. This study evaluated the accuracy of MLS in deriving forest inventory attributes, including basal area (G), number of trees per hectare (TPH), total stem volume (V), basal area-weighted mean tree diameter (Dg) and height (Hg), and dominant height (Hdom). Experiments were conducted in managed boreal forests across 44 sample plots (370–2000 m2) using a Faro Orbis MLS system. Field measurements collected tree-by-tree (n = 4472) with callipers and clinometers during the previous summer served as reference data. We compared two alternative MLS data acquisition trajectories—closed loops (MLS-loop) and line transects (MLS-line)—and two processing workflows: (i) manually assisted tree detection followed by automatic tree measurements, and (ii) a fully automatic workflow. MLS-line provided similar or marginally improved accuracy compared with MLS-loop; however, the substantially shorter acquisition time of MLS-loop (19.0 min per plot on average) favoured its operational use over MLS-line (30.5 min). Clearer differences emerged between processing workflows. The fully automatic workflow identified and measured 74.1% of trees with diameter at breast height (DBH) > 5 cm, whereas manual assistance in tree detection increased this proportion to 97.1%. DBH accuracy was similar for both workflows (root-mean-square-error [RMSE] ≈ 2.4 cm), but tree-height estimates were substantially less accurate under automatic processing (RMSE 6.2 m) than under the assisted workflow (RMSE 2.1 m). These differences propagated to plot-level estimates. Using the automatic workflow, RMSEs were 4.2 m2/ha for G, 610 trees/ha for TPH, 29.3 m3/ha for V, 2.3 cm for Dg, 1.6 m for Hg, and 1.9 m for Hdom. The assisted workflow notably improved accuracy, yielding RMSEs of 3.5 m2/ha for G, 54.0 trees/ha for TPH, 20.2 m3/ha for V, 1.2 cm for Dg, 1.3 m for Hg, and 1.2 m for Hdom when using closed-loop trajectories. Overall, the results emphasize the importance of assisted workflows for attributes sensitive to detection completeness, particularly TPH, while showing that kinematic MLS can efficiently capture forest structure for sample plot measurements.
Why it matches plant phenotyping methodsMLSによる樹木・林分構造形質の抽出精度を、取得経路と処理ワークフロー間で比較検証しており、フェノタイピング手法が中心である。
abstractThis study evaluated the accuracy of MLS in deriving forest inventory attributes, including basal area (G), number of trees per hectare (TPH), total stem volume (V), basal area-weighted mean tree diameter (Dg) and height (Hg), and dominant height (Hdom).
Service crops are grown to provide ecosystem services in viticulture, but their adoption remains limited due to their competition with grapevine for soil resources. To identify trade-offs between services, the effect of service crops management strategies on grapevine performances still need further research. This dataset presents data from two experiments conducted to study the effect of service crops management on soil resources and grapevine performances. The inter-row vegetation was sampled in two Mediterranean vineyards using quadrats for biomass estimation. In addition, an unmanned aerial vehicle (UAV) was regularly flown over the vineyards for a period spanning more than four years in total over the two vineyards. The dataset presented here includes both raw data acquired during fieldwork and processed data derived from this raw inputs. The raw data consists of image series captured by two UAVs during each flight campaign, including RGB and multispectral imagery. Images were acquired between 2021-06-10 and 2022-07-29 for the first vineyard, and between 2023-06-08 and 2025-03-12 for the second vineyard. Based on these raw data, the processed data comprises spatial vectors, raster layers, and dense point clouds generated from UAV images using a Structure from Motion (SfM) photogrammetry workflow, at a 5 cm spatial resolution. The raster layers and dense point clouds provide specific information on vineyard characteristics for each UAV flight date, including elevation, vegetation indices, visible and near-infrared reflectance, and canopy height. In addition, the processed data include measurements of vegetation dry biomass, as well as separate measurements of dry biomass and leaf area measured for selected service crops species. This dataset can be reused for the calibration and/or evaluation of classification algorithms aimed at discriminating vines from the inter-row vegetation, or as part of a larger dataset to explore relationships between remotely-sensed vegetation indices and field-measured vegetation biomass or surface.
Why it matches plant phenotyping methodsUAV画像とSfM処理により、植生指数・樹冠高・バイオマス等の植物形質を取得した再利用可能なデータセットで、分類アルゴリズムの校正・評価用途も明示されており、植物フェノタイピング手法・データ基盤が中心です。
abstractThe dataset presented here includes both raw data acquired during fieldwork and processed data derived from this raw inputs.
Reproduction assets foundThe paper is a Data in Brief article describing a public dataset on Research Data Gouv (doi: 10.57745/MXM55R) containing UAV RGB/multispectral imagery, SfM-derived rasters and point clouds, and field-measured vegetation biomass/leaf-area data from two Mediterranean vineyards — directly the paper's phenotyping inputs. ADataset · publicollected in vineyards located in southern France near Montpellier (43°32.5243′N, 3°50.8240′E). Data are stored on Research Data Gouv, a remote storage solution curated by the French Department of Research.
Data accessibility
Repository name: Research Data Gouv
Data identification number: doi: 10.57745/MXM55R
Direct URL to data: https://doi.org/10.57745/MXM55R
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The fine scale imaging of vineyards (i.e., 5 cm resolution) allows for classification of the vegetation in the vineyard inter-rows, and subsequent exploration of its respective dynamics.
•Open asset ↗Research Data Gouv · 10.57745/MXM55Rlines:1-47Plant phenotyping relevance match · UnverifiedOpenAlex · checked 13 Sept 2026
Due to climate change, drought periods are becoming more frequent and more intense, posing substantial stress to Central European forest stands, especially climatically sensitive conifer forests. The early detection and accurate spatial delineation of forest damage are essential for supporting adaptive forest management decisions. This study presents a two-tier, multi-step forest damage assessment approach that combines Sentinel-2 satellite-based NDVI double-difference analysis with UAV-based high-resolution photogrammetric evaluation. In the first phase, potential damaged forest patches were identified in two sample areas of the Sopron Mountains using double-difference maps derived from monthly window NDVI maxima calculated from Sentinel-2 data. In the second phase, UAV surveys were carried out over the selected forest compartments, resulting in individual-tree-level canopy segmentation and object-based NDVI analysis. The photogrammetric point clouds were combined with ground points derived from airborne laser scanning to enable the accurate generation of canopy height models. The results confirmed that NDVI double-difference analysis is suitable for the spatial detection of both gradual drought-related damage and sudden disturbances—such as forest fire—even under sequences of drought and moderate years occurring in a sporadic pattern. The UAV-based analysis corroborated the satellite observations in detail and enabled an accurate inventory of damaged trees as well as the exploration of their spatial distribution. The proposed methodology provides an efficient, cost-effective, and operational tool for multi-scale monitoring of forest damage, contributing to the timely recognition of climate-change impacts and to the substantiation of targeted forest management interventions.
Why it matches plant phenotyping methods衛星・UAV画像、NDVI差分、樹冠セグメンテーションを用いて森林の個体レベル損傷を抽出する手法が研究の中心であるため。
abstractThis study presents a two-tier, multi-step forest damage assessment approach that combines Sentinel-2 satellite-based NDVI double-difference analysis with UAV-based high-resolution photogrammetric evaluation.
Grassland canopy height is one of the most important traits for determining plant diversity and community structure, directly affecting the resource use efficiency of livestock in grassland ecosystems. However, broad-scale changes in grassland canopy height are seldom reported due to the complex effects of species aggregation on both interspecific and intraspecific structures. Here, we decouple grassland aboveground biomass into vertical and horizontal allocations, thereby offering a pathway to mirror changes in grassland canopy height. Grassland aboveground biomass is estimated using a machine learning algorithm by fusing climatic factors, satellite-driving metrics, and 8-year consecutive ground-truth surveys; the horizontal allocation of grassland aboveground biomass is derived from optimized linear spectral mixture analysis. We find that changes in horizontal biomass allocation primarily accounted for increases in Chinese grassland aboveground biomass from 2001 to 2022, resulting in a significant decline in grassland canopy height. The decline in grassland canopy height is shaped by reduced radiation and, more importantly, by the combined effects of warming and grazing, while also being related to variations in plant diversity. The dwarfing grassland community with declining canopy height may increase the impact of livestock disturbances, thus diminishing the resistance of grassland ecosystems to climate fluctuations.
abstractHere, we decouple grassland aboveground biomass into vertical and horizontal allocations, thereby offering a pathway to mirror changes in grassland canopy height.
This article describes a multi-sensor dataset collected during the TIRAMISU (Thermal InfraRed Anisotropy Measurements in India and Southern eUrope) campaign at the Nawagam research site in Gujarat, India, during the 2023 monsoon season. The objective was to acquire continuous ground-based optical and thermal measurements over a homogeneous rice canopy across different crop growth stages. The dataset integrates several complementary components. Thermal data were acquired with an Optris longwave infrared camera (8-14 µm) at high temporal resolution, capturing canopy temperature dynamics throughout the diurnal cycle. Optical data were obtained with a Micasense RedEdge-M multispectral sensor, providing imagery in Blue, Green, Red, RedEdge, and Near-Infrared bands with radiometric corrections. An Apogee radiometer supplied reference radiometric temperature. Meteorological measurements included air temperature, humidity, wind speed and direction, and net radiation. Ancillary field measurements comprised Leaf Area Index (LAI), plant height, emissivity sampling, hyperspectral observations, and crop stage information. The datasets are provided with metadata and processing workflows, including calibration procedures for optical reflectance and thermal radiance. Together, these components form a comprehensive record of canopy-atmosphere interactions over a homogeneous rice field. The datasets can support research on optical and thermal directional anisotropy, canopy radiative transfer, emissivity characterization, and crop biophysical parameter estimation. In addition, they are relevant for applications in vegetation monitoring, agricultural water stress assessment, and surface energy balance studies. By combining optical, thermal, and meteorological observations, the resource is suited for multidisciplinary investigations in remote sensing, agronomy, and environmental sciences.
Why it matches plant phenotyping methods光学・熱画像、校正手順、処理ワークフロー、LAIや草丈などの植物形質を含む再利用可能な作物キャノピーデータセットが研究の中心であり、植物表現型取得基盤として適格。
abstractThe dataset integrates several complementary components.
Reproduction assets foundThe paper is a Data in Brief describing the TIRAMISU rice-canopy dataset (thermal/multispectral images, meteorological, ancillary LAI/height, hyperspectral, emissivity) publicly deposited at doi.org/10.6096/1028, including processing scripts (Thermal_CSV_to_Image.py, MicaSense notebook) for reproducibility.Dataset · publicRepository name: Optical, Thermal Infrared, and Meteorological Dataset from the Thermal InfraRed Anisotropy Measurements in India and Southern eUrope (TIRAMISU) Rice Canopy Experiment
Data identification number: doi.org/10.6096/1028
Direct URL to data: https://doi.org/10.6096/1028
Instructions for access: Publicly accessible repository; representative subsets provided with metadata and processing scripts.
Related research article
Pinnepalli, C., Roujean, J.-L., Irvine, M., et al. [ 1 ]. Measuring and modelling directional effects in the frame of TIRAMISU. ISPRS Annals, X–3–2024 , 325–330. https://doi.orgOpen asset ↗doi.org · 10.6096/1028lines:49-77Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · checked 14 Sept 2026
Maize phenotyping remains a major bottleneck in genetic analysis and breeding. Despite advances in drones, field robots, and gantry phenotyping systems, ultra-affordable, high-throughput, field-based maize phenotyping at single-plant resolution is still lacking, largely due to the high cost, complex deployment, and limited flexibility of existing platforms under heterogeneous field conditions. To address these challenges, we propose a novel paradigm that integrates DIY imaging devices with customized computer vision–based analytics, and present GLiMPSe ( G iraffe + Li zard M aize P henotyping S yst e m), including two end-to-end phenotyping modules for maize plant architecture (the Giraffe module) and leaf traits (the Lizard module). The imaging device in the Giraffe module are built from modular electronics and 3D-printed parts from local retailers to achieve high-quality image acquisition. The Giraffe and the Lizard modules operate at speeds of 15 seconds and 8 seconds per sample, with costs of $379.1 and $241.1, respectively. Both modules feature fine-tuned YOLOv11x segmentation models for reliable and robust target segmentation, followed by customized Python-based analytical pipelines that enable precise extraction and quantification of phenotypic traits. This methodology achieves high accuracies ( R² ) for five key traits, including plant height (0.928), heights of above-ear leaves (0.87∼0.958), ear height (0.925), above-ear leaf number (0.837), and leaf width (0.937). To enhance accessibility, we developed user-friendly graphical interfaces and publicly released manually annotated datasets and source code to support broader adoption and further innovation. This work provides a practical and accessible solution for high-throughput field phenotyping and offers new opportunities for democratizing crop phenomics through affordable, open-source technologies.
Why it matches plant phenotyping methods低コストな撮像装置、コンピュータビジョン解析、形質抽出パイプライン、GUI、データセットとコードを統合したトウモロコシ表現型測定システムの開発・検証が研究の中心である。
abstractwe propose a novel paradigm that integrates DIY imaging devices with customized computer vision–based analytics, and present GLiMPSe
Accurate acquisition of phenotypic characteristics in protected crops is a crucial prerequisite for intelligent control and digital breeding in greenhouses. To accurately assess the phenotypic traits of protected lettuce, a specialized in situ phenotypic detection method has been developed. The Multimodal Features and Attention Mechanism for Phenotype Detection Model (MFAMNet) was developed for protected lettuce, employing a segmented multi-source image dataset for synchronous regression testing. The results revealed that the predicted values generated by MFAMNet exhibited a strong correlation with the measured values, achieving coefficients of determination of 0.96, 0.92, 0.95, 0.94, and 0.95 for plant height, crown width, leaf area, fresh weight, and dry weight, respectively. Ablation tests demonstrated that the deep learning detection framework based on multi-modal feature fusion significantly outperformed single-feature detection models, highlighting the advantages of integrating diverse data modalities. In addition, the multi-modal feature attention mechanism (MMF) facilitates both inter-modality and intra-modality interactions by capturing the global correlations between modalities and employing dynamic sparse spatial attention. The effectiveness of MMF has been validated through comparative experiments, demonstrating its suitability for the phenotypic detection of artificially cultivated lettuce. In summary, the method proposed in this study facilitates real-time monitoring of facility crops, enabling precise control of environmental parameters in protected agriculture and optimizing resource allocation. This approach contributes to the development of a comprehensive intelligent agriculture system and establishes a foundation for unmanned farms.
Why it matches plant phenotyping methodsレタスの草丈、株幅、葉面積、 fresh weight、dry weightを推定するマルチモーダル画像ベース手法を開発し、実測値との比較およびアブレーション・比較実験で検証しており、フェノタイピング手法が研究の中心である。
abstracta specialized in situ phenotypic detection method has been developed
Plant 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
Automated phenotyping of wheat growth stages from 3D point clouds is still limited. The study presents a concise framework that reconstructs multi-view UAS imagery into 3D point clouds (jointing to maturity) and performs plot-level phenotyping. A novel 3D wheat plot detection network—integrating spatial–channel coordinated attention and area attention modules—improves depth-direction feature recognition, and a point-cloud-density-based row segmentation algorithm enables planting-row-scale plot delineation. A supporting software system facilitates 3D visualization and automated extraction of phenotypic parameters. We introduce a dynamic phenotypic index of five temporal metrics (growth stage, slow growth stage, height/area reduction stage, maximum height/area difference stage, and height/area change rate) for growth-stage classification and yield prediction using static and time-series models. Experiments show strong agreement between predicted and measured plot heights (R 2 = 0.937); the detection net achieved AP 3D = 94.15 % and AP BEV = 95.35 % in “easy” mode; and a Bi-LSTM incorporating dynamic traits reached 82.37 % prediction accuracy for leaf area and yield, a 6.14 % improvement over static-trait models. This workflow supports high-throughput 3D phenotyping and reliable yield estimation for precision agriculture. • Developed a novel 3D wheat plot detection net with spatial–channel coordinated attention and area-attention modules, reaching 94.15% AP 3D and 95.35% AP BEV in high-precision mode, outperforming traditional methods. The CFPT 3D module boosts depth-direction feature extraction for dense planting. • Introduced 5 temporal phenotypic metrics (e.g., growth stage transitions, height/area change rates) to capture dynamic growth patterns. • Bi-LSTM models using these traits predicted yield with 82.37% accuracy, 6.14% higher than static-trait models. • Released a PyQt5-based 3D phenotype extraction tool for automated parameter calculation (height, canopy area, LAI) and visualization. • Proposed a density-based row segmentation algorithm enabling accurate row-level phenotyping, validated in single- and multi-row systems.
Why it matches plant phenotyping methods3D画像・点群から小麦区画の形態形質を抽出する手法、検出・行分割アルゴリズム、動的形質指標、ソフトウェアを中心的に開発・検証しているため。
abstractThe study presents a concise framework that reconstructs multi-view UAS imagery into 3D point clouds (jointing to maturity) and performs plot-level phenotyping.
Robust quantification of crop status in real-time is essential for agile decision-making. While use of unmanned aerial vehicle data (UAV) appears promising in this vein, the contribution and transferability of various features (e.g. vegetation indices, plant height and texture features) in crop above-ground biomass (AGB) prediction remain poorly understood. Here, our objectives were to (1) evaluate the performance of various machine learning (ML) algorithms in the synthesis of multiple features, (2) elicit the contribution of various UAV features, (3) assess the transferability of features across growth stages and sites. Four field experiments, incorporating several water and nitrogen treatments across two sites, were assembled for use in AGB prognostics. We invoked four ML algorithms—Random forest (RF), Lasso regression (LR), K-nearest neighbors (KNN) and a stacked ensemble integrating the three methods (SML)—to predict wheat AGB using multiple UAV data and phenological information. Additionally, interpretable ML techniques were employed to elucidate the influence of UAV features on AGB prediction across growth stages. Our results showed that all algorithms exhibited robust performance in predicting wheat biomass, with RMSE values of 1.64, 1.71, 1.71, and 1.57 Mg ha −1 for RF, LR, KNN, and SML, respectively. RF predominantly relied on plant height features, LR leveraged vegetation indices, and KNN prioritized texture features, while SML synthesized the advantages of multiple ML algorithms. Fusion of multiple datasets amplified model prognostic capacity and scalability, with R 2 and rRMSE of 0.92 and 22 % when using data from external sites. Features pertaining to vegetation indices and plant height during vegetative growth and around flowering had seminal contributions of model predictions. Texture features significantly reduced the saturation effect during the reproductive stage but diminished the model’s transferability during the vegetative stage. Complementarity among data types enhanced effectiveness of ensemble machine learning, which leverages strengths of diverse data to improve the accuracy and robustness of AGB predictions. Future studies could combine multiple sources of remote sensing, such as LiDAR and thermal infrared alongside system modeling, to improve ML accuracy and generalization capability.
Why it matches plant phenotyping methodsUAV由来の植物高・植生指数・テクスチャ等から小麦バイオマスを推定する機械学習手法を比較・検証し、異なる生育段階や圃場への転移性も評価しており、表現型推定法が中心である。
abstractevaluate the performance of various machine learning (ML) algorithms in the synthesis of multiple features
Sugarcane farming demands precise irrigation and vigilant health monitoring to maximize productivity, yet conventional approaches often fall short in efficiency and scalability. This paper introduces a self-sustaining IoT framework that leverages a wireless sensor network to track critical indicators—such as soil moisture, plant temperature, environmental conditions, groundwater levels, and crop height—in real time. Data is processed locally and relayed to a cloud server, enabling automated irrigation decisions informed by the Crop Water Stress Index (CWSI) and growth tracking through advanced image analysis. The system achieved a soil moisture measurement accuracy with a strong correlation (R² = 0.96) to gravimetric methods and a plant height measurement accuracy with a mean absolute error of 1.8 cm. Designed for energy independence, the system operates seamlessly in off-grid environments. Field results demonstrate key findings: 98.7% data transmission reliability, early stress detection 24-48 hours before visible symptoms, 15% water savings through precision irrigation, and continuous operation for 180+ days on battery backup. These outcomes position this solution as a practical advancement for modern, sustainable sugarcane cultivation.
Why it matches plant phenotyping methods植物の健康状態・温度・草丈をセンサーと画像解析で取得し、精度検証まで行うIoTフェノタイピング基盤が研究の中心であるため。
abstractThis paper introduces a self-sustaining IoT framework that leverages a wireless sensor network to track critical indicators—such as soil moisture, plant temperature, environmental conditions, groundwater levels, and crop height—in real time.
Cotton (Gossypium hirsutum L.) is a vital fiber crop that thrives with proper management. Accurate decision-making also improves cotton productivity. However, many field experiments assume uniform conditions and ignore within-field variability caused by nitrogen doses and cultivar differences. Therefore, this field experiment was conducted to develop UAV-based yield-prediction models for cotton under varying nitrogen doses. Five nitrogen treatments were applied as urea fertilizer rates: T0 (0 kg/ha), T1 (100 kg/ha), T2 (120 kg/ha), T3 (140 kg/ha), and T4 (160 kg/ha), with other fertilizer doses kept constant across two cotton varieties, CB Hybrid-1 and CB HYV-15. The findings revealed that increasing nitrogen fertilizer doses boosted vegetative growth and height, but yield decreased at the highest fertilizer rate for both varieties. The most efficient urea fertilizer treatments were T2 (120 kg/ha) for the high-yielding variety and T3 (140 kg/ha) for the hybrid variety. Stepwise Multiple Linear Regression (MLR) and Least Absolute Shrinkage and Selection Operator (LASSO) models were used to predict cotton yield during early growth stages using UAV-based vegetation indices. The Normalized Difference Red Edge (NDRE) was identified as the most effective predictor in the Stepwise MLR model, which performed well at 72 days after sowing (DAS) (R2 = 0.89). The LASSO model also showed strong performance at 79 DAS (R² = 0.75), with the Normalized Difference Vegetation Index (NDVI) as a key predictor. Overall, this study demonstrates that UAV-derived vegetation indices can effectively predict cotton yield and highlight the impact of nitrogen fertilizer on yield.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から植生指数を抽出し、綿花収量を予測する手法の開発・評価が研究の中心であり、単なる窒素処理実験における routine 測定ではない。
abstractthis field experiment was conducted to develop UAV-based yield-prediction models for cotton under varying nitrogen doses.
Lodging is a complex trait that limits wheat (Triticum aestivum L.) yield potential, and no single trait can fully capture lodging resistance. Identifying key traits and developing reliable, field-applicable indicators are crucial for breeding lodging-resistant cultivars. In this study, lodging resistance was systematically assessed in 274 wheat varieties across three consecutive growing seasons (2022–2024). Genotype, growing season, growth stage, and their interactions significantly affected lodging-associated traits, with a clear temporal alignment between meteorological conditions and lodging events. Comparative analysis between lodged and non-lodged plants revealed that lodging negatively influenced spike and kernel traits. Multivariate analyses indicated that height-related traits accounted for nearly 50 % of the phenotypic variance related to lodging resistance and showed negative correlations, while traits related to stem weight and fullness explained 24 % and 8 %, respectively. Among these, stem wall thickness (SWT), second basal internode fullness (SBF), single stem elasticity (SSE), and stem strength (SS) emerged as key positive contributors, whereas plant height (PH), center of gravity height (CGH), and basal internode lengths were negatively associated. Stepwise regression and path analyses further identified SWT and SBF as primary determinants of SS, while CGH was the key factor influencing SSE. Structural equation modeling demonstrated that height-related traits exerted significant negative effects on stem anatomical structure, mechanical traits, and lodging index. Furthermore, a novel lodging index, defined as the SSE-to-CGH ratio, was proposed. It exhibited a strong correlation with the comprehensive lodging score (D value) and high consistency with clustering results, providing a practical assessment tool. These findings provide valuable insights for assessing lodging resistance and guiding strong-stem breeding strategies in wheat.
Why it matches plant phenotyping methods小麦の倒伏抵抗性を複数形質から統合的に評価し、新規倒伏指数を提案して既存スコアやクラスタリング結果で検証しているため、形質評価手法の開発・検証が中心である。
abstractFurthermore, a novel lodging index, defined as the SSE-to-CGH ratio, was proposed.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 5 Sept 2026
Abstract Advances in automation, imaging, and artificial intelligence have enabled large-scale plant phenotyping, but image analysis remains a critical bottleneck for crop improvement and biological discovery. We developed an integrated multispectral phenotyping framework using imagery from the Texas A&M AgriLife Precision Automated Phenotyping Greenhouse and expanded Plant Growth and Phenotyping (PGP v2) data across maize, cotton, rice, and sorghum. The pipeline integrates pseudo-RGB generation, plant detection and segmentation, image stitching, vegetation-index analysis, texture analysis, morphological trait extraction, and temporal comparison of image-derived features to quantify changes in plant structure, spectral reflectance, and texture over time. Among the evaluated segmentation approaches, SAM v3 provided the highest and most consistent accuracy across diverse crop structures, although it required greater computational time than classical methods. SAM2Long maintained plant-instance associations across vertically stacked frames, while Scale-Invariant Feature Transform (SIFT)-based stitching reconstructed plant mosaics when individual plants extended beyond a single field of view. For each plant and imaging date, the pipeline generated an 863-dimensional feature vector spanning vegetation indices, spectral statistics, texture descriptors, and morphological traits. The framework was evaluated through two case studies: treatment-level temporal analysis of mutagenized sorghum lines and cold-stress phenotyping of maize using a separate imaging system. In both studies, the extracted features supported statistical and multivariate analyses of phenotypic variation and enabled separation of plants based on treatmentor stress-related responses. The combined dataset and workflow provide structured, automated, and well-documented phenotypic analysis across multiple crops, experimental settings, and imaging systems for controlledenvironment plant science and crop improvement. Plain Language Summary Temporal imaging of plants in controlled environments helps scientists better understand growth and biological processes. However, analyzing large volumes of images has been limited by a lack of automated tools. Multispectral imagery captures additional information about plant pigments, structure, and stress beyond standard color images. We developed an automated analysis pipeline that identifies individual plants, tracks their growth over time, and measures traits such as height, area, shape, texture, and vegetation indices. Using artificial intelligence, the system efficiently processes thousands of images to provide consistent and repeatable measurements. By integrating engineering and plant biology, this work supports data-driven decisions for crop improvement and agricultural research.
Why it matches plant phenotyping methods植物画像から形態・スペクトル・テクスチャ形質を抽出する統合パイプラインの開発と評価が中心であり、植物フェノタイピング手法として明確に該当する。
abstractWe developed an integrated multispectral phenotyping framework
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.
Introduction. The projected growth of the global population poses a significant challenge in ensuring sufficient food production. Crop genetic breeding, essential to meet this demand, relies on advanced technologies to accelerate field phenotyping processes. Objective. To predict plant height and biomass yield in sorghum using photogrammetry and multispectral data acquired through small unmanned aircraft system (sUAS) flights. Materials and methods. Six sorghum genotypes were evaluated in Cañas, Guanacaste, Costa Rica, using a completely randomized design with eight replications per genotype. Multispectral sensor flights were conducted at selected phenological stages to generate vegetation indices, DTMs (digital terrain models), and DSMs (digital surface models). Manual plant height measurements were used for correlation and simple linear regression analysis, while biomass was predicted using random forest regression. Results. DTMs and DSMs enabled reliable estimation of plant height during early growth stage (R² = 0.53) and achieved higher accuracy at later stages (R²= 0.76; RMSE= 0.13 m). Biomass prediction was most accurate at the booting stage (r= 0.72; RMSE= 1.40 t·ha-¹), with NDRE (Normalized Difference Red-Edge Index) and IKAW (Kawashima Index) identified as the most relevant spectral indices. Conclusions. DTMs and DSMs derived from multispectral imagery predicted plant height accurately in later growth stages but were less accurate in early stages. Incorporating plant height alongside spectral indices into models enhanced biomass prediction. The findings showed that sUAS-mounted sensors and multispectral indices are promising tools for phenotyping in sorghum breeding programs in Costa Rica.
Why it matches plant phenotyping methodssUASマルチスペクトル画像と写真測量からソルガムの草丈・バイオマスを推定し、手測定との相関、回帰、精度評価を行う方法中心の研究である。
abstractTo predict plant height and biomass yield in sorghum using photogrammetry and multispectral data acquired through small unmanned aircraft system (sUAS) flights.
Post-disturbance recovery is a central element of forest resilience against intensifying disturbance regimes. Although recovery signals are strong across Central European forests, the relative roles of different factors contributing to recovery remain incompletely understood. As climate change increasingly challenges recovery, elucidating these processes is essential to adapt forest management to changing climate and disturbance regimes. We extended and applied a biologically grounded model of forest growth to remote sensing data to quantify how management shapes two key drivers of canopy recovery—disturbance legacies and post-disturbance height growth—across Bavaria, Germany. We combined 23,036 ha of quality-filtered photogrammetric canopy height model data with a Landsat-based disturbance map, a forest ownership map and environmental covariates in a Bayesian modelling framework. Post-disturbance growth rates were governed primarily by forest type and site conditions, whereas management strongly influenced disturbance legacies, i.e. the remaining post-disturbance vegetation height structure on site. Legacies varied widely across management types: Federal and set-aside forests retained the highest level of disturbance legacies, while private forests had the lowest legacy levels. Despite marginally lower growth rates, set-aside areas had recovery trajectories that were comparable to managed forests. The median recovery time to 5 m mean canopy height was 14.3 years over all forest and management types. Set-aside areas exhibited the greatest variation in recovery trajectories. We here show that (i) management affects disturbance legacies more strongly than post-disturbance tree growth, (ii) set-aside areas do not differ in recovery speed from managed areas, and (iii) legacies are diversifying forest recovery trajectories, with potential implications for future forest resilience. Our results underline that the post-disturbance reorganization window is a crucial period for management to influence long-term forest development. The framework presented here provides a scalable approach to monitor structural recovery and guide adaptive forest policy and management under increasing disturbance. • Forest management in Central Europe affects post-disturbance recovery more via legacies than tree growth rates. • Set-aside forests recover their canopy height equally fast as managed forests in Central Europe. • Homogenizing and removing disturbance legacies can reduce forest canopy variation across forest stand development. • We combined a biological growth model with remote sensing data to assess forest canopy recovery.
Why it matches plant phenotyping methodsリモートセンシングによる林冠高構造の定量と生物学的成長モデルを組み合わせ、森林の構造回復をスケーラブルにモニタリングする枠組みが研究の中心である。
abstractWe extended and applied a biologically grounded model of forest growth to remote sensing data to quantify how management shapes two key drivers of canopy recovery—disturbance legacies and post-disturbance height growth—across Bavaria, Germany.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the analysis data and code on Zenodo with a public DOI, which is a paper-specific, publicly actionable asset for reproducing the forest recovery analysis.Code · publicthank three anonymous reviewers for providing helpful
suggestions on an earlier version of the work.
Appendix A. Supporting information
Supplementary data associated with this article can be found in the
online version at doi:10.1016/j.foreco.2026.123616.
Data availability
Data and code of the analysis are available at Zenodo: https://doi.org/10.5281/zenodo.17804070.References
Anderson-Teixeira, Kristina J., Miller, Adam D., Mohan, Jacqueline E., Hudiburg, Tara
W., Duval, Benjamin D., DeLucia, Evan H., 2013. Altered Dynamics of Forest
Recovery under a Changing Climate. Glob. Change Biol. 19 (7), 2001–2021. https://
doi.org/10.1111/gcb.12194.
Arano, Kathryn G., Munn, Ian A., 2006. Evaluating Open asset ↗Zenodo · 10.5281/zenodo.17804070pdf-raw-page:10 lines:1-55Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Published23 Feb 2026International Journal of Latest Technology in Engineering Management & Applied ScienceCited by 0 · OpenAlex ↗
Drone-based phenotyping using unmanned aerial vehicles (UAVs) has emerged as a revolutionary approach for high-throughput, precise, and scalable measurement of plant traits critical to crop improvement. This technology integrates advanced imaging sensors—including RGB, multispectral, hyperspectral, and thermal cameras—with sophisticated image processing and artificial intelligence algorithms to non-destructively capture key phenotypic data such as plant height, biomass, canopy temperature, maturity timing, and disease symptoms under natural field conditions. Compared with traditional manual phenotyping and satellite-based remote sensing, UAV phenotyping offers superior spatial and temporal resolution, enabling dynamic monitoring of complex traits such as drought tolerance and disease resistance. Applications span early stress detection, quantitative trait assessment, yield prediction, and accelerating breeding cycles by facilitating objective, rapid selection of superior genotypes across multiple crop species. Despite its transformative potential, challenges remain in standardizing protocols, managing large-scale complex datasets, integrating phenotypic with genomic and environmental data, and providing training resources for widespread adoption. Ongoing advancements in sensor technology, data analytics, open-source tools, and capacity building are poised to cement drone-based phenotyping as a cornerstone technology for sustainable, climate-resilient crop breeding and global food security.
Why it matches plant phenotyping methodsUAV画像・センサーによる植物形質計測を中心に扱う明示的なフェノタイピングレビューであり、手法の応用、技術、課題を総合的に論じている。
abstractDrone-based phenotyping using unmanned aerial vehicles (UAVs) has emerged as a revolutionary approach for high-throughput, precise, and scalable measurement of plant traits critical to crop improvement.
Sorghum is a globally important crop. Under the breeding goals of high yield and stress resistance, the precise selection of elite germplasm is crucial. Phenotypic parameters such as plant height and leaf area at the seedling stage are core indicators for evaluating growth vitality. However, traditional manual measurement is inefficient and error-prone, making it difficult to meet the needs of high-throughput research. To address this, this study proposes an improved model (PTV2-Fr) based on Point Transformer V2 (PTV2), which combines 3D point cloud technology to realize the automatic extraction of sorghum seedling phenotypic parameters and explores the regulatory effects of different gibberellin (GA 3 ) concentrations. In this study, videos of sorghum seedlings were collected using the relevant system of Nanjing Agricultural University, and reconstructed into.ply format 3D point cloud files via the open-source software Colmap. The core optimizations of the PTV2-Fr model are as follows: Firstly, it proposes a Multi-Radius Dual-Coordinate Attention (MRDCA) mechanism to address the problems of leaf overlap and uneven point cloud density, thereby enhancing feature discrimination ability; Secondly, it introduces a Point-Graph Invariant Feature Refinement (PG-InvFR) module to improve the sensitivity of the segmentation head to local geometric details; Thirdly, it constructs a composite loss function (EL Loss) combining class-weighted cross-entropy loss and Lovász loss to alleviate class imbalance and boost segmentation accuracy. We selected 50 valid datasets from 112 video groups, annotated into three categories: Stem, Leaf, and Pot. The results show that PTV2-Fr outperforms PTV2 by 2.5% in accuracy, with significant improvements in Recall and mean F1-score (mF1). Ablation experiments confirm the positive effects of MRDCA, PG-InvFR, and EL Loss. Furthermore, PTV2-Fr demonstrates good robustness in analyzing GA concentrations, revealing that 50-100 mg/L GA concentrations promote seedling growth, while concentrations exceeding 200 mg/L inhibit growth. The PTV2-Fr model provides an efficient solution for the automatic determination of sorghum seedling phenotypes, and the revealed GA 3 regulatory mechanism can offer theoretical references for high-quality seedling cultivation and hormone management.
Why it matches plant phenotyping methods3D点群分割ネットワークを開発・検証し、ソルガム幼苗の草丈や葉面積などの表現型形質を自動抽出する方法が研究の中心である。ジベレリン処理の解析は付加的な応用であり、方法論的貢献が明確。
abstractthis study proposes an improved model (PTV2-Fr) based on Point Transformer V2 (PTV2), which combines 3D point cloud technology to realize the automatic extraction of sorghum seedling phenotypic parameters
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
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.
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.
Aboveground biomass (AGB) is a critical indicator for assessing crop growth status and productivity, yet accurately linking fine-scale ground measurements with coarse-resolution satellite imagery remains challenging. Here, we propose an integrated ground-UAV-satellite framework that combines high-resolution UAV observations with an optimized systematic sampling-Global Moran's I (SS-GMI) procedure and a simple allometric growth model. Multi-variety sugar beet cultivated across heterogeneous habitats was used as a case study. Results indicate that a power-law model effectively captures the allometric relationships between AGB, plant height, and the Dreg vegetation index in sugar beet, achieving high accuracy and strong transferability. Incorporating phenological information from Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie (BBCH) codes and a thermal index further enhanced model robustness across independent habitat trials, yielding coefficients of determination ( R 2 ) of 0.80 and 0.83. The SS-GMI sampling procedure integrates systematic sampling with Global Moran's I to reduce spatial autocorrelation while ensuring uniform spatial coverage, thereby enabling the acquisition of representative and spatially independent samples from UAV-derived AGB maps. These samples were used to develop satellite-based AGB estimation models for PlanetScope and Sentinel-2A imagery, achieving R 2 values of 0.83 and 0.73, respectively. This study provides a practical and scalable framework for field-to-satellite AGB upscaling, offering new insights for the scale conversion of multi-source data in agricultural remote sensing.
Why it matches plant phenotyping methodsUAV・衛星観測とSS-GMIサンプリング、モデル化を組み合わせ、サトウダイコンの地上部バイオマスという植物形質を推定する統合手法が研究の中心である。
abstractHere, we propose an integrated ground-UAV-satellite framework that combines high-resolution UAV observations with an optimized systematic sampling-Global Moran's I (SS-GMI) procedure and a simple allometric growth model.
Abstract Aims This study evaluated the suitability of root electrical capacitance measurements for nondestructive plant phenotyping in a free-air CO 2 enrichment (FACE) experiment. Methods A two-year FACE study was conducted with maize grown under ambient and elevated [CO 2 ], and low and high nitrogen supply in three replicate plots. The saturation root electrical capacitance (C R *) was monitored during the plant growth cycle. Aboveground plant parameters were measured in situ at flowering. Results Capacitance measurements revealed a seasonal pattern in root development with a peak at flowering, and the positive effect of higher nitrogen dose and [CO 2 ] enrichment on plant growth. At anthesis, C R * was significantly ( p < 0.001) and linearly correlated with stem basal area (R 2 : 0.51–0.68), aboveground biomass index (basal area × plant height; R 2 : 0.47–0.62) and leaf chlorophyll concentration (R 2 : 0.40–0.56). However, the best correlation (R 2 : 0.73 and 0.74) was found for plant leaf area, which is closely related to root water uptake, suggesting that the applied current signal penetrated the roots, and that the capacitance method directly measured root status in the field. In addition, C R * at flowering was a reasonable early predictor of maize grain yield (R 2 : 0.58 and 0.64) under our experimental conditions. Conclusions The electrical capacitance method proved to be a practical high-throughput tool for phenotyping not only the root but the whole plant in the field. Being noninvasive, it is particularly beneficial in FACE systems, where destructive sampling and soil disturbance should be minimized. It would also provide cost-effective support for breeding stress-tolerant and climate-resilient crops. Graphical Abstract
Why it matches plant phenotyping methods根の電気容量測定を非破壊・高スループットな植物フェノタイピング手法として評価し、圃場での相関および予測性能を検証しているため、方法が研究の中心である。
abstractThis study evaluated the suitability of root electrical capacitance measurements for nondestructive plant phenotyping in a free-air CO 2 enrichment (FACE) experiment.
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.
This data descriptor presents a dataset comprising crop and soil parameters measured in winter wheat fields near the town of Knezha, Bulgaria. The data were collected as part of a project evaluating the potential of vegetation indices derived from Sentinel-2 satellite imagery to predict biophysical and biochemical crop parameters. The core dataset consists of measurements obtained from 20 m × 20 m field plots and includes a broad range of parameters: leaf area index, fraction of absorbed photosynthetically active radiation, vegetation cover fraction, chlorophyll content, above-ground biomass, plant nitrogen content, biological yield, surface soil moisture, spectral reflectance, plant density, crop height, visual assessments of disease or pest damage, and data on weed occurrence. The dataset is complemented by unmanned aerial vehicle imagery, crop calendars, and field management information. The main soil types in the study area were characterized through soil profiles, while meteorological data were obtained from an automated weather station. The data were collected during the 2016–2017 and 2017–2018 agricultural seasons. The dataset is freely available for download and serves as a valuable resource for researchers in remote sensing—particularly for validating satellite-derived products—as well as for specialists involved in winter wheat monitoring, modeling, and agronomic studies.
Why it matches plant phenotyping methods冬小麦の複数の植物形質を含む再利用可能なデータセットを提示し、UAV画像や衛星由来指標の検証を主目的としているため、植物フェノタイピング用データセットとして採用。
abstractThis data descriptor presents a dataset comprising crop and soil parameters measured in winter wheat fields near the town of Knezha, Bulgaria.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicDataset: In situ and UAV dataset with crop and soil parameters obtained from winter
wheat fields. https://doi.org/10.5281/zenodo.17475742.Open asset ↗zenodo · 10.5281/zenodo.17475742pdf-page:1 lines:1-56Plant 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
Abstract Accurate and efficient assessment of forest structure is crucial for both ecological research and effective forest management. This paper introduces DendRobot, an innovative software pipeline developed to automate the inventory of forest sample plots or entire forest stands using terrestrial LiDAR scans or ground-based photogrammetric point clouds. DendRobot incorporates a novel 2D density-based tree-detection algorithm (Detection Rate = 93%) alongside a new vertical clustering approach for estimating tree height. Both methods are implemented together with established and widely trusted methods to process three-dimensional data into GIS layers. By leveraging these algorithms, DendRobot derives key forest inventory metrics of individual trees, including diameter at breast height (Mean Absolute Error = 3.4 cm), tree height (Mean Absolute Error = 0.7 m), tree locations, and crown projection areas at a fine spatial scale with the resolution of individual trees. Additionally, it produces Digital Terrain Models (DTMs), Digital Surface Models, and Canopy Height Models (CHMs) with user-defined resolution, supporting advanced spatial analyses of forest environments and providing information for forest management planning. Optionally, these data can be enriched with individual-tree point clouds, segmented by a novel approach. Designed as a comprehensive tool for forest researchers, managers, and students, DendRobot supports efficient, data-driven decision-making with minimal manual intervention. Initial tests conducted in complex forest environments demonstrate its capacity to streamline workflows and generate forest-stand-scale inventory data with accuracy comparable to state-of-the-art methods and software. DendRobot (available at https://www.dendrobot.czu.cz/) is a user-friendly, free and open-source solution for the practical application of terrestrial LiDAR scanning in real-world forestry challenges.
Why it matches plant phenotyping methodsLiDAR・写真測量点群から個体樹木の胸高直径、樹高、位置、樹冠投影面積を抽出する新規アルゴリズムとソフトウェアを開発・検証しており、植物形質取得が中心である。
abstractThis paper introduces DendRobot, an innovative software pipeline developed to automate the inventory of forest sample plots or entire forest stands using terrestrial LiDAR scans or ground-based photogrammetric point clouds.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 15 Sept 2026
Hyperspectral imaging (HSI) has emerged as a powerful tool for precision agriculture, enabling the non-destructive monitoring of crop biochemical and physiological traits. However, HSI alone lacks structural context, which limits its ability to accurately capture complex canopy architectures and organ-level traits. Integrating HSI with depth-sensing modalities such as Light Detection and Ranging (LiDAR), Red, Green, Blue, and Depth (RGB-D) cameras, and computational reconstruction technique such as photogrammetry enables the generation of three-dimensional hyperspectral point clouds, combining spectral richness with geometric fidelity. This multi-modal fusion enhances crop trait estimation, including biomass, leaf chlorophyll content, canopy height, leaf area, and stress indicators, while improving the robustness of phenotyping under occlusions, shadows, and varying illumination. Dimensionality reduction, feature selection, and machine learning approaches, including deep learning and explainable AI, are useful for handling high-dimensional hyperspectral data and extracting actionable agronomic insights. Moreover, the integration of thermal, radar, and Global Navigation Satellite System (GNSS) data further expands the capabilities of multi-modal sensing, enabling continuous, all-weather crop monitoring and accurate spatial referencing. Despite these advances, most studies to date focus on controlled environments, highlighting the need for field-based validation to ensure the reliability and scalability of HSI-depth fusion techniques. This review consolidates current knowledge on multi-modal hyperspectral and 3D crop reconstruction, highlighting methods, applications, and challenges, and outlines future directions for implementing high-throughput, real-time phenotyping and precision agriculture solutions.
Why it matches plant phenotyping methods植物形質推定のためのハイパースペクトル・深度センシング融合と3D再構成を中心に扱うレビューであり、フェノタイピング手法の方法論的整理が主題。
abstractThis review consolidates current knowledge on multi-modal hyperspectral and 3D crop reconstruction, highlighting methods, applications, and challenges
• ToF camera was evaluated for 3D phenotyping and canopy volume estimation. • Strong correlation was observed between ToF imagery and manual methods. • ToF technology could accurately estimate canopy volume in horticultural crops. • Study highlights the potential of ToF imagery for smart farm management. Noninvasive and accurate quantification of plant architectural traits remains a persistent challenge in horticultural crop breeding and high-throughput phenotyping (HTPP), largely due to labor constraints and limited availability of scalable 3D sensing technologies. Time-of-Flight (ToF) imaging offers a promising active sensing approach capable of generating high-resolution digital replicas of plant canopies for structural trait analysis. This study evaluated the feasibility of deploying 3D ToF imaging for digital phenotyping of three horticultural and ornamental crops, guava, brinjal, and jasmine with a focus on canopy volume estimation. High-resolution 3D point clouds were acquired for five plants per crop across three growth stages during the summer season of 2024. Structural parameters including plant height, width North–South (W NS ), width East–West (W EW ), and 3D aspect ratio were extracted and used to compute canopy volume via a voxel grid method. To validate accuracy, ToF-derived canopy volumes were compared against manually measured volumes estimated using the prolate spheroid volume (PSV) method. The ToF-based digital phenotyping framework demonstrated strong agreement with manual measurements across all crops. Guava exhibited the highest accuracy at the intermediate growth stage (R² = 1.0; RMSE = 0.0004), while brinjal (R² = 0.908; RMSE = 0.004) and jasmine (R² = 0.935; RMSE = 0.4351) showed robust performance at the full-grown stage. Those regression relationships were statistically significant ( p < 0.05), confirming the reliability of ToF-based canopy reconstruction for structural phenotyping. Overall, the findings demonstrate that ToF imaging provides a robust, noninvasive, and scalable framework for digital canopy phenotyping in horticultural crops, supporting precision breeding and structural trait monitoring applications in field environments.
Why it matches plant phenotyping methodsToFによる3D画像取得とキャノピー体積・構造形質の抽出を開発・評価し、手動測定との比較検証を行っており、植物表現型取得法が研究の中心である。
abstractToF camera was evaluated for 3D phenotyping and canopy volume estimation.
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.
Accurate and timely forage yield prediction in alfalfa-grass mixtures (AGM) is essential for supporting precision agriculture management decisions. This study aimed to develop and evaluate UAV-borne remote sensing models to predict total dry matter yield (DMY) and legume dry matter yield (LY) across multiple harvests and field sites. UAV-borne high-resolution true-colour images were used to derive canopy height models via structure-from-motion. At the same time, multispectral imagery enabled the calculation of reflectance-based vegetation indices. Biomass was destructively sampled, and DMY and LY were determined through drying and botanical fractioning. A total of 276 biomass samples were collected over four harvests, including samples from three AGM fields. To predict DMY and LY, two machine learning regression models (random forest and extreme gradient boosting) were trained and validated using leave-spatial-temporal-group-out cross-validation to ensure robustness across locations and time. Random forest models using fused spectral and height data achieved the best performance, with median prediction errors of 0.51 t ha⁻¹ for DMY (median R² = 0.49) and 0.40 t ha⁻¹ for LY (median R² = 0.65), demonstrating good generalizability under varying agronomic conditions. The study highlights the potential of combining UAV-borne height and spectral data for high-resolution yield mapping in complex forage systems. Predictive maps of DMY and LY provide spatial insights that can inform management and support sustainable nitrogen cycling in crop rotations.
Why it matches plant phenotyping methodsUAV画像から樹冠高・スペクトル情報を抽出し、機械学習で乾物収量とマメ科収量を推定する手法の開発・検証が研究の中心であるため。
abstractThis study aimed to develop and evaluate UAV-borne remote sensing models to predict total dry matter yield (DMY) and legume dry matter yield (LY) across multiple harvests and field sites.
Purpose: Coffee farming plays an essential role in the global economy, making accurate productivity prediction methods indispensable for strategic decision-making in the sector. This study aimed to develop models for early prediction of coffee production per plant based on morphological indices.Methods:Two models were proposed using the following attributes: plant height, canopy width, and the number of fruits on the productive internodes of plagiotropic branches. In Model 1, fruit counts were manually conducted at the 4th and 5th productive nodes of the branches, while in Model 2, the average fruit count from the 1st to the 5th productive nodes was obtained automatically through branch image analysis using Detectron2, an open-source object detection library. Both models were developed using data collected at two distinct periods before harvest—the first five months prior and the second three months prior. The research was conducted in three coffee plots in Viçosa, Minas Gerais, Brazil, where 60 plants were selected to evaluate the production prediction model. During harvest, the production of each plant was individually recorded, enabling validation of the predictions. Results: The results revealed a strong correlation between the models and the field-observed production data, especially for the model based on data collected three months before harvest. Model 1 demonstrated a better fit (R² = 0.889; RMSE = 0.923 L/plant; MAE = 0.635 L/plant), while Model 2 had a lower absolute error (R² = 0.747; RMSE = 0.374 L/plant; MAE = 0.460 L/plant). Additionally, productivity maps were generated for each plot, showing good agreement with field-observed productivity data.Conclusions: It was concluded that the proposed models are promising for application in coffee farming, contributing to early production prediction.
Why it matches plant phenotyping methodsコーヒー果実数を枝画像から自動抽出し、個体あたり生産量を早期予測する手法を開発・検証しており、表現型取得と予測ワークフローが研究の中心である。
abstractBoth models were developed using data collected at two distinct periods before harvest—the first five months prior and the second three months prior.
Field / plotLeafRootWhole plant / canopy / plot / fieldMorphology / geometry measurementLeaf traitsPlant / canopy heightRoot system architecture
To explore the distinctness, uniformity, and stability(DUS) testing technology for Angelica dahurica and develop its DUS testing guidelines, this study conducted systematic research on the morphological and growth characteristics of A. dahurica throughout its whole growth period. The research was based on 428 A. dahurica lines from 48 sampling sites across China, with unified standardized field planting, regular and fixed-point observation, classified statistics of phenotypic characteristics, and comprehensive data analysis. Ultimately, 49 test characteristics were identified, including 36 basic characteristics and 13 optional characteristics. Classified by attribute, they consisted of 4 qualitative characteristics, 3 pseudo-qualitative characteristics, and 42 quantitative characteristics. Classified by organ and growth stage, the characteristics covered 2 cotyledon characteristics, 19 leaf characteristics(including basal leaves and cauline leaves), 2 plant characteristics, 3 saccate leaf sheath characteristics, 5 fruit characteristics, 9 root characteristics, 5 stem characteristics, 3 flower characteristics, and 1 growth period characteristic. Through the evaluation of characteristic discriminability and stability, five grouping characteristics were screened out, namely "basal leaf: anthocyanin coloration on the back of the leaf sheath" "basal leaf: anthocyanin coloration at the attachment site of the petiolule" "flowering period" "plant: height" "main root: arrangement pattern of lenticel-like protuberances". These can serve as important bases for the preliminary screening and classification of A. dahurica varieties. Meanwhile, 20 standard varieties with typical phenotypes were identified to provide a unified reference for characteristic observation. In addition, the guidelines also specify the scope of application, requirements for propagation materials, growth stages, observation periods, observation methods, DUS judgment criteria, and other content. This study fills the gap in the field of DUS testing technology for A. dahurica, and provides a scientific basis and technical support for DUS testing, resource identification and description, variety breeding of A. dahurica varieties, as well as management and protection of new A. dahurica arieties.
Why it matches plant phenotyping methodsアンジェリカ・ダフリカ品種のDUS試験に向け、標準化された形質観察法、判定基準、識別性・安定性評価を開発しており、植物表現型取得手法が研究の中心である。
abstractTo explore the distinctness, uniformity, and stability(DUS) testing technology for Angelica dahurica and develop its DUS testing guidelines
Accurate estimation of tree diameter at breast height (DBH) is essential for forest monitoring, biomass modeling, and carbon accounting. While DBH is traditionally measured in the field, this approach is labor-intensive and costly, especially at large scales. In contrast, tree height can now be efficiently obtained from remote sensing platforms such as airborne LiDAR and photogrammetry, creating opportunities to estimate DBH indirectly. To address this, we developed a species-specific nonlinear framework to predict DBH from tree height and stand-level relative density (RD) in the mixed temperate forests of New Brunswick, Canada. Our analysis used 1807 trees from 653 permanent sample plots (1985–2014), representing six dominant species: Abies balsamea, Acer rubrum, Acer saccharum, Picea mariana, Picea rubens, and Picea glauca. Allometric (height-only) models explained part of DBH variation, with R² ranging from 0.15 to 0.35 (broadleaves) and 0.41–0.74 (conifers), but predictive accuracy was notably low for Acer rubrum and Acer saccharum. Incorporating RD as a competition index substantially improved model performance, with R² increasing to 0.85–0.89 (broadleaves) and 0.72–0.88 (conifers). Prediction errors (RMSE and MAE) consistently decreased, with broadleaves showing the greatest improvement compared to conifers, reflecting their stronger sensitivity to stand density. These findings demonstrate that combining tree height with RD provides reliable estimates of DBH across diverse species. The framework bridges ground-based inventory with remote sensing applications, offering a scalable approach for biomass estimation, stand density analysis, and sustainable forest management in temperate mixed-species forests.
Why it matches plant phenotyping methods樹木のDBHという明示的な植物形質を、樹高と林分密度から推定する種別非線形モデルを開発しており、形質推定手法が研究の中心である。
abstractwe developed a species-specific nonlinear framework to predict DBH from tree height and stand-level relative density (RD)
BACKGROUND: Phenotypic diversity arises from the process of development and is shaped by genomic variation in plants. However, the genetic basis of growth dynamics remains poorly understood in maize. RESULTS: Here, we analyze 679 maize inbred lines derived from a synthetic CUBIC population with approximately 2.8 million SNPs, leveraging high-throughput phenotyping to capture 1,002,240 RGB images across 18 growth stages. We quantify 67 image-based traits (i-traits), revealing distinct dynamic patterns throughout development. Genome-wide association studies identify 857 quantitative trait loci (QTLs) influencing growth variation, with 88.6% classified as period-specific dynamic QTLs exhibiting modest effects, and 11.4% as conservative QTLs with sustained effects. Notably, 1.5% of cryptic pleiotropic QTLs spanning different growth stages suggest genetic relocations during development. These QTLs enhance heritability estimates for mature traits by an average of 6.2%. We further characterize the novel function of key genes linked with these QTLs, including BRD1 with the pleiotropic effects on plant height and perimeter of convex hull and ZmGalOx1 with the broad-spectrum regulation of plant architecture. Developmental rewiring of epistatic networks shapes maize growth, underscoring the vitality of temporal genetic regulation. Trajectory modeling of i-traits across periods decodes the growth variation patterns, supporting the ontogenic hypothesis driven predictive breeding strategies. CONCLUSION: The findings elucidate the genetic architecture underlying growth dynamics from a spatial-temporal perspective, offering novel insights for maize improvement.
Why it matches plant phenotyping methods大規模RGB画像から67の画像形質を抽出し、発育段階ごとのトレイト動態を解析する高スループット植物表現型解析が研究の中核であるため、方法応用として収録する。
abstractleveraging high-throughput phenotyping to capture 1,002,240 RGB images across 18 growth stages
Reproduction assets foundThe paper's own phenotyping assets are publicly available: selected RGB plant images on Zenodo (record 18150504), and the image-analysis/i-trait extraction pipeline code on GitHub with a Zenodo mirror (record 18151471). The NCBI BioProject and MaizeGDB are prior-study/generic resources, not paper-specific.Code · publicThe image analysis and i-trait extraction pipeline and codes followed the previous procedure [ 18 ] without any modifications and has been publicly released at Github [ 49 ] and Zenodo [ 50 ] platform, all code in the repository are released under the MIT License.Open asset ↗GitHublines:195-202Code · publichas been publicly released at Github [ 49 ] and Zenodo [ 50 ] platform, all code in the repository are released under the MIT License.Open asset ↗Zenodolines:195-202Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Forecasting vulnerability of cultivated and wild species to climate changes is highly challenging. Evolutionary genomic models enable the prediction of mal-adaptation (genomic offset - GO) across environments and future climates under the assumption that populations are currently locally adapted but do not predict but the resulting phenotypic changes. To do so, we developed a new genomic prediction model (GP) integrating both genomic offset (GO) and within-population gene diversity (Hs) to capture genotype by environment interaction and inbreeding effects, respectively (GP-HO-Hs). As proof of concept, we applied this GP-GO-Hs model to a collection of 397 maize populations (landraces) evaluated across 25 environments in Europe using high-throughput DNA pool genotyping. GP-GO-Hs model accurately predicted yield, plant height and flowering time. It increased by 13% the predictive abilities of GP model for predicting yield of new landraces in new environments. GP-GO-Hs model also predicted that the more diverse the landrace, the more stable its agronomic performance across environments. GP-GO-Hs model generated phenotypic adaptive landscapes for each landrace in future climatic scenarios, enabling the identification of landraces with enhanced potential to adapt to future or emerging cultivation conditions. This GP-GO-Hs model could be easily applied to other wild and cultivated species. Teaser Identify promising landrace adapted to new and future environments by combining genomic selection and offset
Why it matches plant phenotyping methodsゲノム情報と環境適応指標を統合した新規予測モデルを開発し、収量・草丈・開花期という植物形質を予測することが研究の中心であるため、計算的フェノタイピング手法として収録する。
abstractwe developed a new genomic prediction model (GP) integrating both genomic offset (GO) and within-population gene diversity (Hs)
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
Sahelian ecosystems play a crucial role in climate regulation, particularly through their capacity to store atmospheric carbon. However, their carbon sequestration potential remains poorly quantified due to the high cost and logistical constraints of conventional field-based methods. This study investigates the use of low-cost unmanned aerial vehicles (UAVs) to assess dendrometric parameters and estimate the carbon storage capacity of woody vegetation within a 10-hectare Sahelian plot in Senegal. High-resolution aerial imagery was acquired using a DJI Phantom 4 Pro+ drone and processed with photogrammetric software. Tree height and crown diameter were extracted and validated using field measurements, showing a strong correlation for tree height (R² = 0.94) and a moderate correlation for crown diameter (R² = 0.74). Spectral index analysis enabled the identification of dominant woody species, including Balanites aegyptiaca, Sclerocarya birrea, Acacia raddiana, and Acacia senegal.The results indicate that Balanites aegyptiaca contributes the highest amount of carbon storage per hectare (1520.76 ± 185.5 kg ha⁻¹), mainly due to its high stem density (23.3 individuals ha⁻¹), whereas Sclerocarya birrea exhibits the highest average carbon stock per individual tree (206.8 ± 107.8 kg). These findings highlight the significant role of these species in climate change mitigation strategies. By demonstrating the effectiveness of low-cost UAVs for rapid and non-destructive assessment of Sahelian ecosystems, this study provides a promising approach for large-scale ecological monitoring and sustainable natural resource management. It also identifies methodological improvements required to enhance crown dimension estimates. Overall, this UAVbased approach represents a reliable alternative to destructive methods and is well suited to the ecological and operational challenges of Sahelian environments, with strong potential for application in similar regions.
Why it matches plant phenotyping methodsUAV画像から樹高・樹冠径という植物形態形質を抽出し、地上測定で検証する手法が研究の中心であるため、植物フェノタイピング手法として含める。
abstractTree height and crown diameter were extracted and validated using field measurements, showing a strong correlation for tree height (R² = 0.94) and a moderate correlation for crown diameter (R² = 0.74).
Point clouds and digital surface models (DSMs) derived from unmanned aircraft system (UAS) imagery are widely used for plant height estimation in plant phenotyping and precision agriculture. However, comprehensive evaluations across multiple crops, flight altitudes, and image overlaps are limited, restricting guidance for optimizing flight strategies. This study evaluated the effects of flight altitude, side and front overlap, and image processing parameters on point cloud generation and plant height estimation. UAS imagery was collected at four altitudes (30–120 m, corresponding to 0.5–2.0 cm ground sampling distance, GSD) with multiple side and front overlaps (67–94%) over a 2–ha field planted with corn, cotton, sorghum, and soybean on three dates across two growing seasons, producing 90 datasets. Orthomosaics, point clouds, and DSMs were generated using Pix4Dmapper, and plant height estimates were extracted from both DSMs and point clouds. Results showed that point clouds consistently outperformed DSMs across altitudes, overlaps, and crop types. Highest accuracy occurred at 60–90 m (1.0–1.5 cm GSD) with RMSE values of 0.06–0.10 m (R2 = 0.92–0.95) in 2019 and 0.07–0.08 m (R2 = 0.80–0.89) in 2022. Across multiple side and front overlap combinations at 60–120 m, reduced overlaps produced RMSE values comparable to full overlaps, indicating that optimized flight settings, particularly reduced side overlap with high front overlap, can shorten flight and processing time without compromising point cloud quality or height estimation accuracy. Pix4Dmapper processing parameters strongly affected 3D point cloud density (2–600 million points), processing time (1–16 h), and plant height accuracy (R2 = 0.67–0.95). These findings provide practical guidance for selecting UAS flight and processing parameters to achieve accurate, efficient 3D modeling and plant height estimation. By balancing flight altitude, image side and front overlap, and photogrammetric processing settings, users can improve operational efficiency while maintaining high-accuracy plant height measurements, supporting faster and more cost-effective phenotyping and precision agriculture applications.
Why it matches plant phenotyping methodsUAS画像からの点群・DSM生成と草丈推定について、飛行条件および処理パラメータの影響を体系的に評価・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 evaluated the effects of flight altitude, side and front overlap, and image processing parameters on point cloud generation and plant height estimation.
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.
Early blight, caused by Alternaria alternata, poses a critical challenge to tomato (Solanum lycopersicum L.) production, causing significant yield losses worldwide. Accurate quantification of plant disease severity is essential for the development of intelligent, site-specific crop protection systems. This study investigates the morphological responses of tomato plants following incidence of early blight disease across different stages of disease progression, with the objective of establishing biologically meaningful indicators for imaging-based disease severity classification. Key plant morphological parameters, including plant height, total leaf area, and diseased leaf area, were monitored over time and compared with healthy plants. Analysis of variance revealed a statistically significant difference in plant height between healthy and diseased plants after inoculation of disease, with average plant height after 90 days of growth were 94.86 and 81.81 cm respectively, indicating the impact of disease on overall plant growth. Temporal analysis of leaf area and diseased area exhibited distinct disease progression patterns, comprising an initial latent phase, a rapid symptom expansion phase, and a terminal phase characterized by tissue degradation. Disease severity was quantified using an area-based severity percentage derived from the ratio of diseased area to total leaf area, providing a normalized and scalable metric of infection intensity. The observed morphological and spatial disease characteristics closely correspond to features that can be extracted using machine vision techniques, such as changes in canopy geometry and lesion extent. The findings highlight the potential and the importance of severity based assessment of disease for variable-rate site-specific spraying systems, demonstrating clear advantages over conventional target-specific approaches in reducing chemical application, improving disease control efficiency, and supporting sustainable crop protection practices.
Why it matches plant phenotyping methods植物病害の重症度を、葉面積・病斑面積などの形態指標から画像ベースで定量化する方法の開発が中心であり、単なる病害実験のルーチン測定ではない。
abstractwith the objective of establishing biologically meaningful indicators for imaging-based disease severity classification
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.
Abstract The northern hardwood forests of Eastern Canada, particularly stands dominated by sugar maple (Acer saccharum Marsh.), are facing ongoing decline due to historical and contemporary environmental pressures. Traditional ground-based crown assessments for tree health are essential for management, but can be subjective, costly, and limited by their viewing perspective. While conventional remote sensing methods can effectively capture tree crown structure from above, and terrestrial approaches can capture the stems and crowns from beneath, occlusion by the dense crowns of mature sugar maples makes capturing reliable estimates challenging. We examine the potential of intra-canopy aerial drone-based photogrammetry, involving flights beneath, within, and above tree crowns, to generate detailed 3D point clouds of 29 sugar maple trees in Quebec, Canada. From these point clouds, we derived estimates of key structural attributes including diameter at breast height (DBH), tree height, and crown base height (CBH). We used ray-marching to quantify crown transparency across 162 viewing angles, forming a sphere around the crown, and compared predictions to ground-based visual estimates and health categories. Photogrammetric estimates had significant correlations with ground-measured attributes, including DBH (r = 0.82), tree height (r = 0.55), and CBH (r = 0.73 and 0.78 across two distinct definitions). Modeled crown transparency correlation was also significant when compared to ground-based visual assessments (ρ = 0.54), suggesting that intra-canopy drone-based photogrammetry can offer rapid and objective assessment of crown condition.
Why it matches plant phenotyping methods樹冠内ドローン画像から3D点群を生成し、樹高・DBH・樹冠基部高・透明度などの樹木形質を推定して地上測定と検証しており、フェノタイピング手法が中心である。
abstractWe examine the potential of intra-canopy aerial drone-based photogrammetry, involving flights beneath, within, and above tree crowns, to generate detailed 3D point clouds of 29 sugar maple trees in Quebec, Canada.
Achieving an optimal plant architecture is a key objective in cotton breeding for enhancing yield potential, and accurate quantification of the fruit branch angle (FBA) is essential for understanding genotype–phenotype relationships and advancing ideotype breeding. However, in-field FBA measurement remains technically challenging due to severe occlusion, variable illumination, and background interference. To overcome these limitations, we propose a streamlined 3D phenotyping framework that integrates 3D Gaussian Splatting (3DGS) with a novel structural segmentation model, the Linear Point Cloud Reverse Model (LPCRM). The framework decomposes reconstructed cotton point clouds into linear micro-elements using RANSAC, followed by geometric clustering via K-means to identify and separate the main stem and fruit branches. This process operates without topological priors or large annotated datasets. Model fidelity assessment shows that 80% of point pairs between the LPCRM and the original 3DGS reconstruction exhibit Euclidean distances ≤ 0.5 cm. Phenotypic validation using 268 fruit branches from 25 cultivars demonstrates high measurement accuracy, achieving an R² of 0.874 and an RMSE of 4.01° for FBA extraction. Plant height estimation also shows strong agreement with manual measurements (R² = 0.915, RMSE = 3.858). Overall, this study presents a lightweight and robust solution for extracting 3D structural traits of field-grown cotton. The proposed framework reduces data dependency, adapts well to complex field conditions, and offers an efficient approach for high-throughput phenotyping and cotton ideotype breeding.
Why it matches plant phenotyping methods綿花の果枝角度などの3D植物形質を抽出する画像ベース表現型解析フレームワークを開発し、複数品種・枝で精度検証しており、方法開発と技術検証が研究の中心である。
abstractwe propose a streamlined 3D phenotyping framework that integrates 3D Gaussian Splatting (3DGS) with a novel structural segmentation model, the Linear Point Cloud Reverse Model (LPCRM).
Background Rice plant architecture underpins yield and grain quality, yet two obstacles impede accurate field characterization in dense paddies. First, single-plant reconstruction is constrained by severe inter-plant occlusion, cluttered backgrounds, and limited viewpoints. These factors obscure culms, leaves, basal tillers, and the true physical scale of the plant. Active ranging devices are cumbersome in outdoor plots and can lose accuracy, whereas conventional passive photogrammetry performs poorly under such conditions. Second, delineating panicles within a 3D rice model is intrinsically difficult. Panicles are slender, highly branched, and visually similar to surrounding foliage, often interwoven and partially hidden. These factors result in fragmented boundaries and missing details. Direct point-cloud segmentation struggles with such discontinuous geometry and requires costly 3D annotation, whereas generic image segmentation models trained on natural scenes transfer poorly to paddy imagery. These challenges motivate a field-ready workflow that both reconstructs whole plants at high resolution in dense plantings and reliably segments panicles to enable trait extraction. Results A low-cost, in-field, multi-view pipeline for whole-plant three-dimensional reconstruction, termed One Stop 3D Target Reconstruction And segmentation (OSTRA), operates on color images with a reference-board setup. The pipeline builds detailed three-dimensional models of individual rice plants and automatically segments key organs (in this case, panicles), despite dense surrounding vegetation. When applied to 231 diverse rice landraces grown in a crowded field setting, the method produced high-fidelity plant models with clearly delineated panicle structures. From these reconstructions, three architectural traits were derived: plant height, leaf area, and panicle length. Genome-wide association analysis of the measured traits identified strong genotype-phenotype associations tagging known candidate genes. Natural variants at D2 and RFL/APO2 were associated with plant height variation, variants at FLW7 were linked to differences in leaf area, and allelic variation at AAI1 corresponded to panicle length variation. These loci are established regulators of plant growth and morphology, indicating that this three-dimensional phenotyping pipeline attains accuracy sufficient to rediscover meaningful genetic signals. Conclusions This study provides a practical tool for precise rice phenotyping even under dense field planting conditions, overcoming occlusion and structural complexity. By enabling non-destructive, field-based measurement of complete plant architecture and linking these phenotypes to specific genes, the pipeline bridges field phenomics and genomics. The integrated reconstruction and analysis framework advances the study of rice architecture and offers a general route to connect complex traits with their genetic determinants.
Why it matches plant phenotyping methods密植圃場でのイネ全体3D再構築、器官分割、形質抽出を中核とする画像ベース表現型解析手法の開発・実証であり、明確に収載対象。
abstractA low-cost, in-field, multi-view pipeline for whole-plant three-dimensional reconstruction, termed One Stop 3D Target Reconstruction And segmentation (OSTRA), operates on color images with a reference-board setup.
Reproduction assets foundThe paper explicitly states that the 3D rice plant models (231 landraces) are deposited on Zenodo and the OSTRA source code is publicly available on GitHub. Both are paper-specific, public, and actionable.Code · publicThe source code of OSTRA is available on GitHub at [http://github.com/ganlab/ostra] (http:/github.com/ganlab/ostra).Open asset ↗github · ganlab/ostralines:217-246Plant phenotyping relevance match · UnverifiedCrossref · checked 13 Sept 2026
Mountain treelines are among the most climate-sensitive ecosystems on Earth, yet their fine-scale structural and species level dynamics remain poorly resolved in the Himalayas. In particular, the absence of three-dimensional, crown level measurements have hindered the detection of structural thresholds and species turnover that often precede treeline shifts. To bridge this gap, we introduce UAV LiDAR—applied for the first time in the Hindu Kush Himalayas—to quantify canopy structure and tree species distributions across a steep treeline ecotone in the Manang Valley of central Nepal. High-density UAV-LiDAR data acquired over elevations of 3504–4119 m was used to quantify elevation-dependent changes in canopy stature and cover from a canopy height model derived from the 3D point cloud, while individual tree segmentation and species classification were performed directly on the 3D, height-normalized point cloud at the crown level. Individual trees were delineated using a watershed-based segmentation algorithm while tree species were classified using a random forest model trained on LiDAR-derived structural and intensity metrics, supported by field-validated reference data. Results reveal a sharply defined treeline characterized by an abrupt collapse in canopy height and cover within a narrow ~60–80 m vertical interval. Treeline “threshold” was quantified as a breakpoint elevation from a piecewise model of tree cover versus elevation, and the elevation span over which modeled cover and height distributions rapidly declined from forest values to near-zero. Segmented regression identified a distinct structural breakpoint near 3995 m elevation. Crown-level species predictions aggregated by elevation quantified an ordered turnover in dominance, with Pinus wallichiana most frequent at lower elevations, Abies spectabilis peaking mid-slope, and Betula utilis concentrated near the upper treeline. Species classification achieved high overall accuracy (>85%), although performance varied among taxa, with broadleaf Betula more difficult to discriminate than conifers. These findings underscore UAV LiDAR’s value for resolving sharp ecological thresholds, identifying elevation-driven simplification in forest structure, and bridging observation gaps in remote, rugged mountain ecosystems.
Why it matches plant phenotyping methodsUAV LiDARによる樹冠高・被覆、個体樹木セグメンテーション、種分類を中心的に実施し、植物の構造形質と状態を定量化・検証しているため。
abstractwe introduce UAV LiDAR—applied for the first time in the Hindu Kush Himalayas—to quantify canopy structure and tree species distributions
Forest surveying and inspection face significant challenges due to unstructured environments, variable terrain conditions, and the high costs of manual data collection. Although mobile robotics and artificial intelligence offer promising solutions, reliable autonomous navigation in forest, terrain-aware path planning, and tree parameter estimation remain open challenges. In this paper, we present the results of the AI4FOREST project, which addresses these issues through three main contributions. First, we develop an autonomous mobile robot, integrating SLAM-based navigation, 3D point cloud reconstruction, and a vision-based deep learning architecture to enable tree detection and diameter estimation. This system demonstrates the feasibility of generating a digital twin of forest while operating autonomously. Second, to overcome the limitations of classical navigation approaches in heterogeneous natural terrains, we introduce a machine learning-based surrogate model of wheel–soil interaction, trained on a large synthetic dataset derived from classical terramechanics. Compared to purely geometric planners, the proposed model enables realistic dynamics simulation and improves navigation robustness by accounting for terrain–vehicle interactions. Finally, we investigate the impact of point cloud density on the accuracy of forest parameter estimation, identifying the minimum sampling requirements needed to extract tree diameters and heights. This analysis provides support to balance sensor performance, robot speed, and operational costs. Overall, the AI4FOREST project advances the state of the art in autonomous forest monitoring by jointly addressing SLAM-based mapping, terrain-aware navigation, and tree parameter estimation.
Why it matches plant phenotyping methods自律ロボット、3D点群、深層学習を用いて樹木の直径・高さを推定する手法を開発し、点群密度による推定精度も評価しており、植物形質取得が中心である。
abstractwe develop an autonomous mobile robot, integrating SLAM-based navigation, 3D point cloud reconstruction, and a vision-based deep learning architecture to enable tree detection and diameter estimation.
Reproduction assets foundThe paper's point-cloud-density/tree-parameter analysis (Section 2.3) is based on two open-source MLS forest point cloud datasets (Forest 1 from southern Finland, and Forest 2 openly accessible via the 3DFin platform), which are public, paper-specific phenotype/trait data assets. However, the supplied blocks do not包含 aDataset · publicTwo different open-source datasets acquired using a Mobile Laser Scanning (MLS)
system (i.e., GeoSLAM Zeb-Horizon) and available online were considered in this study.Open asset ↗pdf-page:12 lines:1-60Plant phenotyping relevance match · UnverifiedEurope PMC · bioRxiv · Crossref · checked 6 Sept 2026
ABSTRACT Plant growth is a dynamic process affected by genes and growing environment, with all kinds of interactions between them. These complex relationships make the prediction of plant growth challenging. We propose a hybrid modelling framework that combines a logistic ordinary differential equation model with a Long Short-Term Memory (LSTM) neural network, resulting in a Physics Informed Neural Network (PINN). While PINNs have been widely applied to physical dynamical systems, their use in modelling the dynamics of plant growth systems is still largely unexplored. We illustrate the construction of a PINN on plant height data in wheat and compare its performance with alternative models for longitudinal plant data. All temporal prediction models only require time and temperature as input. Among a set of competing models, our PINN had the lowest average root mean squared error (RMSE) of prediction and the smallest standard deviation across multiple random initialisations. Therefore, we conclude that incorporating biological growth constraints into data-driven growth models can enhance prediction accuracy of longitudinal plant traits. Highlights Integrating plant growth equations into a temporal neural network improves plant height growth prediction over ordinary differential equations and machine learning models, especially when training data are limited.
Why it matches plant phenotyping methods植物高の時系列形質を予測するPINNを開発し、代替モデルと精度比較しているため、植物表現型の計算手法が中心である。
abstractWe propose a hybrid modelling framework that combines a logistic ordinary differential equation model with a Long Short-Term Memory (LSTM) neural network, resulting in a Physics Informed Neural Network (PINN).
Sustainable agriculture in arid regions faces critical challenges due to water scarcity, high temperatures, and inefficient traditional farming practices. This study presents an AI-enabled smart farming framework for optimizing date palm (Phoenix dactylifera) cultivation through the integration of Machine Learning (ML) and Internet of Things (IoT) technologies. A structured multimodal dataset comprising biometric features palm height, trunk diameter, and leaf number, environmental parameters soil moisture, temperature, and humidity, and categorical attributes variety and health status was analyzed to classify palm health and support data-driven irrigation management. Four ML algorithms Random Forest (RF), Gradient Boosting Machine (GBM), Artificial Neural Network (ANN), and Support Vector Machine (SVM) were developed and optimized using grid search with five-fold cross-validation. Among them, the Random Forest model achieved the highest classification accuracy of 95.3%, demonstrating strong robustness for heterogeneous agricultural data. Feature importance analysis highlighted soil moisture, humidity, trunk diameter, and leaf number as key contributors to palm health prediction. The proposed AI-IoT framework enables real-time monitoring, predictive diagnostics, and automated decision support for sustainable water use and crop management, aligning with Saudi Vision 2030 objectives for technology-driven and resource-efficient agriculture.
Why it matches plant phenotyping methodsヤシの生体特徴から健康状態を分類する機械学習手法を開発・比較し、分類性能を評価しているため、植物状態推定が中心的な方法的貢献である。
abstractThis study presents an AI-enabled smart farming framework for optimizing date palm (Phoenix dactylifera) cultivation through the integration of Machine Learning (ML) and Internet of Things (IoT) technologies.
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:320-407Plant 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
Lodging is a major contributor to decreased yield in tef, a staple cereal crop in Ethiopia. Semidwarf varieties have been developed with a goal to increase yield through reduced lodging, but studying lodging susceptibility currently requires a labor-intensive, imprecise, manual scoring method. Here we present workflows for analyzing tef stand height from UAS sensors across time to both predict lodging later in the season with early height and to measure the severity of lodging after a storm event. We compare 3D point clouds generated by photogrammetry from RGB images with those generated from LiDAR to estimate height, demonstrating that they produce similar results, despite differences in cost. Stand height and lodging can both be accurately measured with low-cost UAS, reducing the need for manual measurements and increasing precision and temporal resolution in plant breeding programs. Significance Statement Extreme weather or heavy grain can cause plant stems to bend, a process called lodging. Lodging significantly reduces crop yields globally, particularly in grain crops such as tef ( Eragrostis tef ). Semidwarf crops have previously been reported to be lodging-resistant, increasing crop yields. Here, we used uncrewed aerial systems (UAS) to measure plant growth, height, and lodging in gene edited semidwarf tef lines, and compared the results to ground-truth data. Using a UAS equipped with a red-green-blue (RGB) camera or LiDAR sensor, we measured plant height and lodging, and found that early-season height measurements could predict future lodging potential. The tools used were contributed to the open-source software PlantCV-Geospatial for community use. This work contributes to a broader understanding of genetic resistance to lodging, providing valuable insights for tef crop improvement and reduces the need for labor-intensive manual measurements.
Why it matches plant phenotyping methodsUASのRGB画像・LiDARから3D点群を生成し、植物の草高と倒伏を定量化・検証するワークフローが研究の中心であるため、植物フェノタイピング手法として含める。
abstractHere we present workflows for analyzing tef stand height from UAS sensors across time to both predict lodging later in the season with early height and to measure the severity of lodging after a storm event.
Reproduction assets foundThe paper states that code and data associated with the manuscript (UAS-based tef height/lodging phenotyping analyses) are publicly available in the authors' GitHub repository danforthcenter/teff-manuscript. The PlantCV-Geospatial package and D2S platform are general-purpose tools/platforms rather than paper-specific,.Code · publicInstitute Block Grant to K.M.M. and
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the USDA NIFA AFRI (grant number 2022-67021-36467 to N.F.), and by the Bellwether
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(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
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this version posted January 7, 2026.
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https://doi.org/10.64898/2026.01.0Open asset ↗danforthcenter/teff-manuscriptpdf-raw-page:13 lines:1-76Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
Abstract Plant breeders and weed scientists address weed management collaboratively by selecting for herbicide tolerance in breeding programs. Metribuzin, a Group 5 PSII‐inhibiting herbicide, is labeled for use in wheat ( Triticum aestivum L.). However, application to currently available lines results in frequent, variable, and unpredictable crop injury. Breeding for enhanced metribuzin tolerance would allow growers to utilize this herbicide effectively while minimizing the risk of crop injury. Incorporating an additional herbicide mode of action in winter wheat production would enhance rotational flexibility and weed resistance management. Selection for improved herbicide tolerance in crops has traditionally relied on visual estimation, yet assessments can be variable. The objective of this study was to improve the accuracy and efficiency of selecting for herbicide tolerance in a breeding program by utilizing a drone‐mounted multispectral sensor. Multispectral data were collected on paired rows of an diversity panel and advanced generation lines grown in paired plot yield trials. Vegetation indices calculated include normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), transformed chlorophyll absorption reflectance index, normalized water index, and modified triangular vegetation index. Visual assessments of injury, plant height, and grain yield were also recorded. Correlations between reflectance indices and grain yield were stronger than those between visual injury assessments and grain yield. The top 10 lines overlapped 45%–53% when selected by highest yield and highest NDVI or NDRE, respectively, in treated plots. The relationship between yield and index differences in treated and nontreated plots showed that the difference in indices (multiple R 2 = 0.0802–0.5434) explained more yield variation than visual assessments (multiple R 2 = 0.0003–0.1915). These results suggest that multispectral analysis at the plot level is a more accurate and efficient indicator of herbicide injury in winter wheat than traditional visual assessments.
Why it matches plant phenotyping methodsドローン搭載マルチスペクトルセンサーと植生指数を用いて、冬コムギの除草剤傷害・耐性を従来の目視評価より高精度かつ効率的に推定する方法を実証しており、表現型取得法が研究の中心である。
abstractThe objective of this study was to improve the accuracy and efficiency of selecting for herbicide tolerance in a breeding program by utilizing a drone‐mounted multispectral sensor.
Reproduction assets foundThe article's Data Availability Statement explicitly deposits the datasets generated and analyzed (phenotype/trait and vegetation index data from the metribuzin tolerance phenotyping experiments) in the Washington State University Research Exchange repository with a public DOI. No author analysis code repository is URLDataset · public20-
67037-30671, 2022-67013-36426, and 2022-68013-36439.
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 I B I L I T Y S TAT E M E N T
The datasets generated and analyzed for this study are avail-
able in the Washington State University Research Exchange
repository (https://doi.org/10.7273/000007507).O RC I D
Melinda Zubrod https://orcid.org/0000-0001-7024-8421
AndrewW. Herr https://orcid.org/0000-0001-5111-2342
ArronH. Carter https://orcid.org/0000-0002-8019-6554
R E F E R E N C E S
Ahmadi, Z., Mehrabadi, M., Fazli, M., Khalesro, S., Abedi, R., &
Mokhtassi-Bidgoli, A. (2025). Enhancing tolerance of wheat culti-
vars to meOpen asset ↗Washington State University Research Exchange · 10.7273/000007507pdf-raw-page:12 lines:1-81Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Accurate estimation of above-ground biomass (AGB) and plant height is essential for precision crop management. However, traditional methods like synthetic aperture radar (SAR) data and optical vegetation indices (VIs) often face signal saturation at medium to high AGB levels. To address this, we proposed two polarization texture indices, i.e., Ratio SAR Texture Index (RSTI) and Normalized Difference SAR Texture Index (NDSTI), derived from Sentinel-1 (S-1) data to estimate crop AGB and height. We further investigated their integration with S-1 polarizations and Sentinel-2 (S-2) VIs using four machine learning algorithms to enhance retrieval performance. Results revealed that both RSTI and NDSTI outperformed individual polarizations, polarization texture features, and most of VIs in estimating crop AGB and height. Furthermore, the combination of these indices with S-2 VIs significantly improved the retrieval accuracy. The optimal models achieved R2 values up to 0.75 and 0.80 for maize and soybean AGB, 0.89 and 0.94 for maize and soybean height, respectively. Validation with an independent dataset confirmed the robustness and transferability of the proposed models for estimating maize AGB and height. Overall, RSTI and NDSTI, along with their integration with optical VIs, provide an effective approach for improving crop AGB and height estimation for agricultural monitoring.
Why it matches plant phenotyping methodsSentinel-1/2リモートセンシングから作物バイオマスと草丈を推定する新規テクスチャ指数を開発し、独立データで検証しており、植物形質取得手法が中心である。
abstractwe proposed two polarization texture indices, i.e., Ratio SAR Texture Index (RSTI) and Normalized Difference SAR Texture Index (NDSTI), derived from Sentinel-1 (S-1) data to estimate crop AGB and height.
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
Accurate monitoring of crop phenology, biophysical attributes and agroclimatic variability is essential for optimizing agricultural practices, particularly in smallholder farming systems. In this study, we evaluated how field camera-derived Green Chromatic Coordinate (GCC) reflected variations in agroclimatic factors (rainfall and soil moisture) and biophysical attributes (leaf area index, crop height, chlorophyll content, and stomatal conductance) across agroecological zones (AEZs) in Kenya. Next, we utilized GCC time series to detect six key phenological stages of maize (Zea mays L.) - emergence, stem elongation, tasseling, kernel development, ripening, and senescence - using an amplitude-based relative threshold method. This approach was cross-validated against field observed phenology. Our analysis revealed positive correlations between GCC and plant height, chlorophyll content, and leaf area index (LAI). Daily-scale Pearson lag correlation between GCC and agroclimatic factors revealed that crops in drier ecosystems exhibited shorter response times to agroclimatic fluctuations (32 days to rainfall and 14 days to soil moisture), highlighting site-specific differences in vegetation dynamics captured by field cameras. Furthermore, results indicate that GCC effectively captured phenological stages with high accuracy (R² = 0.9, RMSE = 7.1–7.7 days), though variability was observed across sites and growth stages. Comparisons between within-site and inter-site validation suggest that localized calibration can improve accuracy. Nevertheless, the method remains robust across varying conditions, which is supported by comparison against established curve-fitting methods. Our findings highlight the potential of field cameras as a cost-effective tool for crop monitoring at a high spatial and temporal scale, with applications in crop phenology detection, biophysical monitoring, and validation of remote sensing products. Integrating this approach into regenerative agriculture frameworks could enhance decision-making and management interventions in smallholder farms.
Why it matches plant phenotyping methods圃場カメラ画像からGCCを抽出し、トウモロコシの生育ステージと生物物理形質を推定する手法を開発・検証しており、フェノタイピング手法が研究の中心である。
abstractwe utilized GCC time series to detect six key phenological stages of maize
ABSTRACT High-throughput phenotyping using unmanned aerial vehicles (UAVs) and spectral vegetation indices has been proposed to overcome the cost and logistical constraints of manual measurements in multi-environment breeding trials. However, the reliability of models trained on spectral data to predict structural traits across genotypes and environments remains unclear. This study aimed to develop an approach for predicting soybean plant height (PH) and first pod insertion height (FPIH) using UAV-based vegetation indices acquired at the flowering stage, as well as to compare extreme gradient boosting (XGBoost), multilayer perceptron (MLP), random forest (RF), and multiple linear regression (MLR) models under realistic cross-validation scenarios. Trials were conducted across multiple seasons using UAV multispectral imagery, with PH and FPIH manually measured. The models were evaluated under five phenotyping scenarios: baseline calibration using all data; prediction in a completely unmeasured future season; estimation of missing genotypes within a partially sampled season; calibration using a small fraction of data from a new season; and prediction under absence of field records for specific genotypes across environments. When all data were used for calibration, non-linear models showed a high apparent accuracy. However, prediction in unseen seasons failed for all models, reflecting strong genotype × environment interactions. Under reduced phenotyping within the same environment network, the models maintained a robust accuracy for PH, whereas FPIH predictions declined to moderate levels. UAV-based models are reliable for interpolation, but limited for extrapolation without local calibration, enabling reductions of up to 80 % in manual measurements for PH and 20-30 % for FPIH.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と機械学習によるダイズ形質推定を開発・比較検証し、異なる環境での予測性能と手測定削減を評価しているため、フェノタイピング手法が中心である。
abstractThis study aimed to develop an approach for predicting soybean plant height (PH) and first pod insertion height (FPIH) using UAV-based vegetation indices acquired at the flowering stage, as well as to compare extreme gradient boosting (XGBoost), multilayer perceptron (MLP), random forest (RF), and multiple linear regression (MLR) models under realistic cross-validation scenarios.
Efficient and accurate extraction of plant height (PH) plays an important role in analyzing its deeper phenotypic traits and improving breeding efficiency. Traditional methods make it difficult to measure PH at the individual plant level in the plot-level on a large scale, with high accuracy and low delay. To address this issue, we adopted a truss-type phenotyping platform to acquire LiDAR and RGB canopy data from 120 rapeseed genotypes from the two-leaf stage to the flowering stage, covering a total of 6 growth stages. (1) Object detection was performed to identify per plant of rapeseed images by the K-Means improved Faster R-CNN algorithm. (2) The image data before and after object detection and the point cloud data were fused to recognize per plant on the point cloud. Besides, the rapeseed plant point cloud and the ground point cloud were distinguished by color. (3) The Cloth Simulation Filter (CSF) algorithm is used to fit the ground points obscured by the canopy, which contributes to accurately extracting the PH of individual rapeseed. The field tests indicated that the mAP (IoU = 0.5) of the improved object detection method was 0.902, which achieved a high detection accuracy for rapeseed with different sizes in all periods. Specifically, in the PH accuracy verification of the No. 077 cultivar, R² was 0.997, RMSE was 1.156 cm, rRMSE was 5.48 %, and the maximum difference between automatic recognition and manual measurement in the late stage of growth was less than 6 cm. Especially, the proposed method can extract of the height of individual plant with the values of R² was 0.980, RMSE was 0.651 cm, rRMSE was 6.543 % in the seedling stage and the PH was lower than 20 cm. The differences of pH of 40 rapeseed genotypesunder cold stress were compared, which provide a reference for high-throughput PH extraction and exploring genotypic differences in plant breeding.
Why it matches plant phenotyping methodsLiDAR・RGBデータ融合と画像／点群処理により、個体ごとの草丈を高精度・ハイスループットに抽出する手法の開発と検証が中心であるため。
titleHigh-throughput extraction of individual plant height in rapeseed based on LiDAR-Camera data fusion
Accurate estimation of biomass in energy cane is essential for cultivar selection in breeding programs and biomass supply forecasting in bioenergy production. This study evaluated the integration of Uncrewed Aircraft System (UAS) based Light Detection and Ranging (lidar) and Red-Green-Blue Structure-from-Motion (RGB-SfM) photogrammetry to enhance biomass prediction for high-throughput phenotyping (HTP). Seven cultivars were monitored between December 2023 and July 2024 at an experimental field in Weslaco, Texas. Structural metrics such as percentile-based heights, canopy volume, and interaction variables were extracted and used to train four machine learning models: Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and a one-dimensional Convolutional Neural Network (1D-CNN). Ensemble tree algorithms consistently outperformed CNN, with XGBoost and LightGBM providing the most stable and interpretable predictions. For single sensor inputs, lidar models (R² = 0.70–0.78; RMSE = 1.36–1.60 kg/m²) generally outperformed RGB-SfM (R² = 0.69–0.73; RMSE = 1.52–1.63 kg/m²), though RGB-SfM performed competitively with XGBoost. Fused models combining lidar and RGB-SfM features achieved the highest accuracies (XGBoost: R² = 0.80, RMSE = 1.31 kg/m²; LightGBM: R² = 0.79, RMSE = 1.36 kg/m²), mitigating the underestimation of high-biomass plots in RGB-SfM and the slight overestimation of lidar at the upper tail. Cultivar specific analysis confirmed TH16–22 as the top performer, followed closely by Ho02–113 and TCP10–4928, demonstrating the capacity of UAS HTP to support breeding decisions. These findings confirm the biological relevance of percentile-based height metrics (particularly the 75th percentile), canopy volume, and their interactions for biomass accumulation and underscore the value of sensor fusion in reducing systematic bias. This study provides systematic demonstration of lidar and RGB-SfM fusion for biomass estimation in energy cane, establishing a scalable and non-destructive approach that advances high-throughput phenotyping and supports the development of sustainable bioenergy cropping systems.
Why it matches plant phenotyping methodsUAS搭載LiDAR・RGB-SfMによる植物構造特徴の取得と機械学習によるバイオマス推定を開発・比較評価しており、表現型取得・抽出手法が研究の中心である。
abstractThis study evaluated the integration of Uncrewed Aircraft System (UAS) based Light Detection and Ranging (lidar) and Red-Green-Blue Structure-from-Motion (RGB-SfM) photogrammetry to enhance biomass prediction for high-throughput phenotyping (HTP).
High-throughput field phenotyping (HTFP) has become an important approach for improving the efficiency and objectivity of phenotypic evaluation in modern plant breeding. Within the PHENO_MaizE project the practical application of UAV-based RGB phenotyping in temperate maize breeding under field conditions is investigated. The project integrates repeated drone imaging, extraction of image-derived traits, and predictive modeling in order to evaluate the potential of digital phenotyping for the selection of superior maize genotypes. Experimental material includes maize inbred lines and their corresponding testcrosses evaluated across multiple environments in Serbia. UAV surveys conducted during the growing season will enable monitoring of temporal crop development and extraction of traits such as plant height, canopy cover, vegetation indices, and growth dynamics. The research within the project will also assess the potential of phenomic prediction models for estimating important agronomic traits, including grain yield, flowering time, and grain moisture at harvest. Special emphasis is placed on developing a practical, cost-effective, and scalable HTFP framework adapted to medium-sized breeding programs. The expected outcomes may support wider implementation of digital phenotyping and data-driven selection strategies in maize breeding.
Why it matches plant phenotyping methodsUAV画像、画像由来形質抽出、予測モデルを統合した圃場フェノタイピング枠組みの開発・実装が中心であり、単なる育種試験のルーチン測定ではない。
abstractThe project integrates repeated drone imaging, extraction of image-derived traits, and predictive modeling
Crop height is a key biophysical parameter closely linked to plant growth, biomass, and yield. With the advancement of remote sensing technologies, unmanned aerial vehicles (UAV) have emerged as a promising tool for estimating crop height using structure from motion (SfM) point clouds. However, accurately mapping the digital terrain model (DTM) beneath dense canopies remains a major challenge, as existing methods struggle to capture the soil surface effectively under full vegetation cover. This study aimed to develop and evaluate efficient workflows for generating DTMs from UAV-derived point clouds for in-season crop height estimation, with a focus on eliminating the need for pre-season UAV flights. The experiment was conducted during the 2023 maize growing season in Temple, TX, and compared three workflows: (i) UAV-B CH , which used UAV-derived bare soil surfaces as the DTM; (ii) Sent CH , which used Sentinel-1A-derived surfaces from the dormant season; and (iii) UAV-P CH , a novel approach that applied a low-pass filter to select the lowest 1 % of elevation points within a moving window, followed by fitting a 2.5D regression surface to generate the DTM. Results showed that UAV-P CH consistently outperformed the other methods across two fields with varying elevation patterns, achieving higher accuracy (R 2 ≈ 0.89) compared to UAV-B CH (R 2 ≈ 0.66) and Sent CH (R 2 ≈ 0.69). UAV-P DTM effectively minimized temporal inconsistencies and vertical misalignments commonly associated with multi-date UAV acquisitions. This approach offers a scalable and efficient solution for crop height estimation using a single UAV flight. Future research should explore its applicability across diverse crop types and canopy structures to enhance its utility in precision agriculture.
Why it matches plant phenotyping methodsUAV-SfM点群から作物高を推定するDTM生成ワークフローを開発・比較評価しており、植物形質の取得方法が研究の中心である。
abstractThis study aimed to develop and evaluate efficient workflows for generating DTMs from UAV-derived point clouds for in-season crop height estimation
Point clouds generated by Structure from Motion (SfM) are often affected by significant noise caused by plant movement, as well as missing ground points caused by canopy occlusion. This reduces the quality of canopy and terrain extraction and makes it challenging to accurately estimate the height of crops from Unmanned Aerial Vehicle (UAV) images. To overcome these limitations, this study introduces a Density Consistency Filtering (DCF) algorithm, which adaptively models local density continuity to distinguish between valid points and noisy points. It effectively preserves local canopy structures while removing clustered noise. Furthermore, a color-spatial interpolation scheme based on ExG-RANSAC is developed to reconstruct missing ground points under dense canopies. Evaluated on six datasets from May to June 2019 covering key stages of winter wheat growth, the method achieved an RMSE of 7.7 cm, MAE of 6.2 cm, and R² of 0.91. After the early stem elongation stage, the method achieved an RMSE of 4.9 cm. The approach significantly improves estimation accuracy of the late growth stages, demonstrating strong potential for precision agriculture applications.
Why it matches plant phenotyping methodsUAV点群から冬小麦の草丈を推定するためのノイズ除去・地面点再構成手法を開発し、複数データセットで精度検証しており、植物形質取得手法が研究の中心である。
titleA Method for Estimating Winter Wheat Height Using UAV Point Cloud Data Enhanced by Density Consistency Filtering
Accurate estimation of aboveground biomass (AGB) helps to monitor maize growth and yield prediction, and unmanned aerial vehicles (UAVs) have become one of the most significant technological tools in precision agriculture. However, previous studies have mainly focused on utilizing spectral indices, texture metrics and structural features derived from UAV multispectral imagery. These methods often involve significant uncertainties and ignore overall maize morphological characteristics. In this study, an innovative partial pixel integration (PPI) parameter is introduced to characterize both horizontal and vertical structural features of maize (Zea mays L.) at the plot scale. Field experiments were conducted in Dafeng District, Yancheng City, Jiangsu Province, China. Multispectral UAV imagery was captured at five flight altitudes (10 m, 20 m, 30 m, 50 m, and 80 m). Five structural features—fractional vegetation cover (FVC), plant height (PH), FVC × PH, pixel integration (PI), and PPI—were extracted to develop Fresh and Dry AGB estimation models based on linear, exponential, and power functions. The models were verified with the method of five-fold cross-validation to evaluate the predictive performance of different parameters. The results revealed that: (1) The models with PPI parameter outperformed that with all other metrics (FVC, PH, FVC×PH, PI), achieving the highest R² values of 0.968 for Fresh AGB (at 20 m flight altitude) and 0.948 for Dry AGB (at 10 m flight altitude); (2) Fresh AGB estimation models were generally more accurate than Dry AGB estimation models; (3) Contrary to expectations, increasing UAV flight altitude did not necessarily reduce AGB prediction accuracy. These findings demonstrate that the PPI parameter delivers high accuracy and robustness, presenting a novel and reliable approach for in-field maize AGB estimation.
Why it matches plant phenotyping methodsUAV画像からトウモロコシの形態特徴を抽出し、PPIという新規パラメータで地上部バイオマスを推定・検証することが研究の中心であり、植物表現型取得手法として適格。
abstractan innovative partial pixel integration (PPI) parameter is introduced to characterize both horizontal and vertical structural features of maize
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methodsUAV-SfMによる植物高の評価が中心で、植物形質の取得法を検証し、収量推定・個体選抜へ応用している。
titleEvaluation of Unmanned Aerial Vehicle-Based Structure-from-Motion (UAV-SfM)-Derived Plant Height for Yield Estimation and Individual Selection in Cool-Season Grass Breeding
Aboveground biomass (AGB) of forests is a crucial metric for assessing ecosystem carbon storage and cycling. The geographical complexity of offshore islands, especially their perennial cloud cover, poses a challenge to traditional AGB remote sensing, so unmanned aerial vehicle (UAV) based remote sensing technology is particularly important. However, UAV approaches face limitations from ground interference, structural parameter errors, and allometric equation discrepancies. This study develops a framework integrating UAV photogrammetry and the Mask R-CNN to identify tree species, quantify forest structure, and estimate AGB in a representative offshore island to infer the impact of tree species differences, dominated by allometric equations and structural parameter errors, dominated by canopy occlusion on the estimated AGB of island forests. Compared to the AGB results calculated by species identification of individual trees, multispectral sensors effectively identified four dominant tree species and land cover, as general allometric equations resulted in significant errors ranging from − 68 % to + 36 %. The optimized canopy height model (CHM) approach revealed a 5.6 % underestimation of tree height and 10.0 % error in diameter at breast height (DBH) due to canopy occlusion. This study provides new insights into AGB estimation and forest species identification methodologies, which are important for studying carbon management activities in similar ecosystem types.
Why it matches plant phenotyping methodsUAVマルチスペクトル写真測量とMask R-CNNを用いて樹種、樹木構造、樹高・DBH・地上部バイオマスを推定する手法を開発し、誤差も評価しており、植物表現型取得が中心である。
abstractThis study develops a framework integrating UAV photogrammetry and the Mask R-CNN to identify tree species, quantify forest structure, and estimate AGB
The characterization of wheat genetic resources constitutes a fundamental prerequisite for their effective use in breeding programs aimed at preventing future food shortages. Continued technological developments in plant phenotyping for remote and proximal sensing have enabled multidimensional data acquisition and analysis, making the screening of large numbers of genotypes more accessible and costeffective. Within this framework, 36 bread wheat landraces collected from different localities across Serbia were grown under rainfed conditions during the 2024/25 growing season at Rimski Šančevi, near Novi Sad (45.20° N, 19.51° E) and analyzed using several proximal non-destructive phenotyping devices. In the field trials, genotypes were evaluated at two growth stages for seven traits associated with plant productivity: green cover, leaf area index, maximum plant height, normalized difference vegetation index (Literal sensor, Hiphen), chlorophyll content, and nitrogen balance index (DUALEX optical leaf clip meter, Metos). After harvest, the landraces were assessed for thousand grain weight and grain size fractions (length, width, area) using the MARViN system (MARViTECH), and basic technological parameters (protein, moisture, carbohydrates, oil contents) using the GrainSense Analyzer (Oulu). Principal Component Analysis revealed a clear separation among the analyzed genotypes, reflecting their substantial genetic diversity with respect to the evaluated traits, and highlighting their potential as a valuable source of novel alleles for enhancing breeding value and developing high-yielding varieties with improved technological quality.
Why it matches plant phenotyping methods複数の近位非破壊センシング機器を用いて、遺伝資源の生育・形態・生理・収量関連形質を体系的に取得することが研究の中心であり、実質的なフェノタイピング手法の適用に該当する。
abstractanalyzed using several proximal non-destructive phenotyping devices
In a time of shifting disturbance regimes and anthropogenic pressures, effective management of forest and woodland ecosystems depends on continuous, landscape-level monitoring of canopy structure. While remote sensing can help meet this need, its broad operational implementation is often impeded by a myriad of data constraints, necessitating careful tailoring of monitoring programs to specific management objectives across heterogeneous environments. For example, certain regions, such as African savannas, are heavily data-limited and present unique challenges for detecting subtle structural variations within sparse, low-stature vegetation. Conversely, data-rich regions introduce complexities in reconciling disparate reference datasets created with varied methodologies. Furthermore, widely available 3D remote sensing resources like digital aerial photogrammetry (DAP) remain vastly underutilized, despite directly measuring fundamental attributes like canopy height. A final, critical gap is that remote sensing maps are rarely evaluated for one of their primary intended applications, which is aiding in the estimation of population means or totals for vegetation attributes over distinct management areas. To address these gaps, this dissertation investigates the strategic integration of multi-sensor remote sensing and diverse reference datasets across forest and savanna ecosystems. Crucially, each investigation moves beyond map development to evaluate how these products improve population estimates of vegetation structure using post-stratified and small area estimators. This hybrid approach empowers land managers and forest inventory programs by pairing time-series maps that visualize continuous landscape dynamics with rigorous statistical estimates that deliver defensible, population-level metrics for adaptive decision-making. The first chapter evaluates combining remote sensing predictors from multiple sources for extending canopy measurements from spaceborne waveform lidar (GEDI) across data-limited African savannas. By combining optical time series, synthetic aperture radar, and environmental covariates, parsimonious models successfully quantified annual structural changes from herbivory and woody encroachment, though the estimated magnitude of change was muted. The second chapter examines how the selection of canopy cover reference data for building Landsat-based canopy cover maps ultimately influences the precision of population estimates of forest area and aboveground biomass in the Rocky Mountains. Comparing diverse field, aerial, and lidar-based canopy cover sources revealed that the efficiency of post-stratified estimators for aboveground biomass is driven primarily by the plot-level correlation between the reference data and the target variable, rather than marginal improvements in mapping accuracy. Finally, the third chapter demonstrates the operational viability of multi-temporal DAP for statewide forest structure estimation and post-fire canopy change assessment. While DAP struggled to capture residual standing dead stems and sparse woodlands at high resolutions, spatial aggregated canopy height models closely matched airborne lidar benchmarks and achieved nearly identical precision in statewide aboveground biomass estimation. These investigations together advance broad-scale operational use of remote sensing in diverse and complex environments to support forest inventories and effective land management.
Why it matches plant phenotyping methods森林・サバンナの個体群／プロットレベルで樹冠高・樹冠被覆・構造をリモートセンシングにより推定し、異なるセンサーや参照データとの比較、精度評価、航空レーザーとのベンチマークを行っている。植物構造形質の取得・検証が中心であり、単なる生態学的ルーチン測定ではない。
abstractthis dissertation investigates the strategic integration of multi-sensor remote sensing and diverse reference datasets across forest and savanna ecosystems
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.
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.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
High-throughput phenotyping using unmanned aerial vehicle (UAV)-based imagery offers substantial potential for improving sugarcane breeding efficiency. This study utilized UAVs-equipped multispectral sensors to capture high-resolution imagery of 652 sugarcane varieties under high-density planting condition, enabling the development of predictive models for key phenotypic traits including plant height, leaf length, leaf width, and relative chlorophyll content (SPAD value). A comprehensive feature extraction process yielded 100 vegetation indices, 7 texture indices, and canopy height parameters derived from the UAV imagery. To develop robust predictive models, we implemented three feature processing strategies—correlation-based filtering (COR), stepwise regression selection (SWR), and principal component analysis (PCA)—in conjunction with five machine learning algorithms: Lasso Regression (LASSO), Ridge Regression (Ridge), Support Vector Machine Regression (SVM), Random Forest (RF), and Gradient Boosting Regression Trees (GBR). Two ensemble methods, Bayesian Model Averaging (BMA) and Stacked Generalization, were also employed. Results demonstrated that LASSO performed best among traditional machine learning models, whereas the Stacking ensemble method, which integrated predictions from all individual algorithms, achieved the highest prediction accuracy (the coefficient of determination ( R 2 ) = 0.77; root mean squared error ( RMSE ) = 12.99 cm for plant height). Additionally, K-means clustering partitioned the sugarcane varieties into two distinct clusters (A and B; p ≤ 0.001). Notably, cluster-specific models trained on PCA-processed features demonstrated exceptional predictive accuracy during validation, achieving R 2 values of 0.94, 0.91, 0.87, and 0.90 for plant height, leaf length, leaf width, and SPAD value, respectively. This research presents an integrated framework combining optimized feature processing, population clustering, and ensemble learning to enhance trait prediction in large-scale UAV-based phenotyping for sugarcane breeding.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像からサトウキビの複数形質を推定する予測モデルと統合的な表現型解析フレームワークを開発・検証しており、表現型取得・抽出手法が研究の中心である。
abstractHigh-throughput phenotyping using unmanned aerial vehicle (UAV)-based imagery offers substantial potential for improving sugarcane breeding efficiency.
The non-destructive estimation of grain yield could increase the efficiency of soybean breeding through early genotype testing, allowing for more precise selection of superior varieties. High-throughput phenotyping (HTPP) data can be combined with machine learning (ML) to develop accurate prediction models. In this study, an unmanned aerial vehicle (UAV) equipped with a multispectral camera was utilized to collect data on plant density (PD), plant height (PH), canopy cover (CC), biomass (BM), and various vegetation indices (VIs) from different stages of soybean development. These traits were used within random forest (RF) and partial least squares regression (PLSR) algorithms to develop models for soybean yield estimation. The initial RF model produced more accurate results, as it had a smaller error between actual and predicted yield compared with the PLSR model. To increase the efficiency of the RF model and optimize the data collection process, the number of predictors was gradually decreased by eliminating highly correlated VIs and selecting the most important variables. The final prediction was based only on several VIs calculated from a few mid-soybean stages. Although the reduction in the number of predictors increased the yield estimation error to some extent, the R2 in the final model remained high (R2 = 0.79). Therefore, the proposed ML model based on specific HTPP variables represents an optimal balance between efficiency and prediction accuracy for in-season soybean yield estimation.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から植物形質を取得し、機械学習でダイズ収量を推定するワークフローが研究の中心であり、形質抽出・予測モデルの開発と効率化を扱っている。
abstractHigh-throughput phenotyping (HTPP) data can be combined with machine learning (ML) to develop accurate prediction models.
Alfalfa (Medicago sativa) is a globally important forage crop; however, improvements in its biomass yield have stagnated due to its complex genetic architecture and the costly, labor-intensive phenotyping. This study evaluated the potential of the normalized difference vegetation index (NDVI) to predict biomass yield and enhance selection efficiency in alfalfa breeding programs. Specifically, nineteen alfalfa experimental populations (AEXP 1–19) and one control cultivar (OS 66) were evaluated over two growing seasons in Croatia. NDVI was measured at four development stages using a GreenSeeker sensor and compared with forage yield, dry matter yield, and plant height. NDVI values varied significantly among genotypes, years, and growth stages, ranging from 0.23 to 0.87, and increased consistently from early to late vegetative phases. Strong positive correlations were observed between NDVI and forage yield (r = 0.543–0.843) and plant height (r = 0.537–0.738) at early vegetative, late vegetative, and early bud stages. Conversely, NDVI at the mid-vegetative stage correlated negatively with yield and height (r = –0.622 to –0.794). High-performing populations (AEXP 2, AEXP 15, AEXP 18) also exhibited the highest NDVI values. NDVI is a reliable, non-destructive indicator for early selection of high-yielding alfalfa genotypes, although multi-location validation is advised to confirm its broader applicability.
Why it matches plant phenotyping methodsNDVIセンサーによる非破壊的な生育・収量形質推定を育種選抜へ適用し、収量や草丈との相関で妥当性を評価しており、フェノタイピング手法が中心である。
abstractThis study evaluated the potential of the normalized difference vegetation index (NDVI) to predict biomass yield and enhance selection efficiency in alfalfa breeding programs.
Introduction Diameter at breast height (DBH) is a key parameter for assessing tree growth, carbon storage, and ecological functions. Traditional ground surveys are inefficient, labor-intensive, and terrain-limited, making them unsuitable for large-scale monitoring. Airborne LiDAR, as an advanced remote sensing tool, provides an efficient and non-destructive method for DBH estimation. However, most existing LiDAR-based models overlook the influence of genotype differences, limiting prediction accuracy. Methods In this study, we used data from 2,899 Catalpa bungei trees of different genotypes to develop a nonlinear mixed-effects (NLME) model that incorporates genotype as a random effect. This approach improved model generalizability by using LiDAR-derived tree height (LH) and LiDAR-derived crown diameter (LCD) as core predictors. Multiple sampling strategies were also evaluated to assess their impact on model performance. Results The results showed that, considering genotype effects, the proposed NLME model outperformed both traditional regression models and dummy-variable models (R 2 = 0.8624, RMSE = 1.1330, TRE = 3.9555), demonstrating the important role of genotype differences in improving model accuracy. Random sampling further improved prediction accuracy while effectively reducing measurement costs. Discussion This research introduces a new framework for integrating genotype variability into DBH prediction models and offers valuable insights for future LiDAR-based studies in genetically heterogeneous plantations. The findings provide technical support for forest management and ecosystem monitoring, as well as a methodological foundation for predicting tree growth under varying site and genetic conditions.
Why it matches plant phenotyping methodsUAV LiDARと非線形混合効果モデルを用いて個体樹木のDBHを推定する手法を開発・比較検証しており、植物形質取得が研究の中心である。
abstractAirborne LiDAR, as an advanced remote sensing tool, provides an efficient and non-destructive method for DBH estimation.
Introduction Ground-based Global Navigation Satellite System Reflectometry (GNSS-R) has recently emerged as a low-cost, continuous, and high-resolution technique for monitoring crop growth. However, conventional GNSS-R approaches that rely on signal-to-noise ratio (SNR) observables are limited by data availability, particularly from legacy receivers, and the effectiveness of observable combination methods in this context has not yet been established. Methods This study presents the first successful attempt to retrieve wheat height using ground-based GNSS-R with code pseudorange and dual-frequency carrier phase observables. Six observable combination schemes from GPS and BDS were evaluated through a field experiment at the Fengqiu Agro-ecology Experimental Station in China. A GDD-parameterized Logistic growth model was employed as a continuous reference. A multi-system, multi-satellite fusion strategy was developed, incorporating principal frequency power weighting within each system and residual reciprocal weighting across systems. Results The observable combination method effectively captured wheat growth dynamics. The best-performing combinations-GPS C5I+L5I+L2P and BDS C2I+L2I+L6I-achieved correlation coefficients (R) of 0.935 and 0.957, and RMSE values of 0.081 m and 0.086 m, respectively. Dual-system fusion further enhanced retrieval accuracy, reducing RMSE by 22.6% compared with the best single-system combination and by 34.6% relative to an SNR-based method. Discussion These findings demonstrate the feasibility and superiority of pseudorange and dual-frequency carrier phase combinations for SNR-independent GNSS-R crop monitoring. The proposed strategy offers a robust, scalable, and accessible tool for precision agriculture and continuous crop growth tracking, particularly in contexts where SNR data are unavailable or unreliable.
Why it matches plant phenotyping methodsGNSS-Rを用いてコムギ高さと成長動態を推定する観測量の組合せ・融合戦略を開発し、既存のSNR法と精度比較しており、植物形質取得手法が中心である。
abstractThis study presents the first successful attempt to retrieve wheat height using ground-based GNSS-R with code pseudorange and dual-frequency carrier phase observables.
Weeds pose a ubiquitous challenge to researchers as a source of unintended variation on crop yield and other metrics in designed experiments, creating a need for practical and spatially comprehensive techniques for weed detection. To that end, imagery acquired using unmanned aerial systems (UASs) and classified using pixel-based, object-based, or neural network-based approaches provides researchers a promising avenue. However, in scenarios where spectral differences cannot be used to distinguish between crop and weed foliage, where physical overlap between crop and weed foliage obstructs object-based detection, or where large datasets are not available to train neural networks, alternative methods may be required. For instances where there is a consistent difference in height between crop and weed plants, a mask can be applied to a canopy height model (CHM) such that pixels are determined to be weed or non-weed based on height alone. The CHM Mask (CHMM) approach, which produces a measure of weed area coverage using UAS-acquired, red–green–blue imagery, was used to detect Palmer amaranth in Sweetpotato with an overall accuracy of 86% as well as explain significant variation in sweetpotato yield (p
Why it matches plant phenotyping methodsUAS画像から作物キャノピー高モデルを構築し、雑草面積被覆率という植物状態を推定するCHMM手法が中心で、精度評価も行っているため収載する。
abstractThe CHM Mask (CHMM) approach, which produces a measure of weed area coverage using UAS-acquired, red–green–blue imagery, was used to detect Palmer amaranth in Sweetpotato with an overall accuracy of 86%
Above-ground vertical structure is a critical variable for ecosystem monitoring, carbon accounting, and land management. However, the high cost and limited coverage of airborne lidar hinder its widespread application. To address this, we developed NAIP-CHM, a 0.6-meter resolution canopy height and structure model (CHM) covering the contiguous United States, derived from National Agriculture Imagery Program (NAIP) aerial imagery. Unlike forestry-specific models that exclude human-made features, NAIP-CHM characterizes the full vertical structure of the landscape including vegetation, buildings, and infrastructure. We utilized a U-Net convolutional neural network with attention mechanisms and environmental conditioning, training and validating the model with a peer-reviewed, publicly available dataset of 22.8 million co-registered NAIP imagery and lidar-derived CHM pairs, with stratified sampling to ensure robustness in open-canopy ecosystems. The model achieved a pixel-wise root mean square error (RMSE) of 2.28 meters and an r2 of 0.87. Forested sites alone produced an r2 of 0.82 and RMSE of 3.82 meters. We provide the dataset, source code, and cloud-based tools to enable broad application without requiring specialized computational resources.
Why it matches plant phenotyping methods植生を含む景観の樹冠高・構造を航空画像から推定するモデルを開発し、公開データセットで検証している。植物キャノピーの明示的な構造形質推定が中心だが、建造物等も含むため植物以外の構造も対象とする点には留意が必要。
abstractwe developed NAIP-CHM, a 0.6-meter resolution canopy height and structure model (CHM) covering the contiguous United States, derived from National Agriculture Imagery Program (NAIP) aerial imagery.
Reproduction assets foundThe paper's NAIP-CHM canopy height model, its CONUS 0.6 m dataset, trained weights, and full training/inference code are all publicly released with explicit availability statements and author-hosted URLs (Rangeland Analysis Platform server, GitHub, Zenodo, Colab notebook, Earth Engine app).Dataset · publicFor bulk download, COGs and associated index files are available via HTTP from the Rangeland Analysis Platform server ( http://rangeland.ntsg.umt.edu/data/naip-chm/ ).Open asset ↗Rangeland Analysis Platform serverlines:76-83Model / weights · publicThe source code, trained model weights, validation data, and auxiliary datasets required to reproduce the results are permanently archived in a Zenodo repository 31 .Open asset ↗Zenodolines:89-134Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
Early-season prediction of yield and nitrogen‐related performance is essential for enabling timely agronomic interventions yet remains challenging in crops with limited prior digital phenotyping research, such as Tritordeum . Root traits, although fundamental to early nutrient uptake and crop establishment, remain largely absent in ML prediction frameworks. This study evaluated how root morphological traits, in combination with UAV-derived multispectral indices and proximal agronomic measurements, contribute to predicting yield and nitrogen efficiency indices under Mediterranean field conditions. Measurements were collected during the first three phenological stages, and a diverse set of machine-learning algorithms, including Random Forest, ExtraTrees, AdaBoost, Support Vector Regression, regularised linear models and the foundation model TabPFN, were benchmarked using a rigorous 10×5 cross-validation scheme. Yield emerged as the most reliably predictable trait, reaching an R² of 0.90 in the best multivariate configuration, while nitrogen-efficiency indices (NUE, NHI, NUtE) showed substantially higher variability and limited early-season predictability. Root diameter at the tillering stage consistently ranked among the most informative predictors, and its combination with SPAD at stem elongation, MCARI at tillering, or plant height at tillering produced the strongest yield models. These findings highlight the importance of integrating early-season below-ground information with spectral and agronomic traits to enhance prediction accuracy. Overall, the study demonstrates that accurate early-season yield forecasting in Tritordeum can be achieved using a minimal set of measurements, supporting cost-efficient monitoring and enabling actionable in-season adjustments to nitrogen management. The results also show the potential of foundation models such as TabPFN for limited agronomic datasets, providing a basis for developing scalable, data-driven decision-support tools for sustainable cereal production.
Why it matches plant phenotyping methodsUAVマルチスペクトル、根形態・農学測定を統合した機械学習による収量・窒素関連形質の推定を中心に、複数モデルを厳密にベンチマークしており、形質推定ワークフローが実質的な方法貢献である。
abstracta diverse set of machine-learning algorithms, including Random Forest, ExtraTrees, AdaBoost, Support Vector Regression, regularised linear models and the foundation model TabPFN, were benchmarked using a rigorous 10×5 cross-validation scheme
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 photogrammetric point cloud provides information that allows to estimate dendrometric and dasometric variables at the individual tree level with precision. The objective was to evaluate the potential of the geospatial point cloud generated by photogrammetry of aerial photographs captured by a low-cost drone in the estimation of dendrometric and dasometric variables in conifer species. With data on total height (At: m), basal area (AB: m2) and volume (Vol: m3) of 80 conifer trees measured in the field, linear (M1), exponential (M2), M1 with mixed effects (M3), M2 with mixed effects (M4), artificial neural networks (ANN-M5) and random forest (RF-M6) regression models were fitted to estimate At, AB and Vol based on height metrics (z), of the measured conifers, from the photogrammetric point cloud. The efficiency of the estimates was determined using the highest adjusted coefficient of determination (R2adj), the lowest root mean square error (RMSE), the Akaike Information Criterion (AIC), and Bias. The At was best estimated using the photogrammetric point cloud metrics, with R2adj ranging from 0.87 to 0.98, and RMSE of 1.64 and 0.61 m; M2 being the best. Regarding the estimation of AB and Vol, the RF-M6 model was the best, achieving an R2 of 0.77 and 0.77, and RMSE of 0.046 and 0.269, respectively. It is concluded that the photogrammetric 3D point cloud is an alternative for estimating forest variables at the tree level.
Why it matches plant phenotyping methodsドローンのデジタル写真測量による3D点群から、個体レベルの樹高・胸高断面積・材積を推定する手法を開発・比較検証しており、植物形質取得が研究の中心である。
abstractThe objective was to evaluate the potential of the geospatial point cloud generated by photogrammetry of aerial photographs captured by a low-cost drone in the estimation of dendrometric and dasometric variables in conifer species.
Abstract In tropical ecosystems, accurately quantifying vegetation structure is crucial to determining their capacity to deliver ecosystem services. Terrestrial laser scanning (TLS) and UAV‐based digital aerial photogrammetry (DAP) are remote sensing tools used to assess vegetation structure, but are challenging to use with conventional methods. Single‐Scan TLS and DTM‐independent DAPs are alternative scanning approaches used to describe vegetation structure; however, it remains unclear to what extent they relate to each other and how accurately they can distinguish forest structural characteristics, including vertical structure, horizontal structure, vegetation density, and structural heterogeneity. First, we quantified bivariate and multivariate correlations between equivalent/analogous structural metrics from these data sources using principal component and Procrustes analysis. We then evaluated their ability to characterize the forest and agroforestry landscapes. DAP, TLS, and Field metrics were moderately aligned for vegetation density, canopy top height, and gap dynamics, but differed in height variability and surface heterogeneity, reflecting differences in data structure. DAP and TLS achieved the highest accuracy in classifying forests and agroforestry plots, with overall accuracies of 89% and 78%, respectively. Though the field metrics were unable to resolve 3D characteristics related to heterogeneity, their capacity to distinguish the stand structure at 69% accuracy was driven by the relative pattern of its suite of metrics. The results indicate that the single‐scan TLS and DTM‐independent DAP yield meaningful descriptors of vegetation structure, which, when combined, can provide a comprehensive representation of the structure in these tropical landscapes.
Why it matches plant phenotyping methodsTLSとUAV-DAPによる植生構造形質の取得・比較精度を評価しており、センサー計測法の検証と実質的な適用が中心である。
abstractTerrestrial laser scanning (TLS) and UAV‐based digital aerial photogrammetry (DAP) are remote sensing tools used to assess vegetation structure
Accurately quantifying stump volume on post-harvested sites is required to assess potential volume gains for biomass utilisation. The relatively uniform distribution and shape of stumps across such sites makes them well-suited for detection using machine learning (ML) algorithms. Recent developments in the analysis of Digital Aerial Photogrammetry (DAP) data acquired by unmanned aerial vehicles (UAVs) have enabled the reliable identification of stumps via advanced ML methods. Furthermore, the processed outputs from these algorithms provide estimates of stump diameter and height, facilitating calculations of biomass volume. This integration of UAV-based photogrammetry and ML techniques presents a promising approach for enhancing forest management and biomass assessment. In this study, we trained three different ML model types: Faster Region-based Convolutional Neural Network (R-CNN), Single Shot Multibox Detector (SSD) and You-Only-Look-Once (YOLO). The data for the virtual stump detections came from two Norwegian sites, with stumps of Picea abies (L.) H.Karst., and three South African sites, with stumps of Pinus patula Schiede ex Schltdl. & Cham. We assessed the detection rates of each model and compared metrics by using similarly annotated images. The resultant encapsulating bounding boxes of detected stumps were used to calculate diameters and compared to field measurements. Each bounding box is rectangular in shape, and the average of the height and width was calculated to get an estimated diameter value. Virtual stump heights were determined from the Digital Surface Model (DSM) by subtracting the mean height of the surrounding area from the mean height of the stump. The calculated heights of the stumps can be used to assess potential loss of wood volume due to inefficient harvesting techniques. Similarly, the calculated wood volume can be used to estimate residual biomass, and therefore assist Foresters in deciding how best to utilise these stumps. Visible stumps on post-harvested sites could be detected with high rates of accuracy, with almost perfect precision from some object detection models, albeit at low levels of recall. Overall, all three model types had an F1-score of above 73% with the best model attaining an F1-score of 89%. Stump diameters were generally overestimated and this was not found to be related to stump size. Stump heights were underestimated in most cases.
Why it matches plant phenotyping methodsUAV画像と機械学習を用いて切り株を検出するだけでなく、直径・高さ・体積を推定し、野外測定と比較検証しているため、植物器官形質の取得手法が中心である。
abstractThe resultant encapsulating bounding boxes of detected stumps were used to calculate diameters and compared to field measurements.
Grain yield is one of the most critical indicators for evaluating the performance of wheat breeding. However, the assessment process, from early-stage breeding lines to officially registered varieties that have passed the DUS (Distinctness, Uniformity, and Stability) test, is often time-consuming and labor-intensive. Multispectral remote sensing based on unmanned aerial vehicles (UAVs) has demonstrated significant potential in crop phenotyping and yield estimation due to its high throughput, non-destructive nature, and ability to rapidly collect large-scale, multi-temporal data. In this study, multi-temporal UAV-based multispectral imagery, RGB images, and canopy height data were collected throughout the entire wheat growth stage (2023–2024) in Xuzhou, Jiangsu Province, China, to characterize the dynamic growth patterns of both breeding lines and registered cultivars. Vegetation indices (VIs), texture parameters (Tes), and a time-series crop height model (CHM), including the logistic-derived growth rate (GR) and the projected area (PA), were extracted to construct a comprehensive multi-source feature set. Four machine learning algorithms, namely a random forest (RF), support vector machine regression (SVR), extreme gradient boosting (XGBoost), and partial least squares regression (PLSR), were employed to model and estimate yield. The results demonstrated that spectral, texture, and canopy height features derived from multi-temporal UAV data effectively captured phenotypic differences among wheat types and contributed to yield estimation. Features obtained from later growth stages generally led to higher estimation accuracy. The integration of vegetation indices and texture features outperformed models using single-feature types. Furthermore, the integration of time-series features and feature selection further improved predictive accuracy, with XGBoost incorporating VIs, Tes, GR, and PA yielding the best performance (R2 = 0.714, RMSE = 0.516 t/ha, rRMSE = 5.96%). Overall, the proposed multi-source modeling framework offers a practical and efficient solution for yield estimation in early-stage wheat breeding and can support breeders and growers by enabling earlier, more accurate selection and management decisions in real-world production environments.
Why it matches plant phenotyping methodsUAVマルチスペクトル・RGB・草冠高データから生育形質を抽出し、機械学習で収量を推定するワークフローが研究の中心であり、育種ラインの表現型評価に実質的に適用している。
abstractMultispectral remote sensing based on unmanned aerial vehicles (UAVs) has demonstrated significant potential in crop phenotyping and yield estimation due to its high throughput, non-destructive nature, and ability to rapidly collect large-scale, multi-temporal data.
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.
Olea europaea L. is an economically and ecologically significant species, for which accurate biomass estimation provides critical insights for artificial propagation, yield forecasting, and carbon sequestration assessments. Currently, research on biomass estimation for Olea europaea L. remains scarce, and there is a lack of efficient, accurate, and scalable technical solutions. To address this gap, this study achieved, for the first time, non-destructive estimation of Olea europaea L. biomass across individual tree to plot scales by integrating UAV-RGB (Unmanned Aerial Vehicle-Red-Green-Blue) imagery with the U2-Net model. This study initially developed allometric models for W-D-H, CA-D, and CA-H in Olea europaea L. (where W = biomass, D = ground diameter, H = tree height, and CA = canopy area). A single-parameter CA-based whole-plant biomass model was subsequently developed utilizing the optimal models. An innovative whole-plant biomass estimation model (UAV-RGB, U2-Net Total Biomass, UUTB) that combines UAV-RGB imagery with U2-Net at the sample-plot level was developed and assessed. The results revealed the following: (1) The model for Olea europaea L. aboveground biomass (AGB) was WA = 0.0025D1.943H0.690 (R2 = 0.912), the model for belowground biomass (BGB) was WB = 0.012D1.231H0.525 (R2 = 0.693), the model for CA-D was D = 4.31427C0.513 (R2 = 0.751), CA-H model was H = 226.51939C0.268 (R2 = 0.500). (2) The optimal AGB model for CA single-parameter was WA = 1.80901C1.181 (R2 = 0.845), and the model for BGB was WB = 1.25043C0.772 (R2 = 0.741). (3) The R2 of Olea europaea L. biomass, as estimated by CA derived from the U2-Net and UUTB models, was 0.855. This study presents the first integration of UAV-RGB imagery and the U2-Net model for biomass estimation in Olea europaea L., which not only addresses the research gap in species-specific allometric modeling but also overcomes the limitations of traditional manual measurement methods. The proposed approach provides a reliable technical foundation for accurate assessment of both economic yield and ecological carbon sequestration capacity.
Why it matches plant phenotyping methodsUAV-RGB画像とU2-Netを統合し、個体からプロット規模でオリーブのバイオマスという植物形質を非破壊推定する手法を開発・評価しており、表現型取得・推定が研究の中心である。
abstractAn innovative whole-plant biomass estimation model (UAV-RGB, U2-Net Total Biomass, UUTB) that combines UAV-RGB imagery with U2-Net at the sample-plot level was developed and assessed.
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 · UnverifiedCrossref · checked 13 Sept 2026
The accurate prediction of plant height is crucial for optimizing soybean cultivar selection and improving yield estimations. In this study, we investigate the potential of machine learning (ML) algorithms to predict soybean plant height (PH) based on a diverse set of agronomic parameters analyzed from forty soybean cultivars evaluated across sequential harvests. Using a comprehensive dataset, the models Elastic Net (EN), Extra Trees (ET), Gaussian Process Regressor (GPR), K-Nearest Neighbors, and XGBoost (XGB) were compared in terms of predictive accuracy, uncertainty, and robustness. Our results demonstrate that ET outperformed other models with an average correlation coefficient of 0.674, R2 of 0.426 and the lowest RMSE of 6.859 cm and MAE of 5.361 cm, while also showing the lowest uncertainty (5.07%). The proposed ML framework includes an extensive model evaluation pipeline that incorporates the Performance Index (PI), ANOVA, and feature importance analysis, providing a multidimensional perspective on model behavior. The most influential features for PH prediction were the number of stems (NS) and insertion of the first pod (IFP). This research highlights the viability of integrating explainable ML techniques into agricultural decision support systems, enabling data-driven strategies for cultivar evaluation and phenotypic trait forecasting.
Why it matches plant phenotyping methods大豆の草丈という植物形質を予測する機械学習フレームワークを提案し、複数モデルの精度・不確実性・頑健性を比較評価しており、形質推定手法が研究の中心である。
abstractThe proposed ML framework includes an extensive model evaluation pipeline that incorporates the Performance Index (PI), ANOVA, and feature importance analysis, providing a multidimensional perspective on model behavior.
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:1-84Plant 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.
Cotton is a significant broadacre crop globally, and monitoring its growth is crucial for improving agricultural productivity. With the development of unmanned aerial vehicle (UAV) remote sensing technology, the inversion of cotton growth parameters (including plant height (PH), and leaf chlorophyll content (LCC), leaf area index (LAI), above-ground biomass (AGB)) from remote sensing data has emerged as a prominent research area. To address the issue of limited accuracy in traditional stacking algorithm for remote sensing inversion, this study proposes an enhanced stacking algorithm (ESA). First, multi-source remote sensing data is acquired using UAV equipment equipped with an integrated payload of a LiDAR sensor and a visual RGB camera and raw features are extracted from the data. Then, principal component analysis (PCA) is used to reduce the dimensionality of these features. The model construction is optimized through the following steps: first, explore all feature combinations and train each using multi-class learners to construct the entire set of base models; second, remove over- or under-fitting models to build a candidate pool; third, introduce iterative screening to the pool—each round incorporates the algorithm with the greatest performance gain and removes those with negative contributions, iterating to construct an efficient subset of base models; finally, RidgeCV is used to fuse base-model outputs. The experimental results show that ESA outperforms other traditional methods in terms of prediction performance for the four growth parameters. Specifically, on the test set, the R2 values for PH, LCC, LAI, and AGB are 0.9320, 0.8015, 0.8638, and 0.8272 , respectively. Compared with the second-best model, the relative improvement is approximately 4.6% (PH), 3.6% (LCC), 7.5% (LAI), and 11.2% (AGB) . ESA offers an effective approach for high-precision inversion of cotton growth parameters, providing new insights for the precision management of other crops.
Why it matches plant phenotyping methodsUAVのLiDAR・RGBデータから綿花の複数生育形質を推定するスタッキングアルゴリズムを開発し、他手法と性能比較しており、表現型取得・推定法が中心です。
abstractthis study proposes an enhanced stacking algorithm (ESA)
• A UAV-based novel phenotyping pipeline using multispectral imaging and LASSO regression accurately predicts soybean traits and yield across growth stages by selecting key vegetation indices. • Red-edge and NIR indices best predict plant height, stomatal conductance, and yield. • Chlorophyll-related indices were effective for estimating LAI and leaf chlorophyll. • Best aerial phenotyping time is between pod development and the full seed stage. Morphophysiological parameters, such as plant height, leaf chlorophyll content, stomatal conductance, and leaf area index, are key indicators of soybean ( Glycine max (L.) Merril) yield potential. Traditional in situ methods for assessing these traits, while accurate in small areas, are slow, labor-intensive, and impractical for large-scale monitoring. Similarly, extrapolating yield from manual counts of plant stands, pods, and seeds per pod may provide unreliable results. Therefore, high throughput sensor-based approaches are becoming increasingly popular to efficiently quantify these traits and predict yield. Among various remote sensing sensors, multispectral provides information in the red, green, red-edge, and near-infrared bands, which are critical for studying plant growth and vegetation health by combining multiple spectral bands. While many studies have used vegetation indices (VIs) to estimate individual traits, fewer have predicted multiple traits and yield at the same time using multispectral data. Thus, a study was conducted to identify the most effective VIs and determine the optimal timing for aerial phenotyping using multispectral sensors and LASSO regression. The study suggested Red-edge and NIR-based indices were best for predicting plant height, stomatal conductance, and yield, while chlorophyll-related indices were more effective for LAI and chlorophyll content. The pod development to full seed stages was the best time for aerial phenotyping. Overall, UAV-derived MS images combined with LASSO regression proved to be a practical and efficient approach for large-scale soybean phenotyping and yield monitoring. This study supports precision agriculture by providing a remote sensing-based, rapid, and non-destructive method for assessing crop status.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像とLASSO回帰による複数の植物形質・収量の推定手法を開発・評価しており、フェノタイピング手法が研究の中心である。
abstractA UAV-based novel phenotyping pipeline using multispectral imaging and LASSO regression accurately predicts soybean traits and yield across growth stages by selecting key vegetation indices.
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.
As an economically important crop, tobacco requires the precise extraction of phenotypic characterization data, which is crucial for breeding, cultivation practices, physiological research, and industrial applications. However, there is currently a lack of automated algorithms for extracting key basic phenotypic traits such as plant height, leaf number, leaf area, and stem-leaf angle. In this study, we developed a set of computational methods for fully automated extraction of these phenotypic features from 3D tobacco point cloud data. Specifically, our pipeline includes: (1) preprocessing the 3D point cloud data, involving operations such as downsampling, denoising, normal vector estimation, and coordinate transformation; (2) integrating a graph neural network with a region-growing algorithm to segment leaves, stems, and other organs, and refining the segmentation results to address the challenge of overlapping leaves; and (3) calculating fundamental phenotypic attributes including plant height, leaf count, leaf area, and stem-leaf angle based on the segmentation output. Additionally, to address potential gaps in the scanned point cloud, we implemented perforation detection and repair operations. The effectiveness and accuracy of the proposed algorithm were validated through mathematical model simulations. Distinct from traditional statistical discriminative methods, this approach provides a novel framework for the precise extraction of tobacco phenotypic data.
Why it matches plant phenotyping methods3D点群から植物器官を分割し、草丈・葉数・葉面積・茎葉角を自動抽出する計算手法の開発と検証が中心である。
abstractwe developed a set of computational methods for fully automated extraction of these phenotypic features from 3D tobacco point cloud data.
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 the context of precision agriculture, high-throughput phenotyping (HTP) aims to rapidly and effectively identify factors that affect crop yield, enabling timely and appropriate interventions. However, interpreting data from HTP remains challenging. We performed a proximal red-green-blue (RGB)-based HTP on several tomato genotypes exposed to abiotic stress (drought) or biotic stress induced by tomato spotted wilt virus (TSWV), Pseudopyrenochaeta lycopersici (corky root rot; CRR), or Meloidogyne incognita (root-knot nematode; RKN). We aimed to determine if RGB-based HTP is effectively able to: a) distinguish the effects of biotic from abiotic stress; b) differentiate resistant/tolerant from susceptible genotypes. Our HTP data analysis produced 12 morphometric and eight colorimetric indices. Principal Component Analysis (PCA; P < 0.0001; 83 % variation explained by three PCs) showed that factors such as shoot area solidity and certain color-based indices, including the senescence index and green area, effectively differentiated biotic from abiotic stress. Morphometric parameters, including plant height, projected shoot area, and convex hull area, proved to be applicable for identifying the stress status regardless of the type of stress. HTP effectively distinguished the genotype resistant to TSWV from the susceptible ones. This task was more challenging for below-ground stresses like CRR and RKN. Different profiles of HTP indices were observed among the genotypes assayed for drought tolerance, indicating variability in their ability to withstand drought conditions. In conclusion, our findings highlight the value of RGB-based HTP as a tool for precision farming of tomatoes, enabling the identification of both biotic and abiotic stressors.
Why it matches plant phenotyping methodsトマトのRGBベース高スループット表現型解析を用い、形態・色彩指標の抽出と、ストレス識別および遺伝子型判別への有効性を評価しており、フェノタイピング手法が研究の中心である。
abstractWe performed a proximal red-green-blue (RGB)-based HTP on several tomato genotypes exposed to abiotic stress (drought) or biotic stress induced by tomato spotted wilt virus (TSWV), Pseudopyrenochaeta lycopersici (corky root rot; CRR), or Meloidogyne incognita (root-knot nematode; RKN).
Reproduction assets foundThe paper's HTP dataset (20 indices from five stress experiments) is stated to be available in the supplementary material hosted at the article DOI, which qualifies as a paper-specific public phenotype dataset. However, the analysis code has no public deposit: it is only available from the corresponding author upon 'a'Dataset · publicThe data collected and used in this study are available in the supplementary material. The code used for analysis is available from the corresponding author, GBu, upon reasonable request.Open asset ↗lines:400-518Plant phenotyping relevance match · UnverifiedOpenAlex · checked 13 Sept 2026
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
PURPOSE: Spatial variability within fields and unpredictable rainfall patterns make nitrogen (N) management challenging, with up to 65% of applied N being lost to the environment. Post-emergence sidedress applications of N fertilizer can improve plant uptake and reduce N losses, making it critical to efficiently identify corn (Zea mays L.) N status at early growth stages. We hypothesized that indicators of plant structure (plant height and canopy cover fraction), canopy greenness (vegetation indices), and their integration with soil and topography-related properties would improve the prediction of early-season corn N status. The objectives of this study were to: (1) evaluate plant height, canopy cover fraction (CCF), and vegetation indices (VI) as indicators of biomass, N concentration, and N uptake at early growth stages (~ V4); (2) assess whether linear models integrating UAV-derived CCF with VI improve N uptake prediction; and (3) determine whether incorporating soil and topographic parameters from publicly available datasets into machine learning (ML) models improves performance over linear regressions. METHODS: Two large-scale field trials were conducted in Indiana during the 2019 growing season. Multispectral UAV (MicaSense Altum, 0.03 m resolution) and satellite imagery (Planet, 3 m resolution) were acquired and processed to extract CCF and calculate VI. Biomass samples were collected to determine N uptake. Linear regressions and three ML models were evaluated. RESULTS: Plant structural metrics, CCF and plant height, were the most reliable predictors of biomass and N uptake (R² up to 0.95). Integrating CCF with NIR-based VI improved or maintained model performance. Adding soil and topographic metrics provided limited improvement. CONCLUSION: Linear regression models performed comparably to ML approaches, emphasizing the utility of simpler models for supporting more efficient in-season fertilizer applications. Performance differences across sites reflected variability in crop development and underscore challenges in model generalization.
Why it matches plant phenotyping methodsUAV・衛星画像から植物構造、キャノピー被覆率、植生指数を抽出し、トウモロコシのバイオマスおよび窒素吸収量を予測する手法を比較・評価しており、フェノタイピング手法が中心である。
abstractThe objectives of this study were to: (1) evaluate plant height, canopy cover fraction (CCF), and vegetation indices (VI) as indicators of biomass, N concentration, and N uptake at early growth stages (~ V4); (2) assess whether linear models integrating UAV-derived CCF with VI improve N uptake prediction; and (3) determine whether incorporating soil and topographic parameters from publicly available datasets into machine learning (ML) models improves performance over linear regressions.
Abstract— Plant communities dominated by reeds (Phragmites altissimus (Benth.) Mabille, Phragmites australis (Cav.) Trin. ex Steud.) are widely distributed in floodplain and delta landscapes. Despite their significant biospheric role and potential for industrial use, insufficient attention has been paid to the mapping and assessment of these communities in Russia. The objective of this study is to explore the possibilities of mapping biomass and vegetation height in reed-dominated communities in the Volga Delta using Sentinel-1/2 satellite data supported by ground measurements and aerial surveys conducted with a drone. Allometric relationships between the heights, stem diameters of reeds, and biomass were established for 92 sample plots within the Astrakhan Nature Reserve in the Volga Delta enabling the use of aerial imagery to obtain reference data through photogrammetric methods. The application of vegetation height calculated photogrammetrically based on aerial imagery across 27 test polygons combined with temporally distinct satellite data and the Random Forest nonparametric regression method yielded a high accuracy in mapping heights (coefficient of determination R2 = 0.80, root mean square error (RMSE) 0.46 m) and biomass (R2 = 0.65, RMSE = 12.6 t/ha) of reed-dominated communities in the Volga Delta. Thus, the approach employed proves to be effective for mapping the biomass of reed communities in the Volga Delta and similar landscapes.
Why it matches plant phenotyping methodsヨシ群落の高さ・バイオマスという植物形質を、衛星画像、ドローン空撮、写真測量、回帰モデルで推定・検証する方法が研究の中心である。
abstractThe objective of this study is to explore the possibilities of mapping biomass and vegetation height in reed-dominated communities in the Volga Delta using Sentinel-1/2 satellite data supported by ground measurements and aerial surveys conducted with a drone.
Industrial hemp (Cannabis sativa L.) is known for its high fiber production with lower ecological footprint. Nitrogen (N) status and stem biomass (SB) and total above-ground biomass (AGB) of the crop highly influence fiber quantity and quality. Conventional monitoring practices are labour intensive and time consuming. Unmanned Aerial Vehicles (UAVs) with imaging sensors can be a promising tool for mitigating these challenges. This study evaluated the performance of multispectral camera-equipped UAV in predicting key agronomic parameters, i.e., plant height (PH), Leaf Nitrogen Uptake (LNU) and SB and AGB. Field trials were conducted at UF/IFAS West Florida Research and Education Centre, Jay, FL during the years 2021 and 2022 consisting of two cultivars and six N treatments. The PH was estimated through Crop Height Model, yielding an R² of 0.87 at full crop maturity (90 days after planting). Twenty-seven Vegetation Indices (VIs) were extracted and features, including PH and VIs, were selected through Recursive Feature Elimination with adjusted Variance Inflation Factor (VIF<10) to develop machine learning models for the estimation of yield components. The LNU prediction was best with Support Vector Machine model with R², RMSE and nRMSE % value of 0.364, 34.55 kg N ha⁻¹ and 68.48 respectively. Random Forest Regressor predicted the SB and total AGB most accurately with R², RMSE and nRMSE % value of 0.752 and 0.707, 890.70 and 1492.73 kg ha⁻¹, 48.86 and 43.05 respectively. The results demonstrate the potential of UAVs to generate more reliable estimates of PH, SB and total AGB whereas it remained unreliable for LNU.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と機械学習を用いて植物高、窒素吸収、茎・地上部バイオマスを推定し、モデル性能を評価しているため、表現型取得・推定法が中心である。
abstractThis study evaluated the performance of multispectral camera-equipped UAV in predicting key agronomic parameters, i.e., plant height (PH), Leaf Nitrogen Uptake (LNU) and SB and AGB.
Accurate plant organ segmentation and efficient phenotypic parameter acquisition remain major challenges in plant phenomics. This study develops an automated phenotyping framework for maize that integrates deep learning with 3D point cloud analysis to overcome the inefficiency and subjectivity of traditional manual methods. A high-quality 3D maize point cloud dataset was constructed, and a segmentation model named PSCSO was proposed based on the PointNet++ architecture. The model incorporates an SCConv module to reduce feature redundancy and uses the Sophia optimizer to improve convergence efficiency. Experimental results show the model achieved segmentation accuracies of 0.926 on the training set and 0.861 on the testing set, with a MIoU of 0.843, while significantly reducing training time. Based on the segmentation results, the model automatically estimates seven key phenotypic parameters: plant height, crown diameter, stem height, stem diameter, leaf length, leaf width, and leaf area. This is achieved by integrating point cloud algorithms including linear regression, PCA, and Delaunay triangulation. The predictions showed excellent agreement with manual measurements, with all parameters achieving R2 values exceeding 0.91. Overall, this automated framework provides a reliable and high-throughput solution for plant phenotypic analysis.
Why it matches plant phenotyping methods3D点群と深層学習によるトウモロコシ器官セグメンテーションおよび7種類の表現型形質推定を開発・検証した研究であり、フェノタイピング手法が中心である。
abstractThis study develops an automated phenotyping framework for maize that integrates deep learning with 3D point cloud analysis
Abstract The use of unmanned aerial vehicles for remote sensing is an effective method for monitoring crop growth, particularly for tall crops such as maize. High‐resolution imagery obtained from unmanned aerial vehicles enables the measurement of plant height, which is a critical indicator of crop growth. However, a reference plant height is required to assess growth. This study aimed to develop a model to predict the reference height for growth assessment using temperature data. Furthermore, a methodology was proposed to estimate model parameters from the relative maturity, thus enabling adaptation to a range of maize varieties. In 2022 and 2023, maize plant height was measured using an unmanned aerial vehicle at two flying altitudes (40 and 100 m) several times for 12 varieties with varying relative maturity. Moreover, a regression model was developed to predict the silking stage and identify the optimal sensing time 1 week before the silking stage. The results showed that the growth rate was not statistically different among the varieties, indicating that maximum plant height was determined by the duration of the growth period. A growth model was developed based on these results. The root mean square error (RMSE) for the model was 0.16 and 0.15 m for data sets from 40‐ and 100‐m altitudes, respectively. In estimating plant height, this growth model performed marginally better than the logistic curves used in existing studies. Additionally, a linear relationship was observed between relative maturity and the parameters of the developed growth model. Consequently, the newly developed growth model can predict the plant height for new varieties because the parameters of the model can be inferred from the relative maturity.
Why it matches plant phenotyping methodsUAV画像によるトウモロコシ草高推定のための成長モデルと成熟度に基づく適応手法を開発・評価しており、植物形質取得が中心である。
abstractThis study aimed to develop a model to predict the reference height for growth assessment using temperature data.
Abstract Unoccupied aerial vehicle (UAV)‐based high‐throughput phenotyping provides scalable and cost‐effective access to phenotypic information for crop improvement, yet its application in minor crops such as lentil ( Lens culinaris Medik.) remains limited. This study applied UAV‐derived canopy traits and crop growth regression modeling to a nested association mapping population developed from CDC Redberry crossed with 32 diverse founder lines. UAV imagery collected across four site‐years was used to capture canopy height, crop area, and crop volume per plot basis at multiple time points. Crop growth regression models were fitted to derive crop growth parameters, maximum canopy size, growth rate, and cumulative growth anchored to phenological stages. These static and time‐series traits were evaluated for seed yield prediction using partial least squares regression with a 70:20:10 data split and 10‐fold cross‐validation. Static traits such as maximum crop volume and maximum crop area were consistently associated with yield. Dynamic trait‐based models improved prediction accuracy and identified the swollen pod stage (R5–R6) as the most informative forecasting window. External validation using an independent trial confirmed the generalizability of the approach. This study presents a UAV phenotyping framework that supports crop growth dissection and early yield prediction and downstream trait discovery in lentil.
Why it matches plant phenotyping methodsUAV画像から作物の形態・成長形質を抽出し、時系列成長モデルと収量予測を検証するフレームワークが研究の中心であるため。
abstractUAV imagery collected across four site‐years was used to capture canopy height, crop area, and crop volume per plot basis at multiple time points.
Reproduction assets foundThe paper's data availability statement points to a public KnowPulse experiment page hosting the study's UAV-derived phenotyping and yield data; no author analysis code repository is stated.Dataset · publicof S).
We thank Dr. Ana Vargas at the Crop Development Center, U
of S for generously providing yield data from the independent
field trial.
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 data supporting this study are available at:
https://knowpulse.usask.ca/experiment/AGILE-NAM-UAV-growth-modelling or from the authors upon request.
O RC I D
SandeshNeupane https://orcid.org/0000-0003-3679-1046
KirstinE. Bett https://orcid.org/0000-0001-7959-6959
SteveJ. Shirtliffe https://orcid.org/0000-0002-3603-7417
R E F E R E N C E S
Araus, J. L., Kefauver, S. C., Zaman-Allah, M., Olsen, M. S., & Cairns, J.Open asset ↗KnowPulse · AGILE-NAM-UAV-growth-modellingpdf-raw-page:15 lines:1-90Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 6 Sept 2026
Plant height (PH) is closely linked to yield potential, lodging resistance, and mechanized harvesting efficiency in peanut cultivation. However, breeding efforts for optimized PH are hindered by limited understanding of its genetic architecture. In this study, we utilized a UAV-based high-throughput phenotyping platform to monitor the dynamic growth of 241 peanut accessions across four trials. Using UAV-LiDAR data, we precisely measured time-series PH and applied Gaussian fitting and principal component analysis (PCA) to extract five dynamic growth parameters: parameter a (maximum plant height), b (time to reach maximum height), c (variation extent of PH), (interpreted as average height), and (growth rate). Genome-wide association studies (GWAS) identified 1,133 candidate genes associated with parameters a , b , c , and , and differential expression of genes (DEGs) analysis combined with weighted correlation network analysis (WGCNA) further identified Arahy.1026BX as a candidate gene. This gene is involved in the shikimate pathway and is crucial for the synthesis of auxin and lignin. Reverse transcription quantitative real-time PCR (RT-qPCR) and virus-induced gene silencing (VIGS) experiments validated the significant effect of Arahy.1026BX on peanut PH. Overall, our study integrates advanced UAV-LiDAR time-series phenotyping with genome-wide association study to identify potential candidate genes associated with PH, which providing valuable breeding insights for developing peanut varieties with ideal PH and improving peanut yield.
Why it matches plant phenotyping methodsUAV-LiDARによる時系列の草丈取得と動的成長パラメータ抽出が研究の中心的手法であり、植物表現型解析プラットフォームを実質的に適用している。
abstractwe utilized a UAV-based high-throughput phenotyping platform to monitor the dynamic growth of 241 peanut accessions across four trials.
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イチゴ植物の3D再構成、背景除去、形質推定を一体化した新規フェノタイピング手法の開発・評価が中心であり、植物高と樹冠幅を自動推定して精度・効率を比較している。
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.
Abstract. Historical aerial images, captured by film cameras in the previous century, are valuable resources for quantifying Earth's surface and landscape changes over time. In the post-war period, these images were often acquired to create topographic maps, resulting in the acquisition of large-scale aerial photographs with stereo coverage. Photogrammetric techniques applied to these stereo images enable the extraction of 3D information to reconstruct digital surface models (DSMs) and orthoimages. Here, we present a highly automated photogrammetric approach for generating countrywide DSMs of Switzerland, at a 1 m resolution, from approximately 32 000 scanned aerial stereo images acquired between 1979 and 2006, with known exterior and interior orientation. We derived four countrywide DSMs for the epochs 1979–1985, 1985–1991, 1991–1998, and 1998–2006. From the DSMs, we generated corresponding countrywide vegetation height models (VHMs). We assessed the quality of the historical DSMs at the country scale and within six representative study sites, evaluating the vertical accuracy and the completeness of image matching across different land cover types. Mean completeness ranged from 64 % for “glacial and perpetual snow” to 98 % for “sealed surfaces”, with a value of 93 % for the “closed forest” class. Across Switzerland, the median elevation accuracy of the historical DSMs compared with a reference digital terrain model (DTM) on sealed surface points ranged from 0.08 to 0.16 m, with a normalized median absolute deviation (NMAD) of around 0.8 m and a maximum root mean square error (RMSE) of 1.20 m. Similar accuracies are obtained when comparing historical DSMs with measured geodetic points. The VHMs generated in this study enabled the detection of major changes in forest areas due to windstorm damage, forest dynamics, and growth. This work demonstrates the feasibility of generating accurate, very-high-resolution DSM time series (spanning three decades) and VHMs from historical aerial images of the entire surface of Switzerland in a highly automated manner. The VHMs are already being used to estimate countrywide biomass changes. The countrywide DSMs and VHMs for the four epochs, along with auxiliary data, are available online at https://doi.org/10.16904/envidat.528 (Marty et al., 2024) and can be used to quantify long-term elevation changes and related processes across different surfaces.
Why it matches plant phenotyping methods歴史的航空画像から植生高モデルを生成する自動写真測量法を開発・精度評価し、森林の高さ変化という植物キャノピー形質を抽出しているため、測定法が中心的である。
abstractFrom the DSMs, we generated corresponding countrywide vegetation height models (VHMs).
Reproduction assets foundThe paper's countrywide DSMs, VHMs, and auxiliary rasters (matching mask, vegetation mask, metadata shapefile) for four epochs are deposited publicly on EnviDat with an explicit DOI. These vegetation height models are the paper's plant/canopy phenotyping measurements. No author analysis code or trained models are namedDataset · publicDatasets can be accessed from EnviDat ( https://doi.org/10.16904/envidat.528 , Marty et al., 2024). The following files are available for the four epochs: countrywide digital surface model (DSM), hillshaded DSM, and vegetation height models (VHMs).Open asset ↗Envidat · 10.16904/envidat.528lines:249-256Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 6 Sept 2026
Published3 Nov 2025ISPRS annals of the photogrammetry, remote sensing and spatial information sciencesCited by 1 · OpenAlex ↗
Abstract. Plant height is, as a crucial indicator, capable of reflecting the health status and growth vigor at various growth stages. It provides essential information for increasing crop yield, optimizing cultivation strategies, and improving varieties. Traditional plant height measurements using tapes or rods are labour-intensive, time-consuming, subject to human errors, and inadequate for large-scale observations. In recent years, unmanned aerial vehicles (UAVs) equipped with RGB cameras have demonstrated significant advantages in terms of efficiency and cost-effectiveness, enabling detailed 3D reconstruction of complex farmland environments through photogrammetry techniques. Therefore, we develop a high-throughput plant height measurement approach for the field peanuts from low-cost UAV photogrammetry. First, a UAV platform equipped with RGB camera is used to collect high-resolution imagery, covering the entire peanut growth stages. Following this, the aerial images are processed and precisely aligned with positional and orientation system (POS) data, subsequently generating Digital Surface Models (DSMs). Among these DSMs, the one representing the bare soil period was considered as the Digital Elevation Model (DEM). Afterwards, each plot is clipped based on its minimum bounding rectangles, creating Canopy Height Models (CHMs) by subtracting the DEM from the corresponding DSMs. Finally, Peanut plant heights are estimated via histogram distribution analysis of CHMs and validated with manually measured heights in Wangbian Community, Ningyang County, Tai'an City, Shandong Province. Experimental results indicate excellent effectiveness and reliability, achieving coefficients of determination (R2) of 0.9424 and RMSE of 2.26 cm. These observations demonstrate UAV photogrammetry's practical potential for large-scale crop phenotyping applications.
Why it matches plant phenotyping methodsUAVフォトグラメトリとCHM解析による落花生の草丈推定法を開発し、手測定で検証しており、植物形質取得が研究の中心である。
abstractTherefore, we develop a high-throughput plant height measurement approach for the field peanuts from low-cost UAV photogrammetry.
As global agriculture shifts to intelligence and precision, crop attribute detection has become foundational for intelligent systems (harvesters, UAVs, sorters). It enables real-time monitoring of key indicators (maturity, moisture, disease) to optimize operations—reducing crop losses by 10–15% via precise cutting height adjustment—and boosts resource-use efficiency. This review targets harvesting-stage and in-field monitoring for grains, fruits, and vegetables, highlighting practical technologies: near-infrared/Raman spectroscopy (non-destructive internal attribute detection), 3D vision/LiDAR (high-precision plant height/density/fruit location measurement), and deep learning (YOLO for counting, U-Net for disease segmentation). It addresses universal field challenges (lighting variation, target occlusion, real-time demands) and actionable fixes (illumination compensation, sensor fusion, lightweight AI) to enhance stability across scenarios. Future trends prioritize real-world deployment: multi-sensor fusion (e.g., RGB + thermal imaging) for comprehensive perception, edge computing (inference delay
Why it matches plant phenotyping methods作物属性の検出・監視技術を主題とするレビューで、分光、3Dビジョン、LiDAR、深層学習による植物形質・病害状態の取得方法を中心に整理している。
titleA Review of Crop Attribute Monitoring Technologies for General Agricultural Scenarios
The height of salt marsh vegetation is a key biophysical trait used to assess ecological health, monitor marsh restoration, and evaluate coastal protection from wave attenuation. This study defined a morphology-based correction that improved the accuracy of digital surface model (DSM)-derived canopy height for Spartina alterniflora . Using imagery collected at six drone altitudes (3 m to 120 m), corresponding to ground sampling distances (GSDs) of 1 mm/pixel to 34 mm/pixel, mean canopy height was estimated within 0.5 m by 0.5 m quadrats and validated against in situ RTK-GNSS Rover measurements. DSMs from drone altitudes of 60 m or lower (GSD ≤ 17 mm/pixel) yielded consistent elevation estimates with mean error < 5 cm. Increasing point cloud densification by changing image scale and point density settings did not improve DSM accuracy. Canopy height derived from DSMs was, on average, 60% of the true canopy height measured by Rover. A novel, quantitative assessment of canopy structure showed that the mean vertical position of projected horizontal canopy area was also 60% of the canopy height, suggesting that SfM reconstruction was capturing this position in the canopy, rather than the uppermost plant tips. The canopy structure methodology could be used for other species to estimate the underestimation of canopy height derived from SfM. Overall, this study provides a framework for selecting drone flight settings, processing parameters, and predicting canopy-height correction factors to improve DSM-based plant height measurements in dense canopies and heterogeneous vegetation surfaces.
Why it matches plant phenotyping methodsドローンRGB-SfMによる植物群落高の推定補正法を開発し、RTK-GNSSで検証しており、植物形質取得手法が中心である。
abstractThis study defined a morphology-based correction that improved the accuracy of digital surface model (DSM)-derived canopy height for Spartina alterniflora .
ObjectivePeanuts plant height is a key structural trait for assessing crop growth and nitrogen response. Accurate and efficient height acquisition is essential for monitoring canopy vigor, supporting genotype selection, and enabling precision management. However, conventional ground control point (GCP)-based methods require substantial field deployment and are highly sensitive to local misregistration between multi-temporal digital surface models (DSMs) and digital elevation models (DEMs). In low-stature, prostrate peanut canopies on uneven terrain, such residual elevation errors propagate directly into the canopy height model, severely reducing estimation accuracy. To overcome these limitations, a robust unmanned aerial vehicle (UAV)-based method is developed for peanut plant height estimation using bare-soil invariant constraints. The workflow incorporates crop-mask-assisted fine registration to optimize DSM-DEM alignment and eliminates the need for dense GCP distribution.MethodsField experiments were conducted at a peanut experimental station in Wangbian community, Ningyang county, Tai'an city, Shandong Province, China, using multiple UAV platforms (DJI Mavic 3 Multispectral and DJI MATRICE 350 RTK equipped with a DJI Zenmuse P1 camera), two growth stages (42 d and 49 d after sowing, DAS), and two nitrogen fertilization levels (high nitrogen and low nitrogen). To validate the peanut plant height estimates, representative plants in each plot were selected before and after each UAV image acquisition, and manual measurements from the ground surface to the canopy apex were recorded as the reference plant height. High-resolution digital orthomosaic (DOM) images were then generated from the UAV data, and peanut canopy regions were extracted using the excess green (ExG) index. To mitigate threshold instability caused by variable illumination and soil background conditions, a fixed empirical threshold was combined with an adaptive strategy that integrated Otsu's between-class variance method and the median absolute deviation (MAD), thereby ensuring robust canopy segmentation across growth stages and nitrogen treatments. After canopy extraction, the peanut canopy mask derived from the DOM was used to remove corresponding pixels from the DSM on a per-pixel basis. The DSMs with and without canopy points were then separately used for 3D reconstruction, yielding a canopy point cloud and a bare-soil point cloud. This bare-soil point cloud and a bare-soil DSM acquired before crop emergence (used as the DEM reference) were jointly input into the iterative closest point (ICP) algorithm to solve for a three-dimensional rigid transformation matrix. The resulting matrix was used to jointly optimize translations and rotations along the X, Y, and Z directions. It was applied uniformly to the DSM containing peanut canopy points, thereby achieving fine-scale alignment between the DSM and DEM at the block level. Following registration, the DEM was used as the ground reference, and the canopy height model was constructed by differencing the DSM and DEM pixel by pixel. The 95th percentile (P95) of canopy height within each plot, derived from the canopy height histogram, was used as the representative plant height to reduce the influence of local noise on the statistics.Results and DiscussionsThe results showed that varying the ExG threshold among 0.05, 0.10 and 0.15 had only a limited effect on overall plant height estimation accuracy, with the best performance observed at 0.10. At this threshold, the Mavic 3 platform achieved an R2 of 0.864 7 and a root-mean-square error (RMSE) of 2.57 cm. In contrast, the P1 platform achieved an R2 of 0.918 6 and an RMSE of 2.05 cm, indicating that the proposed threshold selection strategy provided a good balance between accuracy and robustness. Error analysis across different canopy-height percentiles showed that, as the percentile increased from P90 to P99, R2 and RMSE exhibited a typical concave pattern, first improving and then degrading. Among these percentiles, P95 yielded the highest R2 and the lowest RMSE, representing the best trade-off between noise suppression and canopy-top information retention; therefore, P95 was adopted as the representative plant height for this method. Under the P95-based definition of plant height, the traditional GCP method produced R2 values of only 0.592 3－0.669 9 and RMSE values of 4.60~4.94 cm, and the "GCP+ICP" workflow, in which canopy points were not removed prior to ICP registration, was most strongly affected by noise in the point clouds, with R2 dropping below 0.3 in some cases. In contrast, the proposed method maintained R2 values of 0.864 7~0.918 6 and RMSE values of 2.05~2.57 cm across both platforms, markedly improving the agreement between estimated and measured plant height relative to the traditional GCP-based approach. Further platform-specific analysis showed that, owing to its higher spatial resolution, the P1 platform reconstructed a more complete canopy-top structure and yielded better plant height estimates than the Mavic 3 platform at each growth stage. Nevertheless, when combined with the proposed plant height extraction workflow, the Mavic 3 platform still achieved reliable performance (R2 > 0.817 7) in regions with different nitrogen contents, confirming the method's multi-platform applicability. From the perspective of canopy cover and nitrogen level, as the crop progressed from 42 to 49 DAS, the peanut canopy gradually approached full closure, the proportion of high-value pixels in the canopy height model increased, the canopy-top point cloud in the DSM became more continuous, and plant height estimation accuracy improved accordingly. Under high nitrogen treatment, the canopy was denser and structurally more complete than under low nitrogen treatment, resulting in slightly higher R2 and slightly lower RMSE on both platforms; however, these differences remained within a controllable range, demonstrating that the bare-soil-based registration workflow was robust to fertility differences and that the proposed method was stable and transferable across growth stages and fertility conditions.ConclusionsOverall, the proposed method for estimating peanut plant height substantially alleviates the constraints imposed by the misregistration of residual DSM and DEM on plant height inversion for low-stature, prostrate crops. It achieves centimetre-level accuracy for plant height retrieval across platforms and nitrogen treatments. By significantly reducing the dependence on densely distributed GCPs and offering a simple, reproducible, and low-cost processing pipeline, the method provides a scalable technical route for monitoring peanut nitrogen responses, deriving high-throughput agronomic structural traits, and measuring plant height in other low-stature, prostrate crops.
Why it matches plant phenotyping methodsUAV画像・DSM/DEM位置合わせ・キャノピー抽出・点群処理を統合し、ピーナッツ草高という植物形質を推定する手法を開発・検証しており、フェノタイピング手法が研究の中心である。
abstractTo overcome these limitations, a robust unmanned aerial vehicle (UAV)-based method is developed for peanut plant height estimation using bare-soil invariant constraints.
• ADT and DIF precisely regulated elongation and thickening in tomato seedlings. • Canopy traits imaging predicted grafting traits with high accuracy (R 2 > 0.9). • CTPS-imaging integration offers real-time monitoring for grafting suitability. • First trial on tailored production with imaging for an automated grafting. Tomato seedling growth and quality are crucial determinants of the success of grafting and transplant establishment. This study aimed to investigate temperature control strategies in a closed transplant production system (CTPS) and their integration with imaging-based prediction to produce grafting-ready seedlings. Scion ‘Dotaerang Dia’ and rootstock ‘B-Blocking’ were grown under combinations of average daily temperatures (ADTs; 24 and 26 °C) and difference between day and night temperatures (DIFs; –8, –4, 0, +4, and +8 °C). Morphological traits crucial for grafting, including the epicotyl length (EPL) and diameter (EPD) of scions and hypocotyl length (HYL) and diameter (HYD) of rootstocks, and canopy traits, including leaf area index (LAI) and canopy height (CH), were evaluated. Higher ADTs and positive DIFs promoted elongation, whereas lower ADTs and negative DIFs restricted elongation and improved compactness. Compact seedlings with a higher dry matter content are advantageous for grafting, whereas seedlings with greater elongation and dimensional synchrony better meet the requirements of robotic grafting. Imaging-based monitoring using multispectral-derived LAI and light detection and ranging (LiDAR)-derived CH accurately predicted grafting-related traits ( R 2 > 0.9 for EPL, EPD, and HYD); however, HYL predictions were less reliable under negative DIFs. Leave-one-environment-out cross-validation confirmed robust performance for diameter traits across environments. These findings collectively indicate that CTPS enable precise morphological regulation of tomato scions and rootstocks through temperature control, whereas imaging-based phenotyping allows a basis for real-time prediction of grafting suitability. This integration establishes a scalable and automation-ready framework for grafted transplant production, offering a technological foundation for developing automated grafting strategies.
Why it matches plant phenotyping methods画像計測（マルチスペクトルによるLAI、LiDARによる樹冠高）から接ぎ木関連形態形質を予測・検証し、リアルタイムな接ぎ木適性評価を実現する方法が研究の中心である。
abstractCanopy traits imaging predicted grafting traits with high accuracy (R 2 > 0.9).
Abstract. Frame-based VNIR/SWIR multispectral sensors on UAVs offer promising capabilities for precision agriculture by enabling the easy simultaneous acquisition of spectral and structural crop information. This study provides an independent validation of a two-band VNIR/SWIR sensor system for monitoring winter wheat traits and compares the results with previous findings. The UAV flights were conducted on a single date (May 11, 2022), capturing image datasets at wavelengths of 910, 980, 1100, 1200, 1510, and 1650 nm. Structure from Motion (SfM) processing enabled crop height extraction from the same multispectral datasets. Ground-truth data included fresh and dry biomass, moisture, nitrogen concentration, and nitrogen uptake from 36 samples across six varieties and three fertilization levels. Bivariate regression analyses revealed moderate performance for spectral vegetation indices (NRI: R2=0.52–0.61; GnyLi: R2=0.50–0.62), which was lower than that previously reported. Crop height showed a superior predictive capability (R2=0.63–0.75), demonstrating consistency across studies. Multivariate models combining vegetation indices with crop height significantly improved trait estimation (R2=0.72–0.84, nRMSE=0.12–0.15), confirming that integrated spectral-structural approaches provide robust performance even when individual predictors show limitations. While this single-date analysis limits conclusions about temporal stability throughout the growing season, it provides valuable validation of the capabilities of the sensor system. The ability to derive both structural and biochemical data from single-sensor imagery is the key advantage of this camera system. Future research should expand to multi-temporal analyses across complete growing seasons and implement the recently developed 6-channel VNIR/SWIR system to address the current limitations. This study reinforces the fact that combining SWIR spectral features with structural parameters is essential for reliable estimation of crop traits.
Why it matches plant phenotyping methodsUAV搭載VNIR/SWIRセンサーによる作物形質推定を中心に、センサーシステムの独立検証と構造・生化学形質の抽出性能を評価しているため。
abstractThis study provides an independent validation of a two-band VNIR/SWIR sensor system for monitoring winter wheat traits and compares the results with previous findings.
Abstract. Spatial knowledge for supporting precise N fertilization is of key interest in crop management. Therefore, accurate and reliable data on crop dry biomass (DB) and N concentration (Nconc), and N-uptake (Nup) are needed considering spatial heterogeneity. While N uptake in field experiments is computed using in-situ data of DB and Nconc, it also can be directly estimated with remote sensing methods. Usually, these crop traits are derived by using optical remote or proximal sensing approaches. In this contribution, we investigate a paradigm change in providing non-destructive DB, Nconc, and Nup estimates by using non-optical data analyses but structural information extraction. Numerous studies proofed UAV-derived crop height can serve as a robust estimator for biomass. Due to the well-known negative correlation between biomass and N concentration over the growing season crop height might be used as an estimator for Nconc as well. Based on these correlations we investigate three key hypotheses: (i) crop height from UAV images using a Structure from Motion and Multiview Stereopsis (SfM/MVS) workflow serves as a very robust estimator for DB, (ii) Nconc is correlated over the growing season to DB, and (iii) DB is the dominating parameter in determining Nup. Hence, the main research question of this contribution is if UAV-derived crop height (ÚAV-CH) serves as a robust estimator for DB, it also can be used to directly estimate Nconc and Nup, UAV-CH in ultra-high spatial resolution (
Why it matches plant phenotyping methodsUAV画像のSfM/MVSワークフローで作物高を抽出し、バイオマス、窒素濃度、窒素吸収量を推定する手法が研究の中心であるため、植物フェノタイピング手法の実質的応用に該当する。
abstractcrop height from UAV images using a Structure from Motion and Multiview Stereopsis (SfM/MVS) workflow serves as a very robust estimator for DB
Climate change has led growers with uncertainty on crop growth, development, quality and yield. Thus there is a critical need to set up proper tools to help growers follow up their crop during the growth period and ensure better production at the end. In this context we used predictive machine learning models for predictions of Okra development in a controlled lighting environment based on UV-VIS-NIR spectroscopy. Fluorescence and reflectance spectroscopy data was collected from several leaves of Okra grown under different artificial lighting condition, then vegetation spectral indices were computed and used as features for the prediction of four growth and development parameters namely Plant Height (PH), Leaf Number (LN), stem diameter (SD) and Leaf Area Index (LAI). The different trained machine learning models explicitly Linear regression, K-nearest Neighbor, Support Vector Machine, Single Tree, Random Forest, Gradient Boosting, extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM) and Categorical Boosting (CatBoost) give good performance in the prediction of PH (R2 ranged from 0.93 to 0.97), LN (R2 ranged from 0.88 to 0.94), SD (R2 ranged from 0.95 to 0.98) with the tree-based algorithm outperformed the others. However, these trained models give poor performance on the prediction of LAI (R2 ranged from 0.25 to 0.37). Furthermore, the most responsive features and vegetation spectral indices were also identified using Shapley Additive Explanations. This work aims to help growers to follow up their crops development and moreover intend to be used as a decision tool in an overall horticultural management process to engineer their crop development.
Why it matches plant phenotyping methodsUV-VIS-NIRおよび蛍光・反射分光データから、機械学習でオクラの草丈、葉数、茎径、LAIを推定する手法が研究の中心であり、性能評価も実施しているため。
abstractwe used predictive machine learning models for predictions of Okra development in a controlled lighting environment based on UV-VIS-NIR spectroscopy.
Improving light-use efficiency (LUE) is essential for boosting crop productivity, particularly in controlled-environment agriculture. Despite recent advances, most studies still rely on destructive measurements or one-dimensional data, which limits insight into the structural–physiological coordination underlying LUE. We established a multimodal phenotyping platform to dissect the phenotypic regulatory network of LUE in lettuce ( Lactuca sativa L.). Integrating hyperspectral imaging with multiview three-dimensional (3D) reconstruction, we developed a noninvasive, high-throughput system that simultaneously estimates 3D plant architecture, photosynthetic physiology—net photosynthetic rate (A) and relative chlorophyll content (SPAD)—and aboveground biomass (AGB) across 35 cultivars. A modeling pipeline combining StandardScaler (SS) normalization, genetic algorithm (GA) feature selection, and artificial neural networks (ANN) achieved robust prediction of A (R²=0.72), SPAD (R²=0.87), and AGB (R²=0.85). Spectral contribution analysis revealed distinct sensitivities: SPAD across 400–700 nm, A near 430 and 680 nm, and AGB across 500–580 nm. The 426–430 nm blue band emerged as a key region: high-efficiency cultivars showed distinctive reflectance (42.93–59.03 %), consistent with superior photosynthetic performance. Structurally, high-efficiency types exhibited “large-and-loose” canopies, with greater plant height (+64.37 %), projected area (+59.42 %), and convex-hull volume (+166.3 %), alongside reduced compactness (−23.48 %). Network analysis indicated progressively tighter coupling between spectral and structural traits from low- to high-efficiency groups, consistent with adaptive coordination for light capture and use. These results identify actionable phenotypic markers for selecting high-LUE cultivars and provide a transferable platform for phenomics-driven breeding and management in controlled-environment crops. • A multimodal framework enables non-destructive, high-throughput phenotyping in lettuce. • 66 key spectral and structural features linked to light-use efficiency were identified. • A photosynthetic trait network reveals coordination of pigments and canopy architecture. • Breeding targets for blue-light response and canopy structure optimization are proposed.
Why it matches plant phenotyping methodsレタスの構造・生理形質を推定するマルチモーダル表現型プラットフォームを開発し、非破壊・高速測定と予測性能を評価しており、表現型取得手法が研究の中心である。
abstractWe established a multimodal phenotyping platform to dissect the phenotypic regulatory network of LUE in lettuce ( Lactuca sativa L.).
Unmanned aerial systems (UAS) are reliable tools for field phenotyping, enabling rapid, large-scale, and cost-effective data collection to support breeding programs. However, many UAS-based approaches rely on manual data processing, limiting scalability and efficiency. This study presents a fully automated pipeline for high-throughput phenotyping (HTP) of peanut crop architectural traits, including canopy height (CH), growth habit (GH), and mainstem prominence (MP) by integrating UAS imagery, a vision foundation model-Segment Anything Model (SAM), and convolutional neural networks (CNN). SAM auto-mask generator mode was used to identify field extent and orientation, while SAM interactive mode enabled individual plot segmentation using auto-generated point prompts. Terrain points automatically sampled near each plot were used to model the ground surface and compute the canopy height model, allowing CH estimations at the plot level. CH estimations showed strong agreement with manual measurements (R² = 0.78, RMSE = 3 cm, MAPE = 10 %). For MP and GH estimation, three pre-trained CNN models (AlexNet, ResNet18, and EfficientNet-B0) were evaluated, with AlexNet achieving the highest accuracy (89 % for GH, 83 % for MP). To assess the feasibility of using these HTP-derived estimations in plant breeding, quantitative trait loci (QTL) analysis was performed, identifying major-effect loci associated with these traits. The results were consistent with conventional QTL mapping methods, demonstrating that UAS-based phenotyping provides reliable trait data for genetic studies in peanut breeding. Overall, our deep learning-based data processing workflow minimizes manual efforts, providing an efficient and scalable approach that can accelerate genetic studies and trait selection in large-scale breeding programs.
Why it matches plant phenotyping methodsUAS画像、SAM、CNNを統合したピーナッツの草冠高・生育型・主茎優勢度の自動推定パイプラインを開発・検証しており、表現型取得と抽出手法が研究の中心である。
abstractThis study presents a fully automated pipeline for high-throughput phenotyping (HTP) of peanut crop architectural traits, including canopy height (CH), growth habit (GH), and mainstem prominence (MP) by integrating UAS imagery, a vision foundation model-Segment Anything Model (SAM), and convolutional neural networks (CNN).
Reproduction assets foundThe authors deposited the paper's phenotyping inputs (plot-level aerial RGB images and nDSM maps for GH/MP classification) publicly on Zenodo. The analysis source code is only available upon request, so it does not qualify as a public asset.Dataset · public0126 .
Contributor Information
Peggy Ozias-Akins, Email: pozias@uga.edu.
Changying Li, Email: cli2@ufl.edu.
Appendix A.
Supplementary data
The following is the supplementary data to this article:
Multimedia component 1
Multimedia component 1
Data availability
The datasets supporting this study are publicly available on Zenodo [ https://doi.org/10.5281/zenodo.17274012 ]. They include plot-level aerial RGB images and nDSM maps from peanut breeding fields for classification of Growth Habit and Mainstem Prominence. The source code used for data processing and analysis will be made available upon request.
References
1. U. S. Department of Agriculture . USDA National Agricultural Statistics ServiOpen asset ↗Zenodo · 10.5281/zenodo.17274012lines:327-356Plant phenotyping relevance match · UnverifiedCrossref · checked 6 Sept 2026
Unmanned aerial vehicles (UAVs) are transforming agriculture through enhanced data acquisition, improved monitoring efficiency, and support for data-driven decision-making. Complementing this, AI-driven platforms provide intuitive and reliable tools for advanced UAV analytics. However, their integration remains underexplored, particularly in specialty crops. Therefore, in this study, we evaluated the performance of an AI-driven web platform (Solvi) for automated plant counting and biometric trait estimation in two contrasting systems: pecan, a perennial nut crop, and onion, an annual vegetable. Ground-truth measurements included pecan tree number, tree height, and canopy area, as well as onion bulb number and diameter, the latter used for market class classification. Counting performance was assessed using precision, recall, and F1 score, while trait estimation was evaluated with linear regression analysis. UAV-based counts showed strong agreement with ground-truth data, achieving precision, recall, and F1 scores above 97% for both crops. For pecans, UAV-derived estimates of tree height (R2 = 0.98, error = 11.48%) and canopy area (R2 = 0.99, error = 23.16%) demonstrated high accuracy, while errors were larger in young trees compared with mature trees. For onions, UAV-derived bulb diameters achieved an R2 of 0.78 with a 6.29% error, and market class classification (medium, jumbo, colossal) was predicted with
Why it matches plant phenotyping methodsUAV画像とAIプラットフォームによる植物個体数・樹高・樹冠面積・球根径の自動推定を評価しており、表現型取得手法の性能評価が研究の中心です。
abstractwe evaluated the performance of an AI-driven web platform (Solvi) for automated plant counting and biometric trait estimation
This research explores how multispectral UAVs assist plant height monitoring and paddy field yield estimation by combining the aerial imagery with soil nitrogen data. The primary objective of this research is to develop accurate and affordable models for improving farming by linking plant health indicators. A secondary aim is to enhance farming by integrating plant health indicators from UAVs with soil nutrient levels. Multispectral UAV (Phantom 4), which provides five multispectral bands (Blue, Green, Red, Red Edge, Near-Infrared) and one RGB camera, was used to capture images during six stages of the crop growth to calculate vegetation indices like NDVI, GRVI for assessing crop health. Soil samples were taken from nine spots, and nitrogen levels were measured throughout the six growth stages. UAV photogrammetric technique was used to estimate plant height by comparing the Digital Surface Model (DSM) at different growth stages, which was then compared to field measurements. The collected data was used to develop models that predict crop yield by analysing the connection between soil nitrogen, Plant height and vegetation indices. The results obtained concluded the interrelationship between vegetation indices, nitrogen levels and yield, which demonstrated that UAV-based monitoring can accurately predict crop performance. This approach helps farmers to use fertiliser and make more accurate predictions, encouraging precise agriculture. This research emphasizes the significance of evolving technologies like UAVs, in offering valuable information to farmers, agronomists and policymakers for better crop management and data driven decision making.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と写真測量から植物高・植生指数を抽出し、圃場収量推定モデルを構築・地上測定と比較しており、植物表現型の取得・解析が中心的です。
abstractThis research explores how multispectral UAVs assist plant height monitoring and paddy field yield estimation
Introduction Plant type is an important part of plant phenotypic research, which is of great significance for practical applications such as plant genomics and cultivation knowledge modeling. The existing plant type judgment mainly relies on subjective experience, and lacks automatic analysis and identification methods, which seriously restricts the progress of efficient crop breeding and precision cultivation. Methods In this study, the digital structure model of cotton plant was constructed based on multi-dimensional vision, and the rapid analysis and identification method of cotton plant type was established. 50 cotton plants were used as experimental objects in this study. Firstly, multi-view images of cotton plants at boll opening stage were collected, and a three-dimensional point cloud model of cotton plants was constructed based on Structure From Motion and Multi View Stereo (SFM-MVS) algorithm. The original cotton point cloud data was preprocessed by coordinate correction, statistical filtering, conditional filtering and down-sampling to obtain a high-quality three-dimensional model. The three-dimensional model is projected in two dimensions to obtain the two-dimensional projection data of cotton plants from multiple perspectives. Secondly, based on the fast convex hull algorithm, the cotton plant two-dimensional convex hull was constructed from multiple perspectives, and the distribution range and corner change rate of each corners of the convex hull were analyzed, and the identification basis of cotton plant type was established. Results The R2 of plant height and width extracted from the model were greater than 0.90, and RMES were 0.372 cm and 0.387 cm, respectively. When the maximum number of point clouds is 75335, the point cloud reading time, cotton multi-view projection time, and convex hull automatic construction time are 0.402 S, 2.275 S, and 0.018 S, respectively. Finally, the cotton cylinder type classification interval is 0-0.2, and the tower type classification interval is 0.4-1.5. Discussion The cotton plant type identification method proposed in this study is fast and efficient. It provides a solid theoretical basis and technical support for cotton plant type identification.
Why it matches plant phenotyping methods多視点画像とSfM-MVS点群から綿花の草型および高さ・幅を自動抽出・識別する手法を開発し、精度と処理時間も評価しており、植物表現型取得が中心である。
abstractthe digital structure model of cotton plant was constructed based on multi-dimensional vision, and the rapid analysis and identification method of cotton plant type was established.
Crop canopy height is a key structural indicator that is strongly associated with crop development, biomass accumulation, and crop health. To overcome the limitations of time-consuming and labor-intensive traditional field measurements, Unmanned Aerial Vehicle (UAV)-based Light Detection and Ranging (LiDAR) offers an efficient alternative by capturing three-dimensional point cloud data (PCD). In this study, UAV-LiDAR data were acquired using a DJI Matrice 600 Pro equipped with a 16-channel LiDAR system. Three canopy height estimation methodological approaches were evaluated across three crop types: corn, soybean, and winter wheat. Specifically, this study assessed machine learning regression modeling, ground point classification techniques, percentile-based method and a newly proposed Dual-Range Averaging (DRA) method to identify the most effective method while ensuring practicality and reproducibility. The best-performing method for corn was Support Vector Regression (SVR) with a linear kernel (R2 = 0.95, RMSE = 0.137 m). For soybean, the DRA method yielded the highest accuracy (R2 = 0.93, RMSE = 0.032 m). For winter wheat, the PointCNN deep learning model demonstrated the best performance (R2 = 0.93, RMSE = 0.046 m). These results highlight the effectiveness of integrating UAV-LiDAR data with optimized processing methods for accurate and widely applicable crop height estimation in support of precision agriculture practices.
Why it matches plant phenotyping methodsUAV-LiDAR点群から作物群落高を推定する複数手法を比較・評価し、新規DRA法も提案しており、植物形質取得手法が研究の中心です。
abstractThree canopy height estimation methodological approaches were evaluated across three crop types: corn, soybean, and winter wheat.
Aiming to address the accuracy problem of cane tip recognition in complex natural environments, this paper proposes a cane tip feature annotation method based on the growth characteristics of sugarcane. In the context of the demand for lightweight and fast detection of cane tips, this paper optimizes the Yolov8n-Seg model with lightweight shared convolutional separated batch normalized detection head, model pruning, and knowledge distillation strategies. With these improvements, the accuracy of the optimized model increased by 0.2 percentage points, the number of parameters was reduced by 75.03%, the model size was reduced by 70.15%, the inference time is accelerated by 17.34%, and the GFLOPs were reduced by 40.00%. The lightweight cane tip detection model was deployed on the Jetson Orin NX platform with an average recognition frame rate of 7.42 f/s provides a lightweight hardware deployment solution for real-world applications in sugarcane harvesters. Finally, the depth camera was used for cane tip recognition and height measurement. The experimental results showed that the average relative errors of the camera were 0.189%, 0.675%, and 0.949% when the camera was 50 cm, 75 cm, and 100 cm away from the cane tip, respectively, which were all controlled within 1%, and were able to achieve accurate height measurement. Based on the statistical analysis of sugarcane clusters, this paper further proposes a sugarcane cluster identification method, providing a theoretical basis for saving adjustment time of the tip cutter during the harvesting process. It lays a theoretical and technical foundation for researching feature recognition, cutter height positioning, and real-time control of sugarcane harvester cuttings.
Why it matches plant phenotyping methodsサトウキビ先端の画像認識と深度カメラによる高さ測定を開発・検証しており、植物形態形質の取得が中心である。
abstractthis paper proposes a cane tip feature annotation method based on the growth characteristics of sugarcane.
Advanced phenotyping techniques are required in the breeding and management of maize, which is crucial for global food security. Traditional in situ three-dimensional (3D) field phenotyping entails labour-intensive data acquisition. Light detection and ranging technology offers high-resolution maize canopy point clouds under outdoor field conditions, establishing a technical foundation for automated phenotypic trait extraction. However, accurately segmenting individual plants from dense and structurally complex canopy point clouds for single-plant trait analysis is challenging. To address this challenge, we propose a novel framework named Paired-Attention Central Axis Aggregation Network (PACANet) for 3D point cloud-based plant segmentation. Firstly, a 3D paired-attention backbone network is introduced to enhance point-wise feature representations by integrating spatial and channel information, thereby enabling effective learning of high-dimensional point cloud features. Secondly, a projection-based central axis aggregation strategy is incorporated to guide instance separation by projecting plant point clouds onto their respective central axis skeletons, which improves the spatial coherence of segmentation. Additionally, a simulation-based point cloud generation approach is proposed to reduce reliance on large-scale manual annotations, facilitating model training in scenarios with limited real-world population data. Comprehensive experimental evaluations across multiple datasets demonstrate that PACANet consistently outperforms existing plant population segmentation methods. Notably, when trained solely on simulated data, PACANet achieves a state-of-the-art average precision of 0.9246. Finally, based on the segmentation results, phenotypic traits at both the individual plant and organ levels are analyzed under various planting densities, including the field-level distributions of plant height, plant width, leaf base angle and leaf inclination angle, all of which exhibit strong consistency with the validation data. These results highlight the potential of PACANet as a robust and scalable solution for high-throughput phenotyping in smart breeding and precision agriculture. This study provides a new tool for smart breeding and precision agriculture, and the source code and data are available at https://github.com/yangxin6/3D-PACA-Network.git.
Why it matches plant phenotyping methods3D点群による個体分割と形質抽出ネットワークを開発し、複数データセットで性能評価・検証しているため、植物フェノタイピング手法が中心である。
abstractwe propose a novel framework named Paired-Attention Central Axis Aggregation Network (PACANet) for 3D point cloud-based plant segmentation.
Yield and its components are the important traits for plant breeders to select the best genotypes in the breeding programs. However, traditional measurements of these traits across genotypes and environments are labor-intensive and time-consuming, as hundreds or even thousands of plots need to be estimated. A yield trial was carried out using seven sugarcane cultivars planted in a randomized complete block design with four replications for two ratoon crops to estimate sugarcane yield and its components using unoccupied aerial systems (UAS)-based high throughput phenotyping (HTP) and to compare the traditional method with UAS-based yield components in discriminating ability to assess sugarcane yield via a path coefficient analysis. UAS platforms mounted with sensors were flown over the trial. The result shows that UAS-derived plant height (PH) showed a strong relationship with the ground measured PH (R² = 0.89, RMSE = 0.15 m). Likewise, an accurate millable stalk height (MSH) estimation, using UAS-derived PH as a predictor, was observed (R² = 0.54, RMSE = 0.15 m). Canopy height model (CHM)-derived canopy cover (CC) appeared to be a promising feature to indirectly select or to predict for stalk number (SN) (R² = 0.69, RMSE = 10,975 stalks ha⁻¹). Based on a path coefficient analysis, UAS-based yield components performed equally to or slightly underperformed the traditional method. Traditionally, SN was the largest contributor to cane yield. Similarly, CC and CHM were the important components for UAS-based yield components. Additionally, the yield prediction model using UAS-derived canopy features with five cross validation schemes (CVs) revealed that model accuracy increased as association between predictor variables with a responding variable increased. The present study shows that random forest outperformed (higher r and lower RMSE) the linear regression models (stepwise, lasso, and ridge) in all CVs. The linear regressions were off when they were used to predict the performance of cultivars in untested crop/environments (CVs2 and CVs5), while a higher accuracy was observed when using random forest in those CVs. More importantly, the accuracy of all models reduced when they were tested in untested crop/environments (CVs2 and CVs5), indicating the challenge of using a prediction model applied to new environments.
Why it matches plant phenotyping methodsUASベースHTPでサトウキビの草高・群落被覆・茎数・収量構成要素を推定し、地上測定との検証、モデル比較、交差検証を行っており、表現型取得・推定手法が研究の中心である。
abstractUAS-derived plant height (PH) showed a strong relationship with the ground measured PH (R² = 0.89, RMSE = 0.15 m).
Accurate and efficient crop height information retrieval is crucial for applications such as farmland management, growth monitoring, yield estimation, and pest monitoring. Polarimetric Synthetic Aperture Radar (PolSAR) is known for its high sensitivity to the shape, structure, and dielectric constant of vegetation, presenting great potential for crop height retrieval. In this study, we compare the performance of three machine learning algorithms, Random Forest Regression (RFR), Bagging Decision Tree (BAGTREE), and Extreme Gradient Boosting (XGBoost), in the retrieval of crop height from PolSAR data. Using a comprehensive approach, we constructed a set of 32 polarimetric features as the initial input for the model. Subsequently, feature selection is employed to generate a subset aimed at reducing redundancy and improving the final estimation accuracy. Multi-temporal C-band PolSAR RADARSAT-2 data collected over three distinct agricultural types (corn, wheat, and soybean) in Canada are chosen for this study. The results indicate that the optimal average Root Mean Square Error (RMSE) for height retrieval in corn, wheat, and soybean throughout their growth cycles is 43.69 cm, 10.78 cm, and 20.92 cm, respectively. Among the three algorithms, RFR consistently demonstrates stable retrieval performance, and the polarimetric decomposition parameters exhibit the highest sensitivity to crop height. This study offers a valuable technical reference for SAR-based crop height retrieval and remote sensing-based crop growth monitoring without interferometry.
Why it matches plant phenotyping methodsPolSARセンサーデータと機械学習により作物高を推定し、複数アルゴリズムを比較・検証することが研究の中心であるため、植物形質計測手法として含める。
titleCrop height retrieval from polarimetric SAR data using machine learning: A comparative and validation study
Continuous information on soil water content (SWC) and plant development is crucial for environmental monitoring, agricultural management, and beyond. Cosmic-ray neutron sensors (CRNS), widely used to estimate SWC, also have the potential to monitor field-scale variations in vegetation properties. In this study, a CRNS measured both epithermal (EN) and thermal (TN) neutron intensities over a 10-year period at an ICOS Class 1 ecosystem station in Selhausen (Germany). Compared to nearby point-scale sensors, the CRNS provided more representative SWC estimates within the monitoring area of the adjacent eddy covariance (EC) station. A general co-development was observed between TN and gross primary productivity (GPP), but differences during senescence and desiccation suggest that factors beyond plant water content can influence TN. An extensive dataset of plant height (PH), leaf area index (LAI), and dry above-ground biomass (AGB) was used to evaluate the ability of TN to monitor plant development. TN was found to be more closely related to vegetation dynamics than to changes in SWC. CRNS estimations of PH, LAI, and AGB yielded relatively good agreement with reference data (RMSE of 0.13 m, 1.01 m²/m², and 0.27 kg/m², respectively). The RMSE obtained with a leave-one-out cross validation generally confirmed these findings. Although CRNS estimates generally had lower accuracy than traditional methods, they have the key advantages of being continuous, non-invasive, and non-laborious. Combined with simultaneous estimation of SWC at a relevant spatial scale, CRNS becomes a particularly interesting tool among long-term monitoring platforms with further potential in modelling, remote sensing, and decision-making in agriculture.
Why it matches plant phenotyping methodsCRNSを用いて植物高、LAI、地上部乾物量を推定し、基準データおよび交差検証で性能評価しており、植物形質取得法が研究の中心です。
abstractAn extensive dataset of plant height (PH), leaf area index (LAI), and dry above-ground biomass (AGB) was used to evaluate the ability of TN to monitor plant development.
Introduction: Accurate 3D reconstruction is essential for plant phenotyping. However, point clouds generated directly by binocular cameras using single-shot mode often suffer from distortion, while self-occlusion among plant organs complicates complete data acquisition. Methods: To address these challenges, this study proposes and validates an integrated, two-phase plant 3D reconstruction workflow. In the first phase, we bypass the integrated depth estimation module on camera and instead apply Structure from Motion (SfM) and Multi-View Stereo (MVS) techniques to the captured high-resolution images. It produces high-fidelity, single-view point clouds, effectively avoiding distortion and drift. In the second phase, to overcome self-occlusion, we register point clouds from six viewpoints into a complete plant model. This process involves a rapid coarse alignment using a marker-based Self-Registration (SR) method, followed by fine alignment with the Iterative Closest Point (ICP) algorithm. Results: The workflow was validated on two Ilex species (Ilex verticillata and Ilex salicina). The results demonstrate the high accuracy and reliability of the workflow. Furthermore, key phenotypic parameters extracted from the models show a strong correlation with manual measurements, with coefficients of determination (R²) exceeding 0.92 for plant height and crown width, and ranging from 0.72 to 0.89 for leaf parameters. Discussion: These findings validate our workflow as an accurate, reliable, and accessible tool for quantitative 3D plant phenotyping.
Why it matches plant phenotyping methods植物の3D再構成と形質抽出ワークフロー自体を開発・検証しており、植物形質計測が中心的な方法論的貢献である。
abstractthis study proposes and validates an integrated, two-phase plant 3D reconstruction workflow
Three-dimensional(3D) seedling reconstruction technology can provide critical technical support for monitoring plant growth, phenotyping high-throughput plants, and conducting precision agriculture. However, multiview image-based reconstruction methods, which rely on image registration and feature matching, are susceptible to issues such as similar textures and viewpoint differences, leading to matching errors and the loss of key structural information. This can result in local deficiencies and reduced accuracy in the reconstructed models. Therefore, to attain improved reconstruction accuracy under low-cost constraints, deep learning-based feature extraction and matching methods are employed in this study, the SuperPoint network is utilized to increase the robustness of the feature point detection and description processes, and the LightGlue algorithm is introduced to improve the accuracy and stability of matching. Additionally, to reduce the impact of shooting and platform jitter on image quality, a dedicated plant 3D reconstruction platform is designed and constructed, and a dataset of densely planted rice seedlings under light stress conditions is collected, comprising three factors (light quality, light quantity, and the photoperiod) × three levels, totaling nine groups. Experimental results show that the proposed method achieves optimal performance in terms of its point cloud completeness and reprojection error. The phenotypic parameters (e.g., plant height) extracted from the reconstruction data are strongly correlated with the actual measurements (R 2 = 0.989, RMSE = 4.54 mm), validating the potential of the proposed method for applications related to simulating plant growth processes, analyzing the effects of environmental factors (e.g., light), and optimizing crop cultivation schemes.
Why it matches plant phenotyping methodsマルチビュー画像によるイネ幼苗の3D再構成プラットフォームとデータセットを開発し、再構成精度および抽出形質を実測値と検証しており、表現型取得手法が研究の中心である。
abstractdeep learning-based feature extraction and matching methods are employed in this study
Reproduction assets foundThe paper's authors explicitly state their analysis code is publicly available on GitHub; the phenotype/image dataset is only available upon request, so it does not qualify as a public asset.Code · publicThe code used in this study is available at https://github.com/Terrywewee/3D-reconstruction-of-densely-planted-rice-seedlings---superpoint-lightglue.git .Open asset ↗https://github.com/Terrywewee/3D-reconstruction-of-densely-planted-rice-seedlings---superpoint-lightglue.gitlines:433-485Plant phenotyping relevance match · UnverifiedEurope PMC · OpenAlex · checked 15 Sept 2026
Although LiDAR is widely used for tree measurement, its high cost and operational complexity remain significant barriers to widespread adoption. With advances in photogrammetry and deep learning, efficient and accurate alternatives have become increasingly important for forest resource surveys. Accordingly, we propose an automated trunk-parameter measurement framework that maps image pixels to physical units. The framework integrates the SegFormer deep-learning model, trunk-skeleton extraction, an adaptive curvature-segmentation algorithm, and segment-wise 3-D reconstruction, thereby enabling image segmentation, curvature analysis, three-dimensional reconstruction, and measurement. To validate its practical value, we collected images of 3013 trees across four species in the Beijing region. Additionally, we acquired point-cloud data and conducted destructive measurements on 141 trees of various species for comparative evaluation. Experimental results indicate that the stem segmentation algorithm effectively extracts trunk regions in images, and the adaptive segmentation method substantially improves trunk volume estimation accuracy. The approach achieves only 2.01 %-7.68 % error in single-tree volume and height measurements-primarily due to segment-height inaccuracies-and offers an approximately 6.9-fold improvement in efficiency compared with the existing HMLS method. In summary, this method provides an efficient, low-cost solution for forestry surveys and shows great potential for monitoring tasks that require high accuracy under resource constraints. This innovative method is expected to further advance forest resource assessment.
Why it matches plant phenotyping methods画像から樹幹を抽出し、3D再構成によって単木の体積・高さ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す。
abstractwe propose an automated trunk-parameter measurement framework that maps image pixels to physical units.
Accurate non-destructive measurement of phenotypic data for leafy vegetable is critical for effective management of breeding, growth monitoring, and yield estimation. However, the scarcity of large-scale stereo matching datasets in agricultural scenarios and the high cost of depth-sensing devices tailored for small targets pose significant challenges to achieving accurate phenotyping of crops in complex growing environments. This study introduces a novel 3D Gaussian Splatting (3DGS)-supervised stereo matching network to enable accurate depth estimation of leafy vegetable. We enhance Gaussian rendering techniques to generate stereo training data from multi-view images. A multi-scale feature extraction network with deformable convolutions is proposed to adaptively capture diverse features, ranging from low-level details (edges and textures) to structural and semantic information. To address the multi-peak characteristics of disparity in leafy vegetable, a top- k algorithm is employed for disparity regression, significantly improving disparity estimation accuracy. To enhance the adaptability of cost aggregation, we construct a combined cost volume through a concatenation and group-wise correlations and leverage feature attention blocks to refine disparity prediction. Additionally, depthwise separable convolutions and optimized methods of the number of iterative blocks for leafy vegetable small targets are introduced to reduce computational complexity and accelerate training and inference. Experimental results demonstrate the effectiveness of our depth estimation method, achieving an Endpoint Error (EPE) of 2.32 pixels overall and 0.91 pixels specifically in leafy vegetable area compared to disparity ground truth. Furthermore, we developed a 3D point cloud phenotyping measurement method using instance segmentation and 3D reconstruction. Notably, the measured height, number of leaves, and surface area achieve R 2 values of 0.9946, 0.9577, and 0.9775 against manual measurements, respectively. These high-precision results underscore the practical applicability of our method and provide a theoretical foundation for deployment in agricultural production systems.
Why it matches plant phenotyping methods葉菜の深度推定と3D点群による形質測定法を開発し、手動測定との比較で高さ・葉数・表面積を検証しており、フェノタイピング手法が研究の中心である。
abstractThis study introduces a novel 3D Gaussian Splatting (3DGS)-supervised stereo matching network to enable accurate depth estimation of leafy vegetable.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
Aiming to address the issues of low efficiency and large errors in the manual measurement process of phenotypic parameters in Schima Superba seedlings, an automated non-destructive method for acquiring phenotypic parameters based on three-dimensional point clouds is proposed, which includes the main steps of alignment, skeleton extraction, and automatic phenotypic calculation. Aiming to overcome the technical challenges of stem and leaf separation in Schima Superba , a density-weighted voxel centroid method is proposed to extract skeleton points, combined with minimum spanning tree (MST) and principal component analysis (PCA) techniques to accurately identify the stem skeleton point cloud, effectively addressing the problem of stem-leaf separation. The separation process encounters difficulties at the stem-leaf junction, resulting in suboptimal separation accuracy. An improved K-means++ algorithm is proposed to initially estimate the number of adhering leaves based on coarse segmentation, followed by fine segmentation to achieve higher precision in leaf segmentation, effectively improving the accuracy and efficiency of the segmentation process. Following the completion of stem and leaf segmentation, a fully automated phenotypic characterization method based on the segmented point cloud is proposed for the first time. The method automatically outputs relevant phenotypic parameters, including plant height, stem length, stem diameter, and leaf area. The predicted correlation coefficients for the experimental phenotypes were 0.994, 0.992, 0.938, and 0.873, meeting the requirements for on-site measurement of phenotypic parameters in Schima Superba and providing strong technical support for plantation management and cultivar improvement.
Why it matches plant phenotyping methods3D点群による茎葉分離、骨格抽出、形質自動計算を開発・検証しており、植物表現型取得手法が研究の中心である。
abstractan automated non-destructive method for acquiring phenotypic parameters based on three-dimensional point clouds is proposed
Mediterranean ecosystems have been overlooked for climate mitigation due to their relatively low biomass and carbon stocks, although trees in these regions offer important ecosystem services. Under a fast-changing climate, trees in the Mediterranean are particularly vulnerable to droughts and fires. However, the impacts of these extreme events remain difficult to quantify and monitor on a regular basis both because of the lack of systematic and accurate forest inventories and because many trees growing outside forests are not accounted for. In this study, conducted over Cyprus, an extended dataset of field height and diameter measurements and very high-resolution remote sensing photogrammetric and LiDAR images segmented for crown area and height are combined to quantify individual tree characteristics. These variables are then processed to quantify biomass for each individual tree by deriving locally calibrated allometric equations. Local allometric equations for dominant conifer tree species (Pinus Brutia and Pinus Nigra) are calibrated based on a large collection of tree morphology data. These equations are compared against previously reported allometric models used for the same species in other Eastern Mediterranean regions. Our allometric models achieved an accuracy of up to 98%, paving the way for a tree-level biomass and carbon inventory at the scale of the entire country of Cyprus based on wall-to-wall crown area images.
Why it matches plant phenotyping methods個体樹木の形態を高解像度画像から抽出し、局所校正したアロメトリ式でバイオマスを推定する手法の開発・比較・検証が中心であり、植物個体の明示的形質測定に該当する。
abstractvery high-resolution remote sensing photogrammetric and LiDAR images segmented for crown area and height are combined to quantify individual tree characteristics.
Potato above-ground biomass (AGB) and tuber yield estimation remain challenging due to the subjectivity of farmer-based assessments, the high data requirements of spectral analysis methods, and the sensitivity of traditional Structure from Motion (SfM) techniques to soil elevation variability. To address these challenges, this study proposes a novel UAV-based visible-light remote sensing framework to estimate the AGB and predict the tuber yield of potato crops. First, a new vegetation index, the Green-Red Combination Vegetation Index (GRCVI), was developed to improve the separability between vegetation and non-vegetation pixels. Second, an improved single-period SfM method was designed to mitigate errors in canopy height estimation caused by terrain variations. Fractional vegetation coverage (FVC) and plant height (PH) derived from UAV imagery were then integrated into a feedforward neural network (FNN) to predict AGB. Finally, potato tuber yield was predicted using polynomial regression based on AGB. Results showed that GRCVI combined with the numerical intersection method and SVM classification achieved FVC extraction accuracy exceeding 95%. The improved SfM method yielded canopy height estimates with R2 values ranging from 0.8470 to 0.8554 and RMSE values below 2.3 cm. The AGB estimation model achieved an R2 of 0.8341 and an RMSE of 19.9 g, while the yield prediction model obtained an R2 of 0.7919 and an RMSE of 47.0 g. This study demonstrates the potential of UAV-based visible-light imagery for cost-effective, non-destructive, and scalable monitoring of potato growth and yield, providing methodological support for precision agriculture and high-throughput phenotyping.
Why it matches plant phenotyping methodsUAV画像からFVC・草丈・地上部バイオマス・収量を推定する画像解析およびSfM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。
abstractthis study proposes a novel UAV-based visible-light remote sensing framework to estimate the AGB and predict the tuber yield of potato crops.
Efficient and non-destructive extraction of organ-level phenotypic parameters of sesame ( Sesamum indicum L.) plants is a key bottleneck in current sesame phenotyping research. To address this issue, this study proposes a method for organ segmentation and phenotypic parameter extraction based on CAVF-PointNet++ and geometric clustering. First, this method constructs a high-precision 3D point cloud using multi-view RGB image sequences. Based on the PointNet++ model, a CAVF-PointNet++ model is designed to perform feature learning on point cloud data and realize the automatic segmentation of stems, petioles, and leaves. Meanwhile, different leaves are segmented using curvature-density clustering technology. Based on the results of segmentation, this study extracted a total of six organ-level phenotypic parameters, including plant height, stem diameter, leaf length, leaf width, leaf angle, and leaf area. The experimental results show that in the segmentation tasks of stems, petioles, and leaves, the overall accuracy of CAVF-PointNet++ reaches 96.93%, and the mean intersection over union is 82.56%, which are 1.72% and 3.64% higher than those of PointNet++, demonstrating excellent segmentation performance. Compared with the results of manual segmentation of different leaves, the proposed clustering method achieves high levels in terms of precision, recall, and F1-score, and the segmentation results are highly consistent. In terms of phenotypic parameter measurement, the coefficients of determination between manual measurement values and algorithmic measurement values are 0.984, 0.926, 0.962, 0.942, 0.914, and 0.984 in sequence, with root-mean-square errors of 5.9 cm, 1.24 mm, 1.9 cm, 1.2 cm, 3.5°, and 6.22 cm 2 , respectively. The measurement results of the proposed method show a strong correlation with the actual values, providing strong technical support for sesame phenotyping research and precision agriculture. It is expected to provide reference and support for the automated 3D phenotypic analysis of other crops in the future.
Why it matches plant phenotyping methods3D画像・点群分割と幾何クラスタリングにより、ゴマの器官分割および6種類の表現型形質抽出法を開発し、手動測定との精度検証も行っているため、方法が中心的である。
abstractthis study proposes a method for organ segmentation and phenotypic parameter extraction based on CAVF-PointNet++ and geometric clustering.
Abstract Modern agriculture faces the dual challenge of sustainably increasing food production while mitigating the environmental impact of intensive monocultures. Mixed cropping, which is the cultivation of multiple species or varieties, may provide ecological benefits that address productivity and environmental sustainability challenges. However, evaluating its multifunctionality in conventional agricultural field experiments is costly and labour-intensive, and small sample sizes and high spatial variability often make it difficult to detect the statistical significance of mixed cropping effects. This study aims to introduce and validate a high-throughput field phenotyping (HTP) framework that integrates aerial imagery obtained from unmanned aerial vehicles (UAVs) to efficiently assess the multifunctionality of mixed cropping systems. We conducted a field experiment comparing monocultures of oat, rye, and barley; intraspecific mixed cropping combining three oat varieties; and interspecific mixed cropping combining oat, rye, and barley. Using UAV-derived data across the entire field, including vegetation cover, plant height, and the normalised difference vegetation index, we evaluated five multifunctionalities (biomass production, spatial variability in biomass production, early canopy closure, lodging resistance, and lodging resilience). This framework reveals that mixed cropping outperforms monocropping in several key ecological functions. The proposed UAV-based HTP approach enables cost-effective, robust, and scalable evaluation of mixed cropping systems, facilitating their optimisation for multifunctionality and contributing to the advancement of sustainable agriculture.
Why it matches plant phenotyping methodsUAV画像から植被率・草高・NDVIなどの植物形質を取得する高スループット表現型解析フレームワークを導入・検証しており、フェノタイピング手法が研究の中心です。
abstractThis study aims to introduce and validate a high-throughput field phenotyping (HTP) framework that integrates aerial imagery obtained from unmanned aerial vehicles (UAVs)
Reproduction assets foundThe preprint's data availability statement deposits the datasets generated and analysed in the study (UAV-derived phenotypic measurements and field data) on Zenodo with a DOI that appears verbatim in the allowed URL list. No author analysis code or trained models are explicitly deposited.Dataset · publicThe datasets generated and analysed during the current study are available in the Zenodo repository, https://doi.org/10.5281/zenodo.17042273.Open asset ↗Zenodo · 10.5281/zenodo.17042273lines:135-161Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 6 Sept 2026
Introduction: With the advancement of imaging technologies, the efficiency of acquiring plant phenotypic information has significantly improved. The integration of deep learning has further enhanced the automatic recognition of plant structures and the accuracy of phenotypic parameter extraction. To enable efficient monitoring of tomato water stress, this study developed a deep learning-based framework for phenotypic trait extraction and parameter computation, applied to tomato images collected under varying water stress conditions. Methods: Based on the You Only Look Once version 11 nano (YOLOv11n) object detection model, adaptive kernel convolution (AKConv) was integrated into the backbone's C3 module with kernel size 2 convolution (C3k2), and a recalibration feature pyramid detection head based on the P2 layer was designed. Results and discussion: Results showed that the improved model achieved a 4.1% increase in recall, a 2.7% increase in mAP50, and a 5.4% increase in mAP50-95 for tomato phenotype recognition. Using the bounding box information extracted by the model, key phenotype parameters were further calculated through geometric analysis. The average relative error for plant height was 6.9%, and the error in petiole count was 10.12%, indicating good applicability and accuracy for non-destructive crop phenotype analysis. Based on these extracted traits, multiple sets of weighted combinations were constructed as input features for classification. Seven classification algorithms-Logistic Regression, Support Vector Machine, Random Forest, Decision Tree, K-Nearest Neighbors, Naive Bayes, and Gradient Boosting-were used to differentiate tomato plants under different water stress conditions. The results showed that Random Forest consistently performed the best across all combinations, with the highest classification accuracy reaching 98%. This integrated approach provides a novel approach and technical support for the early identification of water stress and the advancement of precision irrigation.
Why it matches plant phenotyping methodsトマト画像から植物高や葉柄数などの表現型形質を抽出・計算する深層学習フレームワークを開発し、精度評価も行っており、表現型取得手法が研究の中心である。
abstractthis study developed a deep learning-based framework for phenotypic trait extraction and parameter computation
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
Accurate acquisition of tobacco phenotypic traits is crucial for growth monitoring, cultivar selection, and other scientific management practices. Traditional manual measurements are time-consuming and labor-intensive, making them unsuitable for large-scale, high-throughput field phenotyping. The integration of 3D reconstruction and stem-leaf segmentation techniques offers an effective approach for crop phenotypic data acquisition. In this study, we propose a tobacco phenotyping method that combines unmanned aerial vehicle (UAV) remote sensing with an improved PointNet++ model. First, a 3D point-cloud dataset of field-grown tobacco plants was generated using multi-view UAV imagery. Next, the PointNet++ architecture was enhanced by incorporating a Local Spatial Encoding (LSE) module and a Density-Aware Pooling (DAP) module to improve the accuracy of stem and leaf segmentation. Finally, based on the segmentation results, an automated pipeline was developed to compute key phenotypic traits, including plant height, leaf length, leaf width, leaf number, and internode length. Experimental results demonstrated that the improved PointNet++ model achieved an overall accuracy (OA) of 95.25% and a mean intersection over union (mIoU) of 93.97% for tobacco plant segmentation-improvements of 5.12% and 5.55%, respectively, over the original PointNet++ model. Moreover, using the segmentation results from the improved PointNet++ model, the predicted phenotypic values exhibited strong agreement with ground-truth measurements, with coefficients of determination (R²) ranging from 0.86 to 0.95 and root mean square errors (RMSE) between 0.31 and 2.27 cm. This study provides a technical foundation for high-throughput phenotyping of tobacco and presents a transferable framework for phenotypic analysis in other crops.
Why it matches plant phenotyping methodsUAV 3D点群、改良PointNet++による茎葉分割と形質推定パイプラインが研究の中心であり、複数のタバコ形質を自動取得・検証している。
abstractIn this study, we propose a tobacco phenotyping method that combines unmanned aerial vehicle (UAV) remote sensing with an improved PointNet++ model.
In situ detection of growth information in greenhouse crops is crucial for germplasm resource optimization and intelligent greenhouse management. To address the limitations of poor flexibility and low automation in traditional phenotyping platforms, this study developed a controlled environment inspection robot. By means of a SCARA robotic arm equipped with an information acquisition device consisting of an RGB camera, a depth camera, and an infrared thermal imager, high-throughput and in situ acquisition of lettuce phenotypic information can be achieved. Through semantic segmentation and point cloud reconstruction, 12 phenotypic parameters, such as lettuce plant height and crown width, were extracted from the acquired images as inputs for three machine learning models to predict fresh weight. By analyzing the training results, a Backpropagation Neural Network (BPNN) with an added feature dimension-increasing module (DE-BP) was proposed, achieving improved prediction accuracy. The R2 values for plant height, crown width, and fresh weight predictions were 0.85, 0.93, and 0.84, respectively, with RMSE values of 7 mm, 6 mm, and 8 g, respectively. This study achieved in situ, high-throughput acquisition of lettuce phenotypic information under controlled environmental conditions, providing a lightweight solution for crop phenotypic information analysis algorithms tailored for inspection tasks.
Why it matches plant phenotyping methods温室内ロボット、複数センサー、画像解析、形質抽出、重量推定を一体化した植物表現型取得手法の開発が中心である。
abstractthis study developed a controlled environment inspection robot
Why it matches plant phenotyping methodsスマートフォンLiDAR・写真測量による樹体3D計測法を開発・精度検証し、樹高・幹径・葉面積という植物形態形質へ適用しており、フェノタイピング手法が中心である。
abstractThis study investigates the performances of a low-cost, consumer-grade device-the iPhone 13 Pro equipped with an integrated LiDAR sensor and RGB camera-for 3D scanning of fruit tree structures.
Woody canopies regulate exchanges of energy, water and carbon, and their three-dimensional (3D) structure supports much of terrestrial biodiversity. Remote sensing technologies such as airborne laser scanning (ALS) now enable the 3D mapping of entire landscapes. However, we lack the large, harmonized and geographically representative ALS collections needed to build a global picture of woody ecosystem structure. To address this challenge, we developed the Global Canopy Atlas (GCA): 3,458 ALS acquisitions transformed into standardized and analysis-ready maps of canopy height and elevation at 1 m2 resolution. The GCA covers 56,554 km2 across all major biomes. 19% of this area has been scanned multiple times, and 87% of all GCA products are openly available, covering 95% of the total area. To showcase its wide range of applications, we applied the GCA in three case studies. First, we validated three global satellite-derived canopy height maps, finding poor performance at native resolution (1-30 m, R2 < 0.38) and moderate performance at 250 m resolution (R2 < 0.65). Second, analyzing global patterns in canopy gap size frequency we discovered an unexpectedly large variation of power law exponents from branch to stand level ( = 1.52 to 2.38), pointing to a fundamental scale-dependence of forest structure. Third, we developed a framework to standardize forest turnover quantification from multi-source, multi-temporal ALS. In a temperate forest in North America it revealed that 21% of canopy gaps closed within 12 years of opening and would thus be missed by infrequent monitoring. As demonstrated by these case studies, the GCA provides a novel data source for ecologists, foresters, remote sensing scientists and the ecosystem modelling community that substantially advances our ability to understand the structure and dynamics of woody ecosystems at global scales.
Why it matches plant phenotyping methodsALSから樹冠高・標高などの植物群落構造形質を標準化して提供する大規模データ基盤を開発し、既存マップの検証や森林構造解析にも用いており、フェノタイピング手法・データ基盤が中心である。
abstractwe developed the Global Canopy Atlas (GCA): 3,458 ALS acquisitions transformed into standardized and analysis-ready maps of canopy height and elevation at 1 m2 resolution.
A crucial initial step for the automatic extraction of plant traits from imagery is the segmentation of individual plants. This is typically performed using supervised deep learning (DL) models, which require the creation of an annotated dataset for training, a time-consuming and labor-intensive process. In addition, the models are often only applicable to the conditions represented in the training data. In this study, we propose a pipeline for the automatic extraction of plant traits from high-resolution unmanned aerial vehicle (UAV)-based RGB imagery, applying Segment Anything Model 2.1 (SAM 2.1) for label-free segmentation. To prevent the segmentation of irrelevant objects such as soil or weeds, the model is guided using point prompts, which correspond to local maxima in the canopy height model (CHM). The pipeline was used to measure the crown diameter of approximately 15000 ball-shaped chrysanthemums (Chrysanthemum morifolium (Ramat)) in a 6158 m2 field on two dates. Nearly all plants were successfully segmented, resulting in a recall of 96.86%, a precision of 99.96%, and an F1 score of 98.38%. The estimated diameters showed strong agreement with manual measurements. The results demonstrate the potential of the proposed pipeline for accurate plant trait extraction across varying field conditions without the need for model training or data annotation.
Why it matches plant phenotyping methodsUAV画像から個体分割と植物形質（冠径）を自動抽出する手法の開発・評価が研究の中心であり、精度指標と手動測定との一致も検証している。
abstractwe propose a pipeline for the automatic extraction of plant traits from high-resolution unmanned aerial vehicle (UAV)-based RGB imagery
The precise segmentation of crop organs plays a crucial role in optimizing crop cultivation strategies and enhancing yield potential. This study proposes a novel deep learning network, CotSegNet, which enables precise and non-destructive segmentation of cotton organs facilitating the extraction of phenotypic characteristics. In CotSegNet, an improved attention mechanism known as CGLUConvFormer is designed. This mechanism significantly improves segmentation accuracy by emphasizing important features while diminishing redundant information. Furthermore, CotSegNet integrates the SegNext attention mechanism. This mechanism facilitates the efficient extraction and integration of multi-scale features, thereby significantly enhancing the ability of CotSegNet to comprehend and segment point cloud data. To address issues related to leaf adhesion and coplanarity that lead to over-segmentation problems, this study proposes an improved region-growing algorithm. This algorithm enhances the accuracy of leaf instance segmentation through the incorporation of distance constraints. In comparative experiments with five advanced deep learning networks (PointNet, PointNet++, DGCNN, SPoTr and CurveNet), CotSegNet demonstrated outstanding performance. Its Precision, Recall, F1-score, and IoU reached 95.06 %, 93.32 %, 94.61 %, and 89.80 %, respectively. The experimental results demonstrated that the proposed method effectively extracted the phenotypic parameters of stem height, leaf length, leaf width, and leaf area in cotton plants. These measurements exhibited a high degree of consistency with manual assessments, yielding determination coefficients of 0.947, 0.948, 0.955, and 0.961 for each parameter respectively. The corresponding root mean square errors were recorded as 0.852 cm, 0.492 cm, 0.551 cm, and 1.674 cm² respectively. The research findings demonstrate that this approach offers essential technical support for the collection and analysis of high throughput phenotyping data in field crops.
Why it matches plant phenotyping methods綿花器官の点群セグメンテーションと表現型形質抽出のためのCotSegNetおよび改良領域成長法を開発し、手動測定との検証も行っており、表現型取得が研究の中心である。
abstractThis study proposes a novel deep learning network, CotSegNet, which enables precise and non-destructive segmentation of cotton organs facilitating the extraction of phenotypic characteristics.
Plant height (PH) of oilseed rape, as a crucial phenotypic indicator, provides essential data for seedling diagnosis and breeding selection when accurately monitored throughout the life cycle of individual plants. However, it is a challenge to obtain precise PH measurements as rapeseed leaves and other crops shade each other after flowering. In this study, a rail-based platform equipped with LiDAR and the BeiDou differential positioning system was designed and manufactured to autonomously collect time-series point cloud of oilseed rape populations in the field. The point cloud data of oilseed rape during the regreening stage was segmented using an improved fast Euclidean clustering algorithm, followed by extraction of the root collar region via an objective function. Centered on the identified root collar region, an adaptive plant envelope area (PEA) was generated based on the distance between adjacent plants to isolate individual rapeseed specimens. Within the PEA corresponding to each plant’s root collar region, the ground position during sowing and the canopy apex at distinct growth stages were precisely localized, enabling automated extraction of individual PH across the full life cycle. The coefficient of determination (R²) between the algorithm and the manual measurement results at 140, 150 and 165 days after sowing were 0.9742, 0.9667, and 0.9208, respectively. And Root Mean Square Error (RMSE) between the algorithm and the manual measurement results at 140, 150 and 165 days were 0.038, 0.043 and 0.061 m, respectively. These results confirm that integrating BeiDou positioning with 3D point cloud processing achieves high-precision phenotyping of crop height dynamics. Furthermore, PHs were applied to frost damage and lodging susceptibility analysis, which indicate that the growth rate of rapeseed slows down as the severity of frost damage increases, and plants that reach a height of approximately 1 m during the flowering stage are prone to lodging after rainfall. These results have the potential to provide guidance for frost damage assessment and variety selection in smart agriculture applications.
Why it matches plant phenotyping methodsLiDAR・BeiDou搭載プラットフォームと3D点群処理により、個体の生育全期間の草丈を自動抽出する手法を開発し、手測定と検証しているため、植物フェノタイピング手法が中心である。
abstracta rail-based platform equipped with LiDAR and the BeiDou differential positioning system was designed and manufactured to autonomously collect time-series point cloud of oilseed rape populations in the field.
This study proposes an Automatic Branch Modeling (ABM) framework that combines AdTree and AdQSM algorithms to reconstruct individual tree models and estimate timber volume from fused Hand-held Laser Scanners (HLS) and Unmanned Aerial Vehicle Laser Scanners (UAV-LS) point cloud data. The research focuses on two 50 × 50 m primary tropical rainforest plots in Hainan Island, China, characterized by dense and vertically stratified vegetation. Key steps include multi-source point cloud registration and noise removal, individual tree segmentation using the Comparative Shortest Path (CSP) algorithm, extraction of diameter at breast height (DBH) and tree height, and 3D reconstruction and volume estimation via cylindrical fitting and convex polyhedron decomposition. Results demonstrate high accuracy in parameter extraction, with DBH estimation achieving R2 = 0.89–0.90, RMSE = 2.93–3.95 cm and RMSE% = 13.95–14.75%, while tree height estimation yielded R2 = 0.89–0.94, RMSE = 1.26–1.81 m and RMSE% = 9.41–13.2%. Timber volume estimates showed strong agreement with binary volume models (R2 = 0.90–0.94, RMSE = 0.10–0.18 m3, RMSE% = 32.33–34.65%), validated by concordance correlation coefficients (CCC) of 0.95–0.97. The fusion of HLS (ground-level trunk details) and UAV-LS (canopy structure) data significantly improved structural completeness, overcoming occlusion challenges in dense forests. This study highlights the efficacy of multi-source LiDAR fusion and 3D modeling for precise forest inventory in complex ecosystems. The ABM framework provides a scalable, non-destructive alternative to traditional methods, supporting carbon stock assessment and sustainable forest management in tropical rainforests. Future work should refine individual tree segmentation and wood-leaf separation to further enhance accuracy in heterogeneous environments.
Why it matches plant phenotyping methodsマルチソースLiDAR融合、個体樹木セグメンテーション、3D再構成によってDBH・樹高・材積を抽出する手法を開発し、精度検証まで行っており、植物形質計測が研究の中心である。
abstractThis study proposes an Automatic Branch Modeling (ABM) framework that combines AdTree and AdQSM algorithms to reconstruct individual tree models and estimate timber volume from fused Hand-held Laser Scanners (HLS) and Unmanned Aerial Vehicle Laser Scanners (UAV-LS) point cloud data.