Plant phenotyping methods.

Visual-SLAM for the detection of hidden tomatoes in greenhouses by Hierarchical Localization and GLOMAP for robotized harvesting ↗

Advanced crop monitoring inside greenhouses is becoming one of the primary objectives of research centers. High-performance sensors, such as LiDAR or stereo cameras, have traditionally been employed for this purpose, though these often have a high cost. This work proposes a Visual-SLAM system using a monocular camera, which is significantly more cost-effective and specifically tailored for agricultural applications, such as mapping tomato crops in a greenhouse. Tests were carried out on a real tomato bunch, located in the Agroconnect experimental greenhouse. A ROS 2 Humble node was developed to run on the robot in order to capture images of these crops, which were then stored for offline processing. To generate a 3D mapped model for the crop in the greenhouse, the GLOMAP mapper, based on Structure-From-Motion, was integrated with the Hierarchical Localization toolbox. This initial mapping is a foundation for future, more advanced algorithms to analyze growth patterns, and optimize agricultural management. The system leverages a hierarchical localization paradigm based on a coarse-to-fine strategy: it first performs global retrieval to generate location hypotheses, then combines local features within the identified candidate regions. The results show a correct identification of the tomato cluster, correctly characterising the tomato that is occluded and inaccessible by classical vision technologies. The reconstructed 3D model was further validated against manual ground-truth measurements of fruit size, centroid position, and orientation, confirming the geometric accuracy of the proposed low-cost monocular pipeline.

Why it matches plant phenotyping methods単なる収穫対象の位置検出ではなく、単眼Visual-SLAMと3D再構成を開発し、果実サイズ・重心位置・向きを実測値で検証しているため、植物器官形質の取得手法が中心である。

abstractThis work proposes a Visual-SLAM system using a monocular camera, which is significantly more cost-effective and specifically tailored for agricultural applications, such as mapping tomato crops in a greenhouse.

A Cost-Effective Approach to Estimate Quinoa Aboveground Biomass Volume Combining UAV RGB Data with Sentinel-1 and Sentinel-2 Satellite Imagery ↗

This study assessed the integration of Unmanned Aerial Vehicles (UAVs) and satellite images (Sentinel-1 and Sentinel-2) in advanced machine-learning techniques to monitor the ABV of quinoa crops (Jacha Grano variety) across the Bolivian Altiplano. The proposed method follows a two-step procedure. First, UAV RGB images were used in photogrammetric and deep-learning (Convolutional Neural Networks-CNN) models to estimate reference quinoa ABVs at a 10 m spatial resolution from the crop canopy 3D model and classification, respectively. Secondly, several spectral and polarization/texture indices derived from Sentinel-2 and -1 images were integrated into three decision-tree-based machine-learning models (Random Forest-RF, Gradient Boosting-GB, eXtreme Gradient Boosting-XGB), and one CNN-based machine-learning model to estimate ABV. Additionally, a Stacking Model (STM) build on top of the three decision-tree-based models was considered for comparison. Model evaluation was also performed in a two-step approach. First, a 10-fold cross-validation strategy was used to highlight ABV sensitivity to Sentinel-2 and Sentinel-1 alone and in combination. Secondly, a Leave-One-Plot-Out Cross-Validation (LOPOCV) strategy was used to avoid autocorrelation between the training and evaluation dataset and therefore provided more insight into ABV mapping potential. The results showed that the combination of Sentinel-1 and Sentinel-2 features in the CNN model achieved the best predictive performance with R2 and RMSE values of 0.64 and 0.39 m3 ∙ 100 m−2, respectively. These findings highlight the potential of integrating multi-source information in advanced artificial intelligence algorithms for quinoa ABV monitoring, offering new insights toward the identification of sustainable practices across remote regions with complex socio-economic contexts.

Why it matches plant phenotyping methodsUAV画像・衛星センサー・機械学習を統合し、キノアの地上部バイオマス体積という植物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である。

abstractUAV RGB images were used in photogrammetric and deep-learning (Convolutional Neural Networks-CNN) models to estimate reference quinoa ABVs

Pinus pinaster Seedling Detection in Coastal Dune Plantations Using a UAS Multispectral Point Cloud and Point Transformer V3 ↗

Early detection of tree-seedling establishment is essential for monitoring regeneration success in coastal-dune plantations, where conventional field assessments remain labour-intensive and spatially limited. This study presents a deep-learning workflow for detecting early-stage Pinus pinaster seedlings using multispectral UAS-derived point clouds. Field surveys in the Quiaios National Forest, Portugal, mapped approximately 1500 seedlings using RTK GNSS positioning, biometric measurements, and field photographs. Multispectral imagery acquired with a DJI Mavic 3 Multispectral platform was processed through Structure-from-Motion to generate calibrated orthomosaics, terrain products, and dense point clouds. Training-data preparation combined pine-centred buffers, spectral conditioning, manual refinement and point-cloud class assignment. Point Transformer V3 models were trained in ArcGIS Pro and evaluated using field-mapped buffers withheld from model training within plantation-line areas. The Baseline high-recall model achieved 88% object-level recall at the operational threshold of at least three classified Pine-Seedling points per buffer. The refined hard-negative model retained 84% recall while reducing off-buffer detections from 243 to 41. False-negative analysis showed that omissions were associated with reduced crown diameter and limited branch development under the adopted buffer-based retrieval framework. These results support transformer-based multispectral point-cloud classification for scalable monitoring of early-stage pine regeneration in heterogeneous coastal environments.

Why it matches plant phenotyping methodsUASマルチスペクトル点群とPoint Transformer V3により、マツ幼苗の存在・定着状態を植物個体レベルで推定する手法を開発・評価しており、検出性能も検証しているため、植物フェノタイピング手法が中心である。

abstractThis study presents a deep-learning workflow for detecting early-stage Pinus pinaster seedlings using multispectral UAS-derived point clouds.

AI-enabled Low-Cost 3D Maize Ear Morphometry Platform at Breeding Scale ↗

Maize ear geometry (length, width, curvature, and volume) is closely tied to yield and grain-filling outcomes, but existing high-throughput phenotyping pipelines remain constrained by the cost, labor, and specialized hardware they require. We developed and validated a low-cost pipeline that reconstructs a watertight 3-D mesh of a maize ear from a single 20-second video captured with a consumer-grade DSLR on a motorized turntable under uniform LED illumination. Camera poses from a multi-seed COLMAP procedure initialize a Neural Radiance Field (NeRF), and a cylindrical holder of known diameter, visible in every frame, provides automatic metric scaling with downstream geometric quality control. Applied to 300 ears spanning a diverse maize inbred panel, 250 (83.3%) passed automated processing and quality control. Skeleton length agreed with manual caliper measurements across all 250 ears (R^2 = 0.964, RMSE = 4.68 mm), and convex-hull volume agreed with water-displacement volume on a 15-ear subset spanning the full size range (R^2 = 0.982, RMSE = 5.26 mL). Residual length error grew with ear curvature, whereas bounding-box height, which records the same straight-line chord as calipers, showed no such trend; the discrepancy therefore originates in the measurement definition, since calipers record the chord while skeleton length traces the geodesic arc. The capture hardware costs approximately 607 USD, and operator involvement fell from roughly five minutes to one minute per ear, with all downstream processing running unattended. The platform provides a foundation for breeding-scale 3-D ear phenotyping.

Why it matches plant phenotyping methodsトウモロコシ雌穂の3D形態形質を抽出する低コスト画像計測パイプラインを開発し、手動測定および体積測定で技術検証しているため、方法が研究の中心である。

abstractWe developed and validated a low-cost pipeline that reconstructs a watertight 3-D mesh of a maize ear

Integrating soil and canopy sensing to map and relate variability in tart cherry orchards ↗

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.

Phenomic Prediction I: Plot-Level Prediction of Lodging Severity in Sorghum Breeding Trials Using UAV-Based Photogrammetric Height Data ↗

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.

Construction and Validation of a High-Fidelity Virtual Scene for Low-Stature and High-Biodiversity Ecosystems—Simulating Multi-Modal Sensing Approaches ↗

The Greater Cape Floristic Region (GCFR) in South Africa is a fire-prone biodiversity hotspot where high species richness, structural complexity, and small plant sizes (0.0001–4 m2) pose substantial challenges for remote sensing-based biodiversity assessment. Spectral similarity among species and the mismatch between plant size and sensor pixel dimensions limit the capacity of current and forthcoming spaceborne systems to resolve individual species and accurately detect plot-level diversity changes. We therefore developed a physics-based simulation framework that couples fynbos trait measurements with radiative transfer modeling in the DIRSIG (Digital Imaging and Remote Sensing Image Generation) environment towards quantifying information loss across spectral and spatial scales and to define theoretical limits for biodiversity monitoring. We constructed a three-dimensional virtual scene of post-fire fynbos communities in Grootbos Private Nature Reserve, integrating high-resolution imagery, terrestrial laser scanning (TLS), and structure-from-motion (SfM)-derived point clouds. Field measurements of mean diameter and percent cover were used to scale vegetation models and constrain species abundance. We distributed plant instances using a blue noise sampling algorithm, guided by density maps derived from unmanned aerial system (UAS) imagery. Species-specific optical properties were parameterized using field-measured reflectance data and the PROSPECT radiative transfer model, while terrain structure was derived from SfM-based digital terrain models. The integrated scene was used to simulate multispectral (DJI Mavic 3 MSI), hyperspectral (AVIRIS-NG), and light detection and ranging (LiDAR) observations. Agreement between simulated outputs were evaluated against corresponding field-acquired datasets using spectral signatures and vegetation indices. This framework enables systematic assessment of sensor specification effects on spectral biodiversity metrics and provides a pathway for evaluating theoretical limits of species discrimination across airborne and satellite platforms.

Why it matches plant phenotyping methods植物群落の種判別・多様性指標を対象に、物理ベースの仮想シーンとマルチモーダルセンシングを開発し、実測データで検証しているため、植物状態の取得・推定法が中心である。

abstractWe therefore developed a physics-based simulation framework that couples fynbos trait measurements with radiative transfer modeling in the DIRSIG (Digital Imaging and Remote Sensing Image Generation) environment towards quantifying information loss across spectral and spatial scales and to define theoretical limits for biodiversity monitoring.

Multi-scale reconstruction of snow-avalanche frequency and vegetation structure using dendrogeomorphology, satellite imagery, and UAV photogrammetry ↗

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.

Comparative evaluation of five biomass quantification methods in bermudagrass ↗

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

Canopy height from drone photogrammetry better predicts aboveground biomass than vegetation greenness indices in a semi-arid savanna ↗

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.

Canopy height from drone photogrammetry better predicts aboveground biomass than vegetation greenness indices in a semi-arid savanna ↗

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.

Canopy height from drone photogrammetry better predicts aboveground biomass than vegetation greenness indices in a semi-arid savanna ↗

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.

Mapping Neighborhood Spatial Structure in Traditional Home Gardens Using UAV-Derived 3D Canopy Models ↗

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

LBD-PointNet++: a point cloud segmentation network for phenotypic trait extraction of broccoli seedlings ↗

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

LiDAR vs. SfM: Which is better for analysing habitat of the harvest mouse ( Micromys minutus )? ↗

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)

A comparative analysis of 3D point clouds and crop surface models for rice plant height estimation using UAV-SfM ↗

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

Assessing the suitability of a developed photogrammetric and multispectral method for detecting biostimulant effects on plants ↗

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.

Non-Destructive Evaluation of In Vitro Blackberry Shoot Architecture Under Different Sucrose Levels Through Smartphone-Derived 3D Reconstruction ↗

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.

3D Reconstruction of deciduous Trees using low-cost UAV - and Crane-based Photogrammetry for Monitoring Shoot Elongation across entire Canopies ↗

Abstract. Tree growth determines how much CO2 is sequestered from the atmosphere and temporarily stored in woody biomass. At the same time tree growth is affected by increasing temperatures, more frequent drought periods, late frosts and other extreme events associated with climate change. While continuous measurements of radial (secondary) tree growth using dendrometers are well established, monitoring of shoot elongation (primary growth) has largely been neglected because suitable measurement techniques are lacking. As a result, the effects of climate change on primary tree growth remain insufficiently understood. This work aims at reconstructing native deciduous trees in 3D as a basis for measuring and monitoring shoot elongation over entire tree canopies. Here we explored the use of low-cost UAV photogrammetry and of a multi-camera CraneCam system under real-world conditions. Data were collected in two study areas over an entire growing season. We present sensor evaluations, photogrammetric data acquisition and processing strategies. A special focus is placed on the analysis of the resulting photogrammetric 3D point clouds in terms of accuracy, resolution and completeness. Results demonstrate 3D point accuracies of 5-6 mm for entire trees using consumer-grade UAVs weighing less than 250 g and a 3D reconstruction completeness between 92% and 98% depending on the UAV type. The paper introduces a novel 3Dßprinted ground-truth branch to evaluate the capability to reconstructing fine-detail structures such as thin tree shoots. Finally, we discuss operational challenges and initial experiments towards a skeletonization of entire trees based on photogrammetric point clouds.

Why it matches plant phenotyping methods樹冠全体のシュート伸長という植物形質を取得するための3D再構成手法を開発・評価しており、精度・解像度・完全性の検証も中心的に扱っている。

abstractThis work aims at reconstructing native deciduous trees in 3D as a basis for measuring and monitoring shoot elongation over entire tree canopies.

Deriving Tree Stem Profile and Volume Using a Close-Range Remote Sensing and Machine Learning Approach ↗

Abstract. Accurate estimation of tree volume is essential for precision forestry and sustainable forest management. Traditional forest inventory methods rely on manual measurements of tree height and diameter, which are time-consuming and costly to conduct over large areas, and difficult to perform efficiently in dense forest stands. This study presents a data-driven approach for estimating tree volume from partial tree stem profiles derived from high-resolution datasets. While the study relies on harvester production data (Sweden) and field-measured tree stem profiles (Brazil), the framework is designed to support the estimation of tree volume from close-range remote sensing techniques, such as terrestrial photogrammetry using handheld cameras. Three modelling approaches were evaluated, including two machine learning models (XGBoost and Random Forest) using partial tree stem profile measurements as predictors, and one baseline model (XGBoost) using diameter at breast height and tree height as predictors. The models were developed using two independent datasets: harvester production data of Norway spruce (Picea abies (L.) H. Karst.) from Sweden and field-measured tree stem profiles of Slash pine (Pinus elliottii Engelm.) and Loblolly pine (Pinus taeda L.) plantations from Brazil. The results show that tree volume can be predicted with reasonable accuracy using partial tree stem profiles, although models incorporating tree height achieved the lowest prediction errors. The findings demonstrate that partial tree stem profiles provide valuable structural information for machine learning-based tree volume estimation. This framework supports the future integration of close-range remote sensing techniques into modern forest inventory systems.

Why it matches plant phenotyping methods樹幹プロファイルから樹木体積という植物形質を推定する機械学習・近距離リモートセンシング手法が研究の中心であり、複数モデルと独立データセットで評価している。

abstractThis study presents a data-driven approach for estimating tree volume from partial tree stem profiles derived from high-resolution datasets.

Non-Destructive Three-Dimensional Phenotyping of Garlic Bulbs Based on Multi-View Imaging ↗

Traditional manual measurement of garlic bulb phenotypic traits is inefficient, subjective, poorly reproducible, and may cause sample damage. To improve the adaptability of three-dimensional reconstruction to garlic bulb morphology and grading-related parameter extraction, this study developed a non-destructive phenotypic measurement workflow based on multi-view image-based three-dimensional reconstruction. Four garlic materials with distinct bulb morphologies and epidermal characteristics were used to demonstrate the feasibility of the reconstruction workflow, and 40 Lanling white-skinned garlic bulbs were used for quantitative accuracy validation. Multi-view images were acquired using a high-resolution camera, a motorized turntable, and a controlled illumination system. Three-dimensional models were reconstructed using ContextCapture, and the resulting point clouds were processed in CloudCompare through cropping, denoising, downsampling, and pose correction. Maximum longitudinal diameter, maximum transverse diameter, and volume were extracted from the processed point clouds according to GB/T 45244-2025 (Grades and Specifications of Garlic) and validated against manual reference measurements. The coefficients of determination for maximum longitudinal diameter, maximum transverse diameter, and volume were 0.9935, 0.9909, and 0.9924, respectively, with RMSE values of 0.0529 cm, 0.0520 cm, and 0.8874 cm3, and MAPE values of 0.6647%, 0.7765%, and 1.9149%. Additional MAE, bias, confidence interval, and Bland–Altman analyses further supported the agreement between model-derived and manual reference measurements. These results demonstrate the feasibility of multi-view image-based three-dimensional reconstruction for non-destructive garlic bulb phenotypic measurement and provide a methodological basis for future grading-related assessment and three-dimensional phenotyping of bulbous horticultural crops.

Why it matches plant phenotyping methodsニンニク球の形態形質を非破壊的に取得する3D画像計測ワークフローを開発し、手動測定と定量検証しており、フェノタイピング手法が中心である。

abstractthis study developed a non-destructive phenotypic measurement workflow based on multi-view image-based three-dimensional reconstruction.

Vegetation Biomass Estimation Using 3D Ground-Based Point Clouds: A Systematic Review ↗

Abstract Purpose of Review Ground-based 3D point cloud technologies, including static terrestrial laser scanning (TLS), mobile laser scanning (MLS), and close-range photogrammetry, are increasingly used for estimation of aboveground vegetation biomass as they provide detailed structural representations across vegetation types; however, a comprehensive synthesis of how point-cloud data are translated into biomass estimates remains lacking. This review evaluates current approaches, performance patterns, and methodological gaps in biomass estimation using 3D ground-based point clouds. Recent Findings We systematically reviewed and analyzed 160 research articles (comprising 171 device-specific studies) published until the end of 2025 (first appearing in 2010). Research was dominated by tree-based applications (74%), with limited attention to shrubs, grasslands or crops. TLS was the prevailing acquisition technology (78%), although MLS adoption is growing. Biomass estimation primarily relied on allometric equations, volume-based reconstructions (e.g., quantitative structure models, voxelizations, convex hull), and parametric regression models. Reported model performance was generally high in tree- and shrub-based studies (median R 2 > 0.8), but more variable in non-woody vegetation types. Despite rapid advances in 3D sensing, point-cloud-native deep-learning approaches remain rarely implemented in biomass estimation workflows. Summary Ground-based 3D sensing is maturing technically, yet methodological heterogeneity persists. Many workflows still depend on destructive calibration data, semi-manual preprocessing, and non-standardized modelling strategies, limiting reproducibility and cross-study comparability. Multi-sensor integration is emerging but lacks consistent upscaling frameworks. Future research should expand coverage of underrepresented vegetation types, promote standardized and automated processing pipelines, and systematically evaluate point-cloud-native deep learning architectures, both for extracting structural proxies and for assessing their capacity to estimate biomass directly.

Why it matches plant phenotyping methods3Dセンシングによる植物バイオマス推定手法を体系的にレビューし、取得技術、推定ワークフロー、性能、再現性、標準化を評価しており、表現型測定法が中心である。

abstractThis review evaluates current approaches, performance patterns, and methodological gaps in biomass estimation using 3D ground-based point clouds.

Improving ecological indicators of mangrove canopy height and aboveground biomass through multi-source data fusion on the Amazon coast ↗

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.

Using unoccupied aerial systems (UAS) and photogrammetry to estimate tree height and trunk diameter of urban American elm trees ↗

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

3D Reconstruction of deciduous Trees using low-cost UAV- and Crane-based Photogrammetry for Monitoring Shoot Elongation across entire Canopies ↗

Tree growth determines how much CO2 is sequestered from the atmosphere and temporarily stored in woody biomass. At the same time tree growth is affected by increasing temperatures, more frequent drought periods, late frosts and other extreme events associated with climate change. While continuous measurements of radial (secondary) tree growth using dendrometers are well established, monitoring of shoot elongation (primary growth) has largely been neglected because suitable measurement techniques are lacking. As a result, the effects of climate change on primary tree growth remain insufficiently understood. This work aims at reconstructing native deciduous trees in 3D as a basis for measuring and monitoring shoot elongation over entire tree canopies. Here we explored the use of low-cost UAV photogrammetry and of a multi-camera CraneCam system under real-world conditions. Data were collected in two study areas over an entire growing season. We present sensor evaluations, photogrammetric data acquisition and processing strategies. A special focus is placed on the analysis of the resulting photogrammetric 3D point clouds in terms of accuracy, resolution and completeness. Results demonstrate 3D point accuracies of 5-6 mm for entire trees using consumer-grade UAVs weighing less than 250 g and a 3D reconstruction completeness between 92% and 98% depending on the UAV type. The paper introduces a novel 3Dprinted ground-truth branch to evaluate the capability to reconstructing fine-detail structures such as thin tree shoots. Finally, we discuss operational challenges and initial experiments towards a skeletonization of entire trees based on photogrammetric point clouds.

Why it matches plant phenotyping methods樹冠全体のシュート伸長を測定するための3D再構成手法を開発・評価し、センサー評価、取得・処理戦略、精度・完全性の検証を中心に扱っているため。

abstractThis work aims at reconstructing native deciduous trees in 3D as a basis for measuring and monitoring shoot elongation over entire tree canopies.

Strengths and Limitations of Handheld Mobile Laser Scanning in Vegetation Assessment in Tropical Mangroves, Wetlands, and Rangelands Ecosystems, Queensland Australia ↗

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

Comparison of 3D Point Cloud Acquisition Accuracy in a Large-Scale Japanese Pear Tree Orchard ↗

Practical three-dimensional (3D) phenotyping in large-scale orchards with repetitive row structures remains challenging, and systematic evidence comparing both accuracy and acquisition efficiency under outdoor conditions remains limited. This study presents a field-deployable evaluation framework and implements it in a 2-ha commercial Japanese pear orchard trained under a joint V-trellis system. Using a terrestrial laser scanner (TLS) as the reference, we evaluated two handheld LiDAR systems (a low-cost SLAM-based system and a high-performance system), structure from motion / multi-view stereo (SfM/MVS) reconstructions from three camera platforms (a digital camera, an action camera, and a 360° camera), and 3D Gaussian splatting (3DGS) constructed from action-camera video. Measurements were taken at two spatial scales to capture scale-dependent effects. In the span-scale survey (4 m), location error was derived from TLS-referenced target coordinate differences, and reconstruction error was quantified using cloud-to-mesh distances with cubic targets. In the row-scale survey (one tree row), positional stability during continuous mapping was evaluated as location error. Operational metrics (acquisition time, data volume, and processing effort) were also documented. The results demonstrate clear trade-offs among the methods: LiDAR enables rapid wide-area acquisition but is susceptible to cumulative drift in row-structured environments, whereas SfM/MVS provides superior geometric fidelity at the cost of increased time and data volume. Although 3DGS is less suitable for precise quantitative measurement, it demonstrates strong potential for intuitive visualization of orchard structure and fruit distribution. These findings highlight the need for staged, purpose-specific, and seasonally adaptive strategies for orchard-scale digital twin development.

Why it matches plant phenotyping methods果樹園における複数の3D取得法を比較・評価し、樹体構造や果実分布の定量的取得精度、安定性、運用性を検証しており、植物フェノタイピング手法が中心である。

abstractPractical three-dimensional (3D) phenotyping in large-scale orchards with repetitive row structures remains challenging

UAV Remote Sensing for Drought-Adaptive Sesame Breeding: Flight-Altitude Benchmarking, Predictive Modelling, and Composite Stress Tolerance Indexing ↗

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.

Comparative evaluation of precision planter performance via UAV remote sensing: A workflow for maize emergence monitoring ↗

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.

Scalable Urban Forest Monitoring: High‐Precision Individual Tree Inventory Using Low‐Cost Oblique UAV Photogrammetry ↗

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.

A comparison of single-photon lidar, conventional lidar, and digital aerial photogrammetry for area-based forest inventory ↗

Abstract More than 20 years have passed since forest management inventories were first implemented using discrete-return linear-mode lidar (LML) and the area-based approach (ABA). Among recent sensor innovations, single-photon lidar (SPL) stands out as a particularly promising technology for improving the cost efficiency of ABA. The main objective of this study was to assess the cost efficiency of ABA when reducing SPL point density by increasing flight altitude, thereby enabling larger area coverage. Three SPL datasets (10.1, 24.2, and 59.3 first returns/m2) were compared with two LML datasets (2.6 and 136.9 first returns/m2) and digital aerial photogrammetry (DAP) (61.3 points/m2). The analysis used 249 systematically distributed ground plots across various boreal forest types, and nonlinear models were constructed for basal area, stem density, volume, Lorey’s mean height, and dominant height. Model predictions were further validated using 530 independent plots aggregated to 47 stands. The results showed that SPL achieved model accuracies comparable to LML and consistently better than DAP. When averaging relative root mean square error (RMSE) across all biophysical attributes and forest types, SPL low-density data yielded smaller errors than low-density LML data and DAP in both cross-validation and independent stand validation. For example, in mature, highly productive forest—where more than half of the validation stands were located—the RMSE values for volume were 9.3, 8.7, 15.6, 6.4, 6.8, and 6.2% for LML low density, LML high density, DAP, SPL low density, SPL medium density, and SPL high density, respectively. Furthermore, SPL data collected at multiple flight altitudes indicate that operating above commonly reported in the literature and manufacturer-recommended heights can allow for up to a 30% reduction in flight distance while maintaining comparable model accuracy, although such gains may not translate into proportional cost reductions due to technical and atmospheric constraints limiting suitable flight conditions.

Why it matches plant phenotyping methods森林プロットの生物物理形質を推定する複数のLiDAR・写真測量法を比較し、独立データで精度検証しているため、センサーに基づく植物形質計測法の検証が中心である。

titleA comparison of single-photon lidar, conventional lidar, and digital aerial photogrammetry for area-based forest inventory

Forest biometrics in the 21 century special issue ↗

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.

Species-Specific Stem Volume Models for Urban Broad-Leaved Trees in Beijing Using Handheld Photogrammetric Height Measurement and Destructive Validation ↗

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

Cross-Day Grape Cluster Tracking Using Branch-Based 3D Alignment in Vineyards ↗

In Japan, the quantity of domestically produced fruit has been gradually decreasing, while wholesale prices have continued to rise due to declining production volumes and a shift toward high-quality varieties. To address these trends, improving quality and reducing labor through automation have become urgent challenges. In precision viticulture, monitoring the growth of grape clusters plays a key role in yield estimation, disease management, and optimal harvest timing. Although recent advances in deep learning and 3D reconstruction have enabled accurate fruit detection and modeling in vineyards, tracking the same clusters on different days remains challenging because of branch movement, fruit growth, and varying imaging conditions. This study proposes a branch-based 3D alignment framework for the cross-day tracking of grape clusters. Stable vine structures, such as trunks and main branches, are reconstructed using Structure from Motion, and their spatial correspondences are estimated through SIFT-based matching and similarity transformation. Once the coordinate systems of different days are aligned, the grape clusters detected by CenterNet are associated based on spatial proximity in the unified 3D space. Experiments over multiple observation days demonstrated that the proposed method successfully maintained the consistent tracking of grape clusters throughout the growth period. These results indicate that branch-based alignment effectively stabilizes multi-day observations and facilitates the temporal monitoring of fruit growth, supporting automated phenotyping and future field robot applications in viticulture.

Why it matches plant phenotyping methodsブドウ房の経日追跡を目的とする3D画像アライメント手法を開発し、果実成長の時系列モニタリングと自動フェノタイピングへの利用を実験的に検証しているため、フェノタイピング手法が中心である。

abstractThis study proposes a branch-based 3D alignment framework for the cross-day tracking of grape clusters.

Comparative evaluation of incremental and global SfM-MVS pipelines for 3D reconstruction of peanut plants: implications for viewpoint configuration and image preprocessing ↗

Three-dimensional (3D) reconstruction based on structure from motion and multi-view stereo (SfM-MVS) is increasingly used in plant phenotyping, but its performance is influenced by crop architecture, viewpoint configuration, and image preprocessing. For compact crops such as peanut, dense branching and severe within-canopy occlusion make reliable reconstruction challenging. This study evaluated the effects of reconstruction pipeline, angular interval, and image preprocessing on 3D reconstruction of peanut plants under controlled rotary imaging. A total of 10,800 RGB images from 30 plants were used to compare representative implementations of incremental and global SfM-MVS pipelines in terms of geometric quality, phenotypic accuracy, and processing efficiency. At the 1° baseline, the tested global pipeline implementation reduced the root mean square reprojection error (RMSRE), point-density coefficient of variation (CV), vertical root mean square error (VRMSE), and the 95th percentile of the absolute point-cloud distance values (P95) by 15.05%, 14.08%, 39.87%, and 33.33%, respectively, and increased average phenotypic accuracy from 96.08% to 97.37%, compared with the tested incremental pipeline implementation. In contrast, the tested incremental implementation showed a lower voxel void ratio and shorter processing time. In both pipelines, increasing the angular interval reduced processing time but also reduced geometric stability, internal voxel filling, and phenotypic accuracy. In the present dataset, angular intervals of 3°–5° provided a favourable balance between reconstruction accuracy and efficiency. Cropping reduced peripheral redundancy, whereas cropping combined with background removal produced the best overall results, with the lowest reprojection error and the highest phenotypic accuracy. These results provide practical guidance for selecting reconstruction pipeline, viewpoint configuration, and preprocessing strategy in close-range indoor 3D phenotyping of peanut plants and crops with similar canopy architectures.

Why it matches plant phenotyping methods落花生の3D表現型取得について、SfM-MVSパイプライン、視点間隔、画像前処理を比較・検証しており、フェノタイピング手法が研究の中心である。

abstractThis study evaluated the effects of reconstruction pipeline, angular interval, and image preprocessing on 3D reconstruction of peanut plants under controlled rotary imaging.

Mapping understorey tree invasion in a drought-affected forest using multi-view UAV imagery and deep learning ↗

Advances in deep learning (DL) and structure from motion (SfM) photogrammetry combined with off-the-shelf unoccupied aerial vehicles (UAVs) and high-resolution cameras enable unprecedented plant species mapping accuracy. While these tools have been mainly applied to flat terrain or upper forest canopies, the forest understorey remains largely unexplored. Here, we present a method combining DL with multi-view UAV imagery to map the invasive tree-of-heaven ( Ailanthus altissima ) in the understorey of a drought-affected Central European forest. The raw UAV photographs were segmented with convolutional neural networks (CNNs). Resulting predictions were projected onto georeferenced point clouds using SfM. This novel approach revealed that more than 40% of the invasion was hidden beneath the canopy and would have been missed by conventional orthomosaic-based methods. To assess CNN generalization abilities, we altered training and prediction domains: lower-processing-level aerial images vs. higher-processing-level orthomosaic, both originating from a small training extent (420 m 2 or 0.25% of the study area). For intra-domain predictions, aerial-trained models (F1=0.880) outperformed ortho-trained models (F1=0.805). When applied cross-domain, aerial models retained superior performance (F1=0.843) over ortho-trained ones (F1=0.750). Expanding the training extent eightfold raised the accuracy of the ortho-trained models to F1=0.836 within-domain and F1=0.846 cross-domain. In summary, integrating photogrammetry with DL is a promising route for utilizing overlapping aerial imagery efficiently and leveraging information that conventional orthomosaic-based analyses discard. The resulting richer 3D information and improved transferability may support decision-making and retrofitting this novel technique to upcoming and existing datasets opens new avenues in vegetation studies and beyond.

Why it matches plant phenotyping methods森林下層の侵入樹木を対象に、UAV多視点画像、SfM、深層学習を統合した植物状態（侵入・分布）の抽出法を開発し、ドメイン間性能も検証しており、方法が中心である。

abstractHere, we present a method combining DL with multi-view UAV imagery to map the invasive tree-of-heaven ( Ailanthus altissima ) in the understorey of a drought-affected Central European forest.

LiDAR and UAV Photogrammetry for Three-Dimensional Canopy Reconstruction: A Comparative Study for Precision Agriculture Under Mediterranean Conditions ↗

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

The Sykes tree: Changes in the architecture of Fouquieria columnaris after six decades on the coast of Sonora, Mexico ↗

Estimating the age and growth of long-lived desert succulents is challenging due to the absence of annual growth rings. This study utilizes Single-Image Photogrammetry (SIP) and a historical photograph taken by G. Sykes in 1965 and two replicated photographs obtained in 2016 and 2025 to analyze architectural changes over six decades in an iconic “Boojum tree” ( Fouquieria columnaris ) and an adjacent columnar cactus ( Pachycereus pringlei ) from a relictual population in Sonora, Mexico. Results indicate that F. columnaris exhibited a slow mean vertical growth rate of 1.71 cm yr −1 during the 1965-2016 interval which increased to 3 cm yr −1 for the recent 2016-2025 period. This acceleration aligned with values recorded at more favorable sites (i.e. 3.3 to 3.6 cm yr −1 ). Conversely, P. pringlei exhibited a minimal growth rate (1.01 cm yr −1 ), which is much lower than rates reported from repeat photography (5-10 cm yr −1 ) or radiocarbon dating of spines (3-23 cm yr −1 ). Together, these species demonstrate high ecological resilience near their physiological limits in the Sonoran Desert through divergent morphofunctional pathways. F. columnaris favors architectural stability and sustained growth, whereas P. pringlei relies on mechanical robustness and structural redundancy. These findings highlight the efficacy of SIP and historical records for long-term demographic monitoring in extreme arid environments.

Why it matches plant phenotyping methods単一画像フォトグラメトリを用いて植物の建築構造変化と成長速度を定量化することが研究の中心であり、植物表現型の実質的な測定法適用に該当する。

abstractThis study utilizes Single-Image Photogrammetry (SIP) and a historical photograph taken by G. Sykes in 1965 and two replicated photographs obtained in 2016 and 2025 to analyze architectural changes over six decades

Evaluation of Global and High-Resolution Canopy Height Models for Forest Monitoring and Disturbance Detection: From GEDI Footprint to Deep Learning High-Resolution Mapping ↗

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.

Application of Photogrammetric Software for Digital Canopy Height Modelling from Old Aerial Photographs ↗

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.

BN-NeRF: A fast 3D reconstruction and phenotyping framework for banana plants using handheld devices ↗

High-fidelity 3D reconstruction and precise phenotypic parameter extraction of banana plants are critical for crop growth monitoring and yield estimation in precision agriculture. However, traditional methods encounter significant bottlenecks: LiDAR systems are cost-prohibitive for widespread adoption, while traditional photogrammetry often fails to handle the complex canopy structures, severe occlusions, and weak texture features characteristic of banana leaves. To address these limitations, this article proposes a novel framework for 3D reconstruction and automatic phenotyping based on multi-view images captured by mobile phones. We introduce BN-NeRF, an enhanced Neural Radiance Field method built upon Instant-NGP. Specifically, we integrate three key technical improvements: (1) frame-level geometric calibration to correct camera pose drift caused by handheld motion; (2) sparse geometric anchoring to explicitly constrain depth and scale using sparse point clouds; and (3) thin-leaf prior regularization to suppress artifacts and improve the geometric accuracy of leaf surfaces. Building on this reconstruction, we establish a complete pipeline to recover explicit metric geometry from implicit radiance fields. By combining mesh topological analysis with geodesic algorithms, we achieve automated and precise extraction of key morphological parameters. Extensive experiments were conducted on a dataset of 90 banana plants in a real-world orchard. The results demonstrate that BN-NeRF achieves superior rendering quality (PSNR of 32.4 dB, SSIM of 0.951, and LPIPS of 0.152) while maintaining inference speeds comparable to Instant-NGP. Furthermore, the extracted phenotypic parameters showed strong agreement with manual ground truth across both leaf-level and structural traits. In addition to trait-specific regression performance, the evaluation also includes normalized completeness analysis, calibration-cube-based scale validation, and Bland-Altman agreement analysis, supporting the measurement reliability of BN-NeRF for field phenotyping. This study demonstrates that low-cost smartphone-based acquisition, combined with BN-NeRF, can support accurate field phenotyping of banana plants. In addition, an implemented mobile-cloud system was functionally validated through repeated end-to-end runs on an iPhone 13 client and a cloud workstation.

Why it matches plant phenotyping methodsスマートフォン画像からの3D再構成と植物形態形質抽出を中核とするBN-NeRF手法を開発し、圃場データで精度・再現性を検証しているため。

abstractthis article proposes a novel framework for 3D reconstruction and automatic phenotyping based on multi-view images captured by mobile phones

Active and passive 3D sensing for forest stem geometry: Comparing MLS, consumer LiDAR, SfM and Gaussian Splatting ↗

Accurate characterization of tree stem geometry is essential for forest inventories, yet conventional field measurements of diameter at breast height (DBH) are limited to a single cross-section and do not capture vertical variability along the trunk. This study compares five approaches for stem characterization in a Mediterranean forest: mobile laser scanning (MLS), consumer-grade iPad-LiDAR, Structure from Motion (SfM) photogrammetry, Gaussian Splatting (GS), and manual field measurements. Data were acquired simultaneously within a 2.5 m radial plot. DBH was estimated through RANSAC-based circular fitting, and stem sections were extracted every 20 cm to assess diameter stability along the trunk. All techniques produced similar mean DBH values closely matching field measurements (23 cm), with MLS achieving the lowest RMSE (1.29 cm), followed by SfM (1.52 cm), GS (1.60 cm), and iPad-LiDAR (2.26 cm). However, marked differences were observed in vertical completeness. MLS captured the full vertical profile of the stems, reaching 14.11 m, whereas SfM and GS from iPhone, and iPad-LiDAR were limited to approximately 6 m or less. The results indicate that although low-cost image-based approaches can provide accurate DBH estimates under controlled conditions, MLS remains the most robust solution for comprehensive vertical stem characterization.

Why it matches plant phenotyping methods森林樹幹のDBHと垂直方向の形状を、複数の3Dセンシング手法で推定・比較し、RMSEや垂直完全性を評価している。植物形状計測法の技術比較・検証が中心である。

abstractThis study compares five approaches for stem characterization in a Mediterranean forest: mobile laser scanning (MLS), consumer-grade iPad-LiDAR, Structure from Motion (SfM) photogrammetry, Gaussian Splatting (GS), and manual field measurements.

Three-dimensional photogrammetric analysis of shoot growth dynamics and spatial competition in in vitro–grown kiwifruit explants ↗

Because conventional vegetative propagation methods for Kiwifruit (Actinidia deliciosa (A.Chev.) C.F.Liang & A.R.Ferguson) often are constrained by their need for extensive plantation areas, high labour inputs, and intensive weed management. Therefore, in vitro micropropagation has emerged as an effective approach for the large-scale production of uniform, disease-free kiwifruit plant material. In the present study the shoot growth dynamics and spatial competition between explants for kiwifruit (cv. Hayward) in vitro-grown were investigated considering different explant densities (3, 5, and 7) and two subculture durations (30 and 45 days). Growth performance was assessed integrating traditional measurements (shoot viability, number and length, callus formation, fresh and dry biomass) with high-resolution three-dimensional photogrammetric reconstruction. Image acquisition was performed using a smartphone-based system (iPhone+viDoc RTK rover), and dense point clouds were generated through the Structure from Motion photogrammetric processing, providing the basis for quantitative geometric analysis. Consistent correlations were observed between manually measured growth traits and smartphone-derived morphometric parameters at both 30 and 45 days of subculture. Specifically, point cloud–based estimates of surface area, height, and volume were significantly associated with shoot number, shoot length, and biomass accumulation, supporting the reliability of 3D photogrammetry as a non-destructive tool for phenotyping in vitro kiwifruit growth. The proposed approach demonstrates the potential of 3D photogrammetry to enhance the objectivity, resolution, and repeatability of growth assessment in in vitro culture systems, offering new insights into shoot development and density-dependent interactions. Graphical Abstract

Why it matches plant phenotyping methodsスマートフォン撮影とSfMによる3Dフォトグラメトリで、キウイフルーツ苗条の表面積・高さ・体積を非破壊推定し、手動測定およびバイオマスとの相関で信頼性を検証しており、表現型取得手法が中心である。

abstractdense point clouds were generated through the Structure from Motion photogrammetric processing, providing the basis for quantitative geometric analysis.

Assessing UAV imagery and high-resolution LiDAR for tree height estimation: The role of flight speed and image overlap ↗

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.

Integration of Aerial Photogrammetry, UAV LiDAR and Terrestrial LiDAR Point Clouds for Individual Tree Measurement and Individual Tree Carbon Storage Estimation ↗

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.

Evaluation of UAV Flight Altitude for Accurate Mangrove Canopy Height Estimation: A Case Study from Melgonze, Persian Gulf, Iran ↗

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

Comparison of Unmanned Aircraft System–based Photogrammetry and Light Detection and Ranging for Pecan Tree Height Estimation ↗

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.

Estimation of Ramie Key Phenotypic Traits Based on UAV Remote Sensing ↗

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.

An Enhanced Image Feature Extraction and Matching Method for Three-Dimensional Reconstruction of Forest Scenes ↗

Accurate and efficient 3D reconstruction of trees is of paramount importance for studying forest spatial structures and dynamic resource patterns, optimizing forest management, protecting environments, and analyzing carbon cycles. Currently, Light Detection and Ranging (LiDAR) remains the dominant method for generating 3D models of forest scenes. However, with advancements in computer vision, photogrammetry has emerged as a crucial tool for forest inventory and 3D reconstruction due to its cost-effectiveness. Nevertheless, in practical forestry applications, traditional photogrammetry often suffers from low reconstruction efficiency and poor quality during feature extraction and matching. These issues stem from the complex structure of forest scenes, severe occlusion, and repetitive texture patterns. To address these challenges, this paper proposes an improved 3D tree reconstruction approach based on images, integrating deep learning-based methods. In the sparse reconstruction stage, we utilize the ALIKED (A LIghter Keypoint and descriptor Extraction network with Deformable transformation) algorithm and construct an image pyramid to extract multi-scale robust features. Furthermore, by combining the LightGlue matching algorithm with a neighborhood search constraint strategy, we enhance the stability of camera pose recovery while reducing redundant computations. Experimental results demonstrate that our method outperforms traditional algorithms in both accuracy and robustness regarding image matching. Compared to baseline models, the proposed approach increases the number of feature points by approximately 50% with a more widespread distribution, improves matching accuracy by 4% to 8%, and achieves a 100% image registration rate. Consequently, under the condition of maintaining equivalent re-projection errors, the subsequent sparse point clouds exhibit an average track length increase of 0.6 to 1.4 and a density increase of up to 1.2 times. Notably, this method effectively mitigates artifacts and spurious reconstructions caused by pose drift in forest photogrammetry.

Why it matches plant phenotyping methods森林内の樹木の3次元形態を画像から再構成する特徴抽出・マッチング手法を開発し、精度と頑健性を評価しており、植物形態の取得方法が研究の中心である。

abstractthis paper proposes an improved 3D tree reconstruction approach based on images, integrating deep learning-based methods.

UAS-Based Analysis of a Black Locust Clone Trial ↗

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.

Precision and accuracy of tree height estimation in citrus orchards: a systematic investigation of manual, airborne LiDAR, SLAM LiDAR, AI-driven photogrammetry ↗

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

Monitoring plant moisture content and optimizing irrigation prescriptions based on UAV multimodal data ↗

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.

Research on Branch Recognition and Pruning Method for Dormant Apple Trees Based on Neural Radiance Fields and PointNeXt ↗

Abstract To address the problem of fine branch identification and pruning decision for dormant apple trees, this study proposes a 3D point cloud branch recognition method integrating Neural Radiance Fields (NeRF) and the PointNeXt network. This method employs the neural radiance field theory to construct a point cloud model of apple trees, achieving fine detail representation and providing a high-precision, high-standard dataset for subsequent branch pruning experiments. First, a panoramic video is captured by circling the fruit tree, and a multi-view image sequence is obtained through frame sampling. Subsequently, the Structure from Motion (SfM) algorithm is employed for sparse reconstruction to recover the pose information of the images. On this basis, a neural radiance field model is trained. Hierarchical sampling is performed using ray casting, and the sampled points, combined with positional encoding, are fed into a multi-layer perceptron (MLP). The radiance field is then generated via volume rendering, from which a high-fidelity 3D point cloud model of the fruit tree is derived. Finally, the point cloud is processed using the PointNeXt semantic segmentation network to achieve the identification and segmentation of branches to be pruned and branches to be retained. To verify the effectiveness of the method, this study reconstructed point cloud models of dormant apple trees and selected 10 of them for experimental analysis. The algorithm achieved an average overall recognition accuracy of 75.15% and an average false negative rate (FNR) of 24.85%. The experimental results demonstrate that the proposed method constructs a 3D point cloud model with multi-scale, multi-modal, and high-precision phenotypic information at a relatively low cost. It not only overcomes the limitations of traditional 3D reconstruction methods, such as insufficient point cloud accuracy and difficulty in accurately identifying thin branches, but also effectively mitigates the high misrecognition rate observed in conventional branch recognition approaches. This provides technical support for unmanned agricultural machinery pruning in orchards and holds significant implications for achieving precision agriculture and sustainable development.

Why it matches plant phenotyping methodsNeRFとPointNeXtを用いてリンゴ樹の3D点群を構築し、剪定対象枝を認識・分割する手法が研究の中心であり、植物の形態・構造状態を直接推定して性能評価している。

abstractthis study proposes a 3D point cloud branch recognition method integrating Neural Radiance Fields (NeRF) and the PointNeXt network.

Stem Modeling for Savanna Tree Characterization with Close-Range Photogrammetry: Comparison of the Performance of Automatic Algorithms ↗

Sudanian savannas remain underexplored in terms of utilizing close-range photogrammetry (CRP) for assessing tree characteristics, leaving a gap in ecological research. This study evaluates the performance of automatic stem modeling techniques using CRP-generated point clouds for 30 trees from five savanna species. Two labeling methods, a machine learning-based approach (StemML) and a flatness/vertical structure-based method (StemFlat), were used to extract stem points. We applied three diameter estimation techniques: convex-hull line fitting (CHM), least squares circle fitting (LSM), and Ransac circle fitting (RANSAC), comparing their results against field measurements using root mean square error (RMSE), bias and the coefficient of determination R2. The combination of StemML and CHM yielded the best performance, with an RMSE of 2.1 cm (5.8%), R2 of 0.983 and a bias of −0.30 cm, accurately identifying 93% of stem segments. Diameter estimation accuracy varied with height, with optimal alignment between CRP-derived profiles and manual measurements occurring between 0.5 and 2.5 m. These findings demonstrate CRP’s potential for modeling savanna tree stems and highlight the importance of method selection in ensuring reliable measurements.

Why it matches plant phenotyping methods近距離写真測量による樹幹点群の抽出・モデル化と直径推定手法を開発・比較し、野外測定で性能検証しているため、植物形質計測手法が研究の中心である。

abstractThis study evaluates the performance of automatic stem modeling techniques using CRP-generated point clouds for 30 trees from five savanna species.

LiDAR and UAV Photogrammetry for Three-Dimensional Canopy Reconstruction: A Comparative Study for Precision Agriculture Under Mediterranean Conditions ↗

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.

High-Resolution UAV-Based NDVI Monitoring Method for Sustainable Post-Mining Land Management ↗

The transition of coal regions under the European Green Deal and Just Transition Fund creates a need for quantitative, transparent monitoring of ecological recovery on post-mining land. This study presents an autonomous UAV-based methodology for high-resolution monitoring of vegetation dynamics on a reclaimed coal waste heap in Upper Silesia, Poland. A DJI Mavic 3 Multispectral platform with RTK positioning conducted approximately biweekly flights from August 2024 to October 2025 over three study plots acquiring RGB and multispectral imagery at approximately 4 cm/pixel. Photogrammetric processing in DJI Terra produced radiometrically corrected orthomosaics and NDVI maps, which were analyzed using an automated QGIS workflow for reprojection, clipping, NDVI-based classification, and quantification of vegetation area across three different reclamation variants. The results indicate that intensive soil conditioning through the application of compost derived from bio-waste achieved a maximum vegetation cover of 94.4%. This treatment consistently maintained the highest level of cover during periods of environmental stress and significantly surpassed both seeding-only treatments and those combining seeding with irrigation. Baseline vegetation cover below 6% confirmed the necessity of active reclamation. This workflow provides rapid and reproducible metrics that are suitable for adaptive management and regulatory reporting. It also offers a scalable template for monitoring coal waste heaps across Europe undergoing SDG-aligned reclamation.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像と自動QGISワークフローにより、植生被覆を plot レベルで定量化する再現可能な計測手法が研究の中心であるため、植物状態の画像ベース表現型計測として採用。

abstractThis study presents an autonomous UAV-based methodology for high-resolution monitoring of vegetation dynamics on a reclaimed coal waste heap

Branch shape parameters for young open-grown Carya laciniosa (F.Michx.) G.Don (Juglandaceae) ↗

Abstract Branch shape is a relevant tree morphological trait, representative of tree ontogenetic stage, successional status and resource-use strategy. However, branch-level studies have been limited due to tedious, time-consuming or costly measurement procedures. Here we applied a cost-efficient, quantitative framework for tree branch shape data collection and statistical evaluation, applied to young open-grown Carya laciniosa , an ecologically valuable but rare large-seeded deciduous tree species. After accounting for branch size and orientation, we fitted different polynomial models to photogrammetric points of 41 major branch axes belonging to four arboretum-grown leaf-off C. laciniosa trees, ranging from 6.1 to 8.7 m in height. Parametric branch shape was identified with great precision by the fourth-order polynomials (R 2 = 0.96 ± 0.07 SD), but also acceptably by the third-order polynomials (R 2 = 0.93 ± 0.13 SD). The shape parameters were weakly related to branch position within the crown (R 2 < 0.40), in contrast to branch size (R 2 = 0.73). The identified S-shaped branch type may be termed plagio-orthotropic, with the proximal part arching plagiotropically and the distal part ascending orthotropically. This type of shape was stable across canopy height strata, but the shape variation and the magnitude of branch curvature clearly decreased towards the upper canopy layers, revealing combined effects of branch age, gravitropism and bending strains induced by the seasonal loads. Our results corroborate the architectural similarity among the mid-successional Carya spp. This study highlights the relevance of branch shape, which can be feasibly recorded in terms of transferable parameters, possibly as a generic functional trait with a potential for quantification of ecosystem services, such as rainfall interception and retention, shading potential, thermal regulation and biodiversity support.

Why it matches plant phenotyping methods枝形状という植物形態形質をフォトグラメトリで定量化する枠組みの開発・適用が研究の中心であり、植物フェノタイピング手法に該当する。

abstractwe applied a cost-efficient, quantitative framework for tree branch shape data collection and statistical evaluation

Turning Mediterranean Farmlands Into Priority Habitats: Natural Expansion of Juniper Woodlands After Agricultural Abandonment ↗

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

Application of unmanned aircraft remote sensing image processing method based on artificial intelligence algorithm in corn growth assessment ↗

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.

A collaborative management approach to transmission line forest clearing tasks in the context of grid intelligence transformation ↗

Tree barriers are the key factors of current transmission line failures, early discovery of tree barriers hidden dangers, the implementation of forest clearing tasks on transmission lines is the current power inspectors need to pay attention to the key issues. The article is based on the inclined photogrammetry technology to obtain the three-dimensional data of transmission lines, and construct a real-time database to realize the standardized management of three-dimensional data. Then the Mean Shift algorithm is used to preprocess the remote sensing data, and the forest diameter measurement system and transmission line forest clearing program are designed. QDN Power Supply Bureau was selected as a research sample to verify the effectiveness of the application of the above methods. The study showed that the RMSE of the breast diameter monitoring results ranged from 4.30% to 5.05%, and the reduction of forced outage rate of transmission lines of 110kV and above in the power grid could be up to 72.28%, and the overall work efficiency was improved by about 5.14 times. Therefore, actively realizing the optimization of transmission line forest clearing tasks can ensure the stable operation of transmission lines and provide basic support for ensuring the power supply of the grid.

Why it matches plant phenotyping methods傾斜写真測量とMean Shift処理により樹木の胸高直径を推定する測定システムを構築し、RMSEで検証しており、植物形質の取得・評価が実質的な方法貢献として含まれる。

abstractThe article is based on the inclined photogrammetry technology to obtain the three-dimensional data of transmission lines, and construct a real-time database to realize the standardized management of three-dimensional data.

Advancements in 3D Reconstruction for Plant Phenotyping: Technologies, Applications, Challenges, and Future Directions ↗

Recent advancements in 3D reconstruction technologies have significantly transformed plant phenotyping, enabling precise, scalable, and automated trait extraction. Traditional manual phenotyping methods are increasingly being replaced by image-based approaches, such as photogrammetry, LiDAR, RGB-D sensing, and deep learning (DL)-based techniques. These tools allow for non-destructive, high-throughput measurements of plant morphology, structure, and physiological traits. This review synthesizes the state of the art in 3D reconstruction methods, including conventional geometric algorithms and emerging DL methods, and evaluates their application across diverse plant species. In addition, we discuss the sensing modalities, evaluation metrics, and crop-specific deployments. Although promising, current technologies still face challenges in terms of computational efficiency, scalability to outdoor environments, and generalizability across crop types. This review concludes by identifying research gaps and future directions for making real-time, field-deployable 3D phenotyping systems.

Why it matches plant phenotyping methods植物フェノタイピングにおける3D再構成技術と形質抽出を主題とする方法論レビューであり、評価指標やセンサー、応用を体系的に扱っている。

abstractThis review synthesizes the state of the art in 3D reconstruction methods

Sorghum plant height and yield prediction using multispectral data and sUAS ↗

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.

"Fertile islands from above": Utilising UAS imagery to map argan forest patches in a UNESCO biosphere reserve in Morocco ↗

Abstract. Vegetation in dryland ecosystems often exhibits spatial patterning that leads to the formation of fertile islands. Discrete patches accumulate nutrients, organic litter, seeds and water, slow down geomorphodynamic dispersion processes and contrast with rather barren interpatch areas. While this fragmentation shapes surface dynamics in dryland environments across the globe, their spatial arrangement remains difficult to quantify at fine scales. In this study, we utilised data collected by an uncrewed aircraft system (UAS) together with field data to study surface patterns in degraded Argania spinosa forests in South Morocco that show fertile island dynamics. Point clouds generated from UAS imagery using a Structure-from-Motion photogrammetric workflow were classified into vegetation and ground points, allowing the derivation of digital terrain and digital surface models as well as conventional orthophotos and artificial ground-only orthophotos. This high-resolution geospatial data was used to map tree-influenced soil surface areas and crown areas. Their size and spatial relationships were compared and complemented by a detailed assessment of tree morphologies and terrain characteristics based on both field observations and UAS-based geodata. Spatial and statistical analyses were conducted to study the effects of tree morphology, hillslope wash, shading and wind on emerging surface patterns beneath Argania spinosa trees. Across the 496 evaluated tree-influenced areas, the surface influence extended on average to 1.69 times the size of the crown covered area. The extent of this influence beyond the canopy cover is strongly controlled by tree size and morphology, indicating that browsing-induced degradation influences not only tree conditions but also the spatial extent of positive surface effects in the interpatch area. The tree-influenced areas exhibit a consistent north-east displacement, a pattern that surprisingly appears largely decoupled from hillslope wash and is most reasonably explained by a combined influence of shading and wind effects. The results of this study demonstrate the potential of UAS imagery to complement fertile island research by providing spatial insight beyond conventional field-based assessments. At the same time, the impact of browsing on surface dynamics in a UNESCO biosphere reserve is highlighted as a factor contributing to degradation in this silvopastoral land-use system.

Why it matches plant phenotyping methodsUAS-SfM画像から樹冠面積、樹体形態、樹木影響域を抽出するワークフローが研究の中心で、個体レベルの植物形態・状態を定量化しているため。

abstractPoint clouds generated from UAS imagery using a Structure-from-Motion photogrammetric workflow were classified into vegetation and ground points, allowing the derivation of digital terrain and digital surface models as well as conventional orthophotos and artificial ground-only orthophotos.

Advancing 3D radiative transfer of conifers: Evaluation of spruce shoot reflectance modelling with high resolution structural and optical data ↗

Accurately simulating shoot-scale light scattering in physically based radiative transfer models remains a key challenge for conifer ecosystems. This study evaluates the high-resolution three-dimensional (3D) radiative transfer capability of the Discrete Anisotropic Radiative Transfer (DART) model using laboratory reflectance measurements and detailed photogrammetric reconstructions of Norway spruce ( Picea abies (L.) H. Karst) shoots. Samples representing multiple age classes and crown positions were collected from temperate (Czech Republic) and hemiboreal (Estonia) Norway spruce stands. Their geometry was reconstructed with sub-millimetre accuracy using structured blue-light 3D scanning, while the optical properties of needles and twigs were measured using an integrating sphere. We measured shoot reflectance under controlled laboratory illumination and compared it to DART simulations based on the identical 3D structures and optical inputs. DART simulations accurately reproduced the measured spectral signatures (R 2 = 0.95; median spectral angle mapper = 4.8°), demonstrating the model's capacity to simulate shoot-scale reflectance across diverse viewing geometries. These results suggest that detailed 3D shoot representations can improve radiative transfer modelling accuracy, and that DART efficiently simulates shoot reflectance across diverse viewing geometries as an alternative to labour-intensive goniometer measurements. This work provides the first empirical evaluation of DART at the shoot-scale and establishes a transferable framework for integrating detailed 3D photogrammetry into radiative transfer modelling. This approach enables more accurate upscaling from the conifer needle to the canopy-level and can enhance future model intercomparison exercises, such as the Radiation Transfer Model Intercomparison benchmark. • First empirical validation of DART simulation of conifer shoots. • High-resolution blue-light photogrammetry captures realistic shoot architecture. • DART-simulated reflectance closely matches laboratory measurements. • Framework enables realistic needle-to-canopy upscaling in radiative transfer models.

Why it matches plant phenotyping methods針葉樹シュートの3D構造を高精度に取得し、反射率モデルを実測値で検証する技術研究であり、植物形質（シュート構造・反射特性）の取得とモデル評価が中心である。

abstractTheir geometry was reconstructed with sub-millimetre accuracy using structured blue-light 3D scanning

Modelling mountain pine beetle-impacted forest wildland fire fuel distributions from remotely piloted aircraft systems imagery and point clouds ↗

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.

AUTOMATIC OIL PALM TREE COUNTING AND HEIGHT ESTIMATION USING DIRECT GEOREFERENCING PHOTOGRAMMETRY, CANOPY HEIGHT MODELLING, AND TEMPLATE MATCHING ↗

Abstract has not been obtained from indexed metadata or an accessible article page.

Why it matches plant phenotyping methodsオイルパーム個体数と樹高を推定する画像・測量手法が題名の中心であり、植物形質の取得・推定方法に該当する。

titleAUTOMATIC OIL PALM TREE COUNTING AND HEIGHT ESTIMATION USING DIRECT GEOREFERENCING PHOTOGRAMMETRY, CANOPY HEIGHT MODELLING, AND TEMPLATE MATCHING

Non‐Invasive Estimation of Short‐Term Changes of Transpiration Using a Combination of 3D Imaging and Energy Balance Modelling ↗

Conventional approaches to measuring stomatal conductance (gs) and transpiration often rely on instruments that interfere with plant physiology. Porometers, for example, restrict natural leaf movement, apply pressure, and introduce dry airflow that can alter stomatal behaviour, thereby reducing the relevance of such measurements. Prior studies report discrepancies among devices attributable to such interferences (Toro et al. 2019). To minimise artefacts, transpiration should be estimated remotely without physical contact, which theoretically can be achieved via a thermal leaf energy-balance approach that infers gs from leaf temperature, radiative load, and boundary-layer terms. In this study, we combine 3D plant models, light interception models, and thermal imaging to solve the energy-balance equation of individual leaves, estimating transpiration entirely remotely. Approaches to estimate stomatal conductance based on the energy-balance equation were developed recently to aid phenotyping of plantss. Most methods either imposed rapid changes in air humidity to perturb transpiration and, consequently, leaf temperature (Driever et al. 2023), or relied on ‘dry’ and ‘wet’ reference surfaces (as in Leinonen et al. 2006) to compute stress indices (Vialet-Chabrand and Lawson 2020). These methods require reference materials to assess surface temperatures under maximum and zero transpiration, showing the effect of longwave radiation. However, reference-material methods were constrained by heterogeneity in light interception caused by variation in leaf angle and orientation, because reference surfaces could not reorient like real leaves (Zhang et al. 2025). In this study, we addressed this challenge by using thermal imaging and 3D photogrammetry to capture leaf temperature and geometry noninvasively, allowing parameter estimation for each leaf individually. Here, ρ is the density of air (kg m−3), cp is the specific heat capacity of air (J kg−1 K−1) and rHR is the parallel resistance to heat and radiative transfer on the leaf surface (s m−1), s is the slope of the curve relating saturating water vapour pressure to temperature (Pa °C−1). TL and TA are leaf and air temperatures (°C), respectively, δe is air vapour pressure deficit (Pa), γ is the psychrometric constant (Pa K−1) and rva is the boundary layer resistance to water vapour (s m−1) (Supporting Information S2: Equation S1). The net radiative energy Rn in the energy-balance term was obtained from the same 3D light interception model, which integrates measured direct and lateral scattered irradiance (W m−2) (Supplement Material and Methods, File S2). Stomatal conductance gs (m s−1) is the inverse of stomatal resistance rs (s m−1). To experimentally obtain a wide range of gs values, we grew eggplant (Solanum melongena L.) plants in hydroponic units in growth chambers under four sets of environmental conditions (Table 1). Thirty-day-old plants (4–5-leaf stage) were placed on balances (Supplementary Materials and Methods, File S2). Units were sealed with plastic film to minimise evaporation. Mass loss attributable to transpiration was logged automatically every 30 s. To induce short-term changes in stomatal conductance, we imposed an acute osmotic stress by delivering a saline NaCl solution with high electrical conductivity (60 mS cm−1) to the root zone, producing a steep drop in root osmotic potential. This created rapid physiological and morphological responses that altered incident irradiance at the leaves, leaf temperature, and consequently energy balance, stomatal conductance and transpiration. We chose this stressor for operational simplicity. Any perturbation that modifies transpiration dynamics and thus gas exchange could have served our purpose. The total transpiration of a leaf, Et (kg s−1), is the product of the total conductance to water vapour from the mesophyll to the atmosphere, gv (m s−1), calculated from the estimated stomatal resistance rs (s m−1) and the boundary layer conductance gva (m s−1), the difference between water vapour concentration inside the leaf Cvs (dimensionless), and in surrounding air Cva (dimensionless), the leaf area A (m2), and the density of water ρw (kg/m3) (Jones 1992). Estimated stomatal conductance was obtained from leaf energy balance calculation (Equation 1). Boundary-layer conductance was computed from measured wind speed and leaf dimensions (leaf area, length, width) extracted from structure-from-motion 3D reconstructions (Supporting Information S1: Equation S6; Grace et al. 1980). Transpiration was then calculated for each leaf at each thermal 3D imaging time point, and whole-plant transpiration for comparison with gravimetric logs was the sum of all per-leaf estimates. As a non-invasive approach, we evaluated the plausibility or our model derived stomatal conductance (Equation 2) indirectly by comparing calculated and measured whole plant transpiration. We emphasise that this is not a direct validation of gs. Rather, the close agreement between modelled and measured transpiration across the wide range of environmental treatments, both stressed and non-stressed, provides confidence that the inferred gs is realistic. RGB and thermal images acquired before, during, and after stress application enabled dynamic tracking of leaf position and temperature (Supplementary Material and Methods, File S2). As expected, osmotic stress application had immediate effects on morphology and physiology. While control leaves maintained an angle of around 110° throughout, osmotic shock induced immediate turgor loss and drooping in all environments except one (Figure 1A,B). Leaf angles recovered to pre-stress positions within 1 h, indicating adaptation to the osmotic shock and restoration of turgor. Only environment 4 (high light, low air temperature and low humidity) maintained turgor during stress. Angle shifts were most pronounced in older leaves, which drooped and reduced light interception; younger leaves better preserved structure and turgor (Supporting Information S1: Figure S2). These angle changes also altered incident irradiance at the leaf surface (Supporting Information S1: Figure S3). These morphological responses coincided with increases in leaf temperature, consistent with altered water fluxes and stomatal regulation after stress. Across environments, plants showed a uniform rise in leaf temperature following osmotic stress, regardless of initial temperature (Supporting Information S1: Figure S4). This response held across leaf ages, encompassing older (Figure 1C) and younger (Figure 1D) leaves. Stomatal conductance estimated with our method followed the same pattern, dropping rapidly after osmotic shock in both older (Figure 1E) and younger (Figure 1F) leaves (Supporting Information S1: Figure S5). We estimated no stomatal conductance recovery to pre-stress conditions over the time course of stress exposure. Model-estimated and gravimetrically measured transpiration showed identical time courses across all four environmental conditions (Figure 1G–J). Transpiration rates did not recover to the same extent as leaf turgor, indicating long-term effects of the osmotic shock. Across environments and time points, correlation between model estimated and measured whole-plant transpiration was high (Figure 1K). In this study, stomatal conductance (gs) is a model-derived quantity inferred from the same physically constrained framework and model (leaf temperature, boundary-layer conductance and vapour pressure deficit). Since we did not measure gs directly, we cannot validate gs directly. Instead, we used a non-invasive check via transpiration. Model predictions closely tracked measured transpiration across the four controlled environments. This agreement increases confidence that the inferred gs is realistic, while we acknowledge that transpiration agreement alone is not a rigorous validation and cannot fully rule out compensating errors. Our study demonstrated the potential of our approach to estimate transpiration accurately by combining 3D imaging and thermography with physiological modelling without the use of reference materials that imitate real leaves. This remote approach enables simultaneous assessment of morphological and physiological responses to stress, yielding a more integrated view on plant transpiration and gas exchange. In contrast to chamber and porometer measurements or IR methods requiring wet and dry references or calibration plates, our workflow is reference-free. Absorbed shortwave radiation is derived from measured irradiance and a 3D reconstruction of leaf geometry, with no external reference materials. Moreover, remote measurements avoid continuous pressure from clamp-on porometers, permitting long-term observation and capture of rapid stress responses without sustained damage or microclimate artifacts. Further, the approach is not limited by any clamp on sensors and as such enables multi-leaf tracking. Applied to crop canopies, this approach could improve understanding of canopy processes that influence productivity and enable remote estimation of canopy transpiration. Future research could further improve by replacing our strong saline solution stress by gradual soil drying to depict a more realistic and natural stress while testing the approach under long-term conditions. Recent studies indicate that, with rising atmospheric CO2 concentrations, breeding for reduced stomatal conductance could increases WUE without affecting photosynthetic capacity (Srivastava et al. 2024). As such, remote systems for high-throughput plant phenotyping (HTP) are required to scan vast quantities of plants. We see a potential use of our system for such purposes to quickly estimated whole plant and individual leaf transpiration, as initial image capturing is very fast. A large bottleneck in our work was 3D model generation speed and manual extraction of leaf parameters from these 3D models. Both could be streamlined with more automated software, possibly including neural network solutions. The authors have nothing to report. The authors declare no conflict of interest. The data that support the findings of this study are available from the corresponding author upon reasonable request. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Why it matches plant phenotyping methods3D画像、熱画像、光遮断モデル、エネルギーバランスモデルを統合し、葉ごとの蒸散と気孔コンダクタンスを非侵襲的に推定する手法を開発・評価しており、植物表現型取得が研究の中心である。

abstractIn this study, we combine 3D plant models, light interception models, and thermal imaging to solve the energy-balance equation of individual leaves, estimating transpiration entirely remotely.

Turning Mediterranean Farmlands Into Priority Habitats: Natural Expansion of Juniper Woodlands After Agricultural Abandonment ↗

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

Evolution of Forest Tree DBH Measurement Technologies: From Contact-Based Traditional Approaches to Remote Sensing Non-Contact Methods ↗

Diameter at Breast Height (DBH) is a key parameter in forest measurement. However, existing research has mostly focused on improving the accuracy of individual technologies, lacking a systematic synthesis of the evolutionary logic of measurement techniques and a standardized selection framework for forestry applications. To this end, this paper constructs a multi-level classification framework based on measurement platforms and technical principles, establishes for the first time a five-dimensional comprehensive evaluation system (covering accuracy, efficiency, cost, environmental adaptability, and automation) along with a hierarchical technology decision tree, and systematically analyzes the application logic of multi-source fusion technologies across three levels: ground-based, near-ground mobile, and aerial. The review indicates that traditional contact-based measurement has limited efficiency; modern remote sensing technologies (photogrammetry and LiDAR) offer significant advantages in automation and accuracy, but still face challenges such as high equipment costs, complex data processing, and poor environmental adaptability. Multi-source fusion and machine learning are key methods to overcome the limitations of single sensors and improve the robustness of DBH estimation. Finally, it is anticipated that with decreasing sensor costs and the advancement of intelligent algorithms, DBH measurement will continue to evolve toward automation, intelligence, and engineering practicality, providing technical support for large-scale, long-term, and repeatable forest monitoring.

Why it matches plant phenotyping methods森林樹木のDBHという明示的な植物形態形質の測定技術を対象に、測定原理の分類、評価体系、意思決定木、リモートセンシングと機械学習の比較を体系化した方法論レビューであり、フェノタイピング手法が中心である。

abstractDiameter at Breast Height (DBH) is a key parameter in forest measurement.

Exploring the optimal timing for detecting maize growth differences using unmanned aerial vehicle-derived crop surface models ↗

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.

Exploring the optimal timing for detecting maize growth differences using unmanned aerial vehicle-derived crop surface models ↗

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.

Species-specific aboveground biomass estimation using semantic segmentation of UAV photogrammetric point clouds ↗

Accurate, species-specific estimation of aboveground biomass (AGB) at the individual plant level is essential for characterizing forest structure, supporting ecological and wildfire modelling, and enabling fine-scale carbon accounting. This study presents a methodological framework for estimating species-specific AGB at individual plant level in Mediterranean ecosystems using UAV-based digital aerial photogrammetry (UAV-DAP). High-resolution point clouds were processed through a multi-step workflow including object-based segmentation, thirteen species classification and AGB regression modeling. The overall accuracy of species classification across six study areas was 81.6%, with a maximum of 89.9%. The regression models for AGB estimation yielded an average R 2 of 0.69 across all species, highlighting species such as Anthyllis cytisoides (R 2 = 0.83, RMSE = 0.07 kg, n = 47), Juniperus oxycedrus (R 2 = 0.83, RMSE = 3.17 kg, n = 32); or Pinus halepensis (R 2 = 0.77, RMSE = 11.79 kg, n = 20). These findings demonstrate the potential of UAV-DAP for practical estimates of AGB. The study underscores UAV-DAP as a cost-effective tool for forest management, ecological monitoring, and biomass assessments, paving the way for broader applications in environmental science and resource management.

Why it matches plant phenotyping methodsUAV-DAP点群から個体レベルの樹種別地上部バイオマスを推定する画像・計算ワークフローが中心で、セグメンテーション、分類、回帰と精度評価を含むため、植物形質計測手法として適格。

abstractThis study presents a methodological framework for estimating species-specific AGB at individual plant level in Mediterranean ecosystems using UAV-based digital aerial photogrammetry (UAV-DAP).

Exploring the optimal timing for detecting maize growth differences using unmanned aerial vehicle-derived crop surface models ↗

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.

Improving 3D reconstruction quality for root phenotyping: assessing the impact of camera calibration and imaging parameters ↗

Accurate 3D reconstruction is essential for high-throughput plant phenotyping, particularly for studying complex structures such as root systems. While photogrammetry and Structure from Motion (SfM) techniques have become widely used for 3D root imaging, the camera settings used are often underreported in studies, and the impact of camera calibration on model accuracyccu remains largely underexplored in plant science. In this study, we systematically evaluate the effects of focus, aperture, exposure time, and gain settings on the quality of 3D root models made with a multi-camera scanning system. We show through a series of experiments that calibration significantly improves model quality, with focus misalignment and shallow depth of field (DoF) being the most important factors affecting reconstruction accuracy. Our results further show that proper calibration has a greater effect on reducing noise than filtering it during post-processing, emphasizing the importance of optimizing image acquisition rather than relying solely on computational corrections. This work improves the repeatability and accuracy of 3D root imaging for phenotyping pipelines by giving useful calibration guidelines. This leads to better trait quantification for use in crop research and plant breeding in downstream analysis.

Why it matches plant phenotyping methods根の3D画像フェノタイピングにおけるカメラ校正・撮像条件を体系的に評価し、再構成精度と再現性を改善する技術指針を提示しており、フェノタイプ取得手法が研究の中心である。

abstractThis work improves the repeatability and accuracy of 3D root imaging for phenotyping pipelines by giving useful calibration guidelines.

A Reproducible Methodology for 3D Tree-Structure Mensuration and Risk-Oriented Decision Support: Integrating SfM–MVS, Field Referencing, and Rule-Based TRAQ/ALARP Logic ↗

This manuscript presents a transferable and reproducible methodology for quantitative 3D tree-structure mensuration and transparent, rule-based decision support for tree risk management. The workflow integrates (i) Structure-from-Motion/Multi-View Stereo (SfM–MVS) reconstruction from multi-view imagery, (ii) independent referencing to ensure metric scaling and a consistent local frame, and (iii) point cloud analytics to derive branch-level geometric descriptors (e.g., base diameter, length, inclination, slenderness, and projected reach). A clear rule-based layer operationalizes Tree Risk Assessment Qualification (TRAQ)-style risk components and As Low As Reasonably Practicable (ALARP) principles to map geometry and exposure into auditable management recommendations (e.g., monitoring intervals, pruning/weight reduction, supplemental support, and exclusion-zone planning). To provide a real-data example, the demonstration uses the public Fuji-SfM apple orchard dataset, including three neighboring trees with partially overlapping crowns for tree instance extraction and subsequent TRAQ/ALARP scenarios on an outer tree. The proposed decision layer is intentionally based on external geometry and exposure; internal decay indicators and species-specific mechanical properties (e.g., Modulus of Elasticity (MOE), Modulus of Rupture (MOR)) are outside this demonstration and should be incorporated via complementary diagnostics in operational deployments.

Why it matches plant phenotyping methodsSfM–MVSと点群解析による樹木・枝の3D形状形質抽出が中心的な方法論であり、実データでの適用も含むため。

abstractThis manuscript presents a transferable and reproducible methodology for quantitative 3D tree-structure mensuration

Development of a low‐cost 3D imaging system for sorghum root phenotyping ↗

Abstract Root system architecture plays a critical role in water and nutrient acquisition, particularly in semi‐arid environments where drought stress limits crop productivity. Despite advances in three‐dimensional (3D) root phenotyping, no dedicated low‐cost imaging platform currently exists for sorghum ( Sorghum bicolor (L.) Moench) in the United States. The objective of this study was to design and construct an affordable laboratory‐based 3D imaging system for sorghum root phenotyping modeled after the digital imaging of root traits (3D) framework. The system consists of a rotating aluminum frame equipped with eight high‐resolution digital cameras controlled by Raspberry Pi microcomputers, uniform LED lighting, and background reference markers to ensure accurate image alignment. Approximately 2000–3000 overlapping images are captured in under 5 min and processed using structure‐from‐motion algorithms to generate colorized 3D point clouds. The total system cost was approximately $6000, substantially lower than commercial imaging technologies such as computed tomography or magnetic resonance imaging. Initial assembly demonstrated strong geometric alignment and minimal distortion, enabling measurement of key root traits including volume, nodal root angle, and whorl spacing. This platform provides a reproducible and scalable approach for sorghum root phenotyping and addresses a critical gap in crop research tools for semi‐arid production systems. The system also offers educational value by integrating engineering design, programming, and plant science, supporting interdisciplinary training and future genotype‐phenotype studies aimed at improving drought resilience.

Why it matches plant phenotyping methodsソルガム根の形態形質を取得する低コスト3D画像プラットフォームの設計・構築が研究の中心であり、根体積や根角度などの測定法を提供している。

abstractThe objective of this study was to design and construct an affordable laboratory‐based 3D imaging system for sorghum root phenotyping modeled after the digital imaging of root traits (3D) framework.

A method for 3D reconstruction of trees via SfM guidance and depth estimation ↗

Abstract has not been obtained from indexed metadata or an accessible article page.

Why it matches plant phenotyping methods樹木の3D再構成手法を開発する研究であり、植物の構造・形態取得が中心的な方法論的貢献と判断できる。

titleA method for 3D reconstruction of trees via SfM guidance and depth estimation

Automated structural parameter estimation in planted and natural forests using unmanned aerial vehicles and vision foundation models ↗

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.

TreeDGS: Aerial Gaussian Splatting for Distant DBH Measurement ↗

Aerial remote sensing efficiently surveys large areas, but accurate direct object-level measurement remains difficult in complex natural scenes. Advancements in 3D computer vision, particularly radiance field representations such as NeRF and 3D Gaussian splatting, can improve reconstruction fidelity from posed imagery. Nevertheless, direct aerial measurement of important attributes like tree diameter at breast height (DBH) remains challenging. Trunks in aerial forest scans are distant and sparsely observed in image views; at typical operating altitudes, stems may span only a few pixels. With these constraints, conventional reconstruction methods have inaccurate breast-height trunk geometry. TreeDGS is an aerial image reconstruction method that uses 3D Gaussian splatting as a continuous scene representation for trunk measurement. After SfM–MVS initialization and Gaussian optimization, we extract a dense point set from the Gaussian field using RaDe-GS’s depth-aware cumulative-opacity integration and associate each sample with a multi-view opacity reliability score. Then, we isolate trunk points and estimate DBH using opacity-weighted solid-circle fitting. Evaluated on 10 plots with field-measured DBH, TreeDGS reaches 4.79 cm RMSE (about 2.6 pixels at this GSD) and outperforms a LiDAR baseline (7.66 cm RMSE). This shows that TreeDGS can enable accurate, low-cost aerial DBH measurement.

Why it matches plant phenotyping methods樹木の胸高直径（DBH）という植物形態形質を、航空画像から3D再構成と信頼度重み付き推定で測定する手法を開発・検証しており、フェノタイピング手法が中心である。

abstractTreeDGS is an aerial image reconstruction method that uses 3D Gaussian splatting as a continuous scene representation for trunk measurement.

Two-Phase Forest Damage Assessment with Sentinel-2 NDVI Double Differencing and UAV-Based Segmentation in the Sopron Mountains ↗

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.

MSA-MVSNet: A Cross-Scale Collaborative Attention-Based Multi-View Reconstruction Network for Orchard Tree 3D Reconstruction with Instance Segmentation for Fruit Counting ↗

Abstract To address the issues of detail loss and matching difficulties in fruit tree 3D reconstruction caused by complex branch–leaf morphology, fruit occlusion, and illumination variations, this paper proposes an end-to-end cross-scale collaborative attention multi-view stereo network, termed MSA-MVSNet, for high-quality 3D reconstruction of orchard trees, while integrating semantic segmentation for fruit counting. A multi-scale feature enhancement module is designed to adaptively fuse deep semantic features and shallow fine-grained details through a spatial–channel collaborative attention mechanism, thereby enhancing the network’s capability to represent multi-scale structures such as trunks, branches, and leaves. Multi-branch dilated convolutions are introduced to enlarge the receptive field, and deformable convolutions are incorporated to adaptively capture the irregular geometric shapes of fruits, improving modeling robustness. In addition, a feature matching transformer is introduced to strengthen long-range global contextual correlations within and across images via intra-attention and inter-attention mechanisms, thereby improving matching stability in low-texture and repetitive-texture regions.To validate the effectiveness of the proposed method, experiments are conducted on self-collected real orchard dataset and public benchmark datasets. The results demonstrate that MSA-MVSNet outperforms baseline models by 8.2% in terms of 3D reconstruction quality. Finally, by combining depth filtering with the semantic segmentation results of YOLOv11-Seg, a semantic-guided fruit reconstruction and counting framework is constructed. This framework achieves an overall counting F1-score of 92.8% on the self-collected dataset with varying scene sparsity and 93.5% on the public Fuji-sfm dataset, demonstrating its effectiveness and generalization capability.

Why it matches plant phenotyping methods果樹の3D再構成と果実カウントという植物形質取得を目的に、マルチビュー再構成ネットワークとセグメンテーション統合手法を開発・検証しており、フェノタイピング手法が中心である。

abstractthis paper proposes an end-to-end cross-scale collaborative attention multi-view stereo network, termed MSA-MVSNet, for high-quality 3D reconstruction of orchard trees, while integrating semantic segmentation for fruit counting.

Monitoring crop leaf area index using improved global structure-from-motion and multi-feature data fusion on a phenotyping robot ↗

Reconstruction of crop three-dimensional (3D) point clouds is essential for monitoring phenotypic parameters, like plant height and leaf area index (LAI), which is a critical phenotype predictor for smart crop breeding. The main 3D reconstruction technologies include image-based approaches, laser scanning, and depth camera methods. Among these methods, image-based structure-from-motion (SfM) is widely used due to its low cost and high accuracy. However, field crop canopy image data for high-resolution point cloud construction are often large-scale, unordered, and uncalibrated. Conventional SfM methods struggle with 3D reconstruction due to high computational costs and long processing times, delaying phenotypic analysis. To address this issue, we developed an improved global SfM algorithm, which increases the point cloud reconstruction speed by an average of 1.39 times compared to traditional incremental SfM methods and by more than 10 % on average compared to two mainstream global SfM algorithms. In addition, we integrated three types of predictors, point cloud features, color indices and texture features, through multi-feature data fusion and machine learning. A random forest algorithm for the prediction of LAI for a combined data set of four different crops, and using all three categories of predictors, achieved higher monitoring accuracy compared to using a single feature category (R²=0.78 vs R²=0.71–0.74). This new method, which includes an improved global SfM algorithm and a three-predictor fusion-based LAI monitoring approach, offers an efficient and reliable solution for precise crop phenotyping and continuous growth monitoring in complex field environments, enabling accurate assessment of crop morphology and developmental dynamics.

Why it matches plant phenotyping methods改良型SfMによる3D再構成と、特徴量融合・機械学習によるLAI推定を開発しており、植物表現型取得手法が研究の中心である。

abstractwe developed an improved global SfM algorithm

AI-driven 3D point cloud analysis in plant phenotyping: A Systematic Review ↗

Three-dimensional point cloud (3DPC) data capture detailed geometric and structural plant traits beyond the capability of 2D imaging. When combined with artificial intelligence (AI), it offers a powerful, non-invasive tool for plant phenotyping, which is crucial for driving advancements in plant breeding and agriculture. However, challenges related to data complexity, limited datasets, and model generalization hinder 3DPC’s widespread adoption. To provide a comprehensive overview and guide future research in this area, we conducted a systematic literature review (SLR) following Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines by analysing 381 papers published between January 2017 and October 2025 from major databases. Our review examines the advantages, current status, limitations, and future directions of AI applications in 3DPC-based plant phenotyping. Our findings indicate a rapid increase in publications since 2022, with deep learning (DL) methods, especially pointwise MLP-based networks, driving much of this growth, with a notable recent surge in Transformer-based, Graph-based, and particularly Hybrid models that combine their strengths. Furthermore, novel methods like Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) are emerging as powerful tools for 3D reconstruction and scene synthesis. Time-of-Flight (ToF) and Structure from Motion and Multi-View Stereo (SfM-MVS) technologies remain the predominant 3DPC data acquisition techniques. Research in this area focuses on trees/shrubs and cereals, typically involving single-species studies. Although the overall use of public datasets remains low (18.9%), their adoption has significantly increased since 2020. Key limitations identified include: (1) a lack of standardized data collection and formats, (2) insufficient model robustness and generalization, especially from lab to field, (3) high computational demands, and (4) a reliance on species-specific models. The future of AI-driven 3DPC phenotyping hinges on overcoming these bottlenecks. Priority should be given to: developing field-deployable, computationally efficient models; exploring the potential of the foundation model; establishing diverse and standardized public datasets; and strengthening the integration of 3D phenomics with genomics to bridge the genotype-to-phenotype gap. This review provides a foundational roadmap to guide research in plant phenomics, crop breeding, and plant science.

Why it matches plant phenotyping methods3D点群とAIによる植物形質取得・解析を中心に扱う体系的レビューであり、植物フェノタイピング手法のレビューとして明確に適格。

titleAI-driven 3D point cloud analysis in plant phenotyping: A Systematic Review

Monitoring crop leaf area index using improved global structure-from-motion and multi-feature data fusion on a phenotyping robot ↗

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

Sapling-NeRF: Geo-Localised Sapling Reconstruction in Forests for Ecological Monitoring ↗

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.

Sorghum plant height and yield prediction using multispectral data and sUAS ↗

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.

Replicability of Digital Terrain Models and Canopy Height Models Derived from Drone Photogrammetry ↗

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

Machine Learning-Based Analysis of Forest Vertical Structure Dynamics Using Multi-Temporal UAV Photogrammetry and Geomorphometric Indicators ↗

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.

The integration of multi-source 3D data for heritage greenery inventory and monitoring in the Royal Castle in Warsaw ↗

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.

Organ-level 3D phenotyping of saffron using a low-cost dual-camera workflow. ↗

BACKGROUND: Precise, non-destructive phenotyping of saffron during vegetative growth is critical for optimizing corm yield and accelerating breeding programs, yet systematic 3D measurements have remained elusive due to extreme morphological challenges: ultra-narrow leaves, severe mutual occlusion, and prostrate growth architecture. Traditional single-view imaging systems fail to resolve individual leaves under such conditions, limiting phenotypic analysis to whole-canopy descriptors. Here, we developed a specialized organ-level 3D phenotyping workflow specifically designed for narrow, overlapping leaves using a low-cost dual-camera rotary acquisition system integrated with open-source Structure-from-Motion Multi-View Stereo (SfM-MVS) reconstruction. RESULTS: > 0.94, MAPE < 6%), achieving accuracy benchmarks established for broad-leaved crops using commercial-grade hardware at 100 × lower cost. Systematic voxel sensitivity analysis across nine scales identified optimal preprocessing parameters (2 cm voxel size) balancing measurement precision with computational efficiency, addressing a critical reproducibility gap in plant phenotyping. Exploratory longitudinal tracking revealed that above-ground biomass was correlated with final corm yield (r = 0.68, P < 0.001), with mid-vegetative canopy volume also showing strong correlation (r = 0.52, P < 0.01), suggesting potential resource allocation trade-offs between vegetative expansion and storage organ development. CONCLUSIONS: This work demonstrates that organ-level 3D phenotyping of narrow, overlapping leaves is achievable using low-cost imaging hardware and transparent methodological workflows. Complete documentation of algorithmic parameters and hardware specifications enables direct replication and adaptation to other narrow-leaved crops (wheat, rice, onion, leek), democratizing access to high-throughput phenotyping in resource-limited settings. The workflow advances plant phenomics by demonstrating that methodological transparency and cost-effectiveness need not compromise measurement precision, opening new avenues for phenotype-to-genotype mapping and predictive breeding in underutilized crops.

Why it matches plant phenotyping methods低コストの双眼カメラとSfM-MVSによるサフラン葉の器官レベル3D形質取得ワークフローを開発し、精度検証、再現性、パラメータ最適化まで扱っており、植物フェノタイピング手法が研究の中心である。

abstractHere, we developed a specialized organ-level 3D phenotyping workflow specifically designed for narrow, overlapping leaves using a low-cost dual-camera rotary acquisition system integrated with open-source Structure-from-Motion Multi-View Stereo (SfM-MVS) reconstruction.

Advancements in 3D field-crop phenotyping using point clouds: a comparative review of sensor technology, target traits, and challenges under controlled and field conditions ↗

3D phenotyping refers to the quantitative characterization of a plant's structural and morphological traits in three-dimensional space, allowing for a detailed analysis of plant architecture and growth patterns. In recent years, rapid advancements in non-destructive, high-throughput 3D imaging technologies have enabled the precise measurement of these traits. Initially focused on single-plant traits under controlled conditions, the field has now expanded towards robust applications in real-world field environments, enabling large-scale analyses of plant canopies and complex structures. This study focuses on the recent advancements in 3D crop phenotyping using point cloud technologies. It compares sensor technology and its application in controlled environments (Chamber-Crop Phenotyping, CCP) and field conditions (Field-Crop Phenotyping, FCP). Technologies such as Multiview stereo (MVS) reconstruction, LiDAR, and laser triangulation have enhanced plant phenomics by enabling high-throughput, non-destructive measurements of key traits such as canopy structure, leaf area, and stem diameter. This review highlights the strengths of the CCP, where environmental variables and flexibility are tightly controlled, facilitating precise trait measurement, and contrasts it with the challenges of the FCP, where unpredictable factors, such as occlusion, wind, light variability, and terrain complexity, complicate data acquisition. Various sensor platforms, including ground-based robotic systems and unmanned aerial vehicles (UAVs), have been discussed regarding their ability to overcome occlusion and limited sensor range in real-world conditions. The need to transition these technologies from laboratory environments to real-world agricultural applications is emphasized, highlighting their potential to improve crop management and plant breeding through accurate phenotypic trait extraction. Finally, current research gaps and future directions for integrating advanced sensor platforms and analytical techniques in both CCP and FCP settings are identified, emphasizing the need to enhance the scalability and robustness of 3D phenotyping for field applications.

Why it matches plant phenotyping methods3D作物フェノタイピングのセンサー技術、点群処理、対象形質、検証上の課題を中心に扱う方法論レビューであり、植物形質の取得手法が明確に中心である。

abstractThis study focuses on the recent advancements in 3D crop phenotyping using point cloud technologies.

Feasibility assessment of aboveground biomass and carbon stock estimation in heterogeneous tropical campus green spaces using unmanned aerial vehicle RGB photogrammetry ↗

Abstract has not been obtained from indexed metadata or an accessible article page.

Why it matches plant phenotyping methodsUAV RGBフォトグラメトリを用いて植物群落の地上部バイオマスを推定する技術評価であり、植物形質の取得・推定手法が中心である。

titleFeasibility assessment of aboveground biomass and carbon stock estimation in heterogeneous tropical campus green spaces using unmanned aerial vehicle RGB photogrammetry

DendRobot: 2D-based tree-detection from LiDAR and photogrammetric point clouds of forest environments ↗

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.

3D crop reconstruction: A review of hyperspectral and multispectral approaches ↗

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

A pixel-aligned co-registration and DSM-grid fusion framework for UAV multispectral and thermal imagery and point-cloud data: 3D Characterization of crop canopy water status ↗

Accurate estimation of crop water status is essential for monitoring plant senescence and enabling intelligent agricultural management. This study proposes a pixel-aligned co-registration and DSM-grid fusion framework that integrates high-resolution point clouds, multispectral (MS) images, and thermal imagery acquired by Unmanned Aerial Vehicles (UAVs) to enable three-dimensional prediction and visualization of cotton canopy leaf water content (LWC) and equivalent water thickness (LEWT). To address the low spatial resolution of thermal imagery, a downsampling–upsampling simulation framework was developed to evaluate interpolation errors. This framework quantitatively compares three common interpolation methods—nearest neighbor, bilinear, and bicubic interpolation—using RMSE and PSNR metrics. Results show that bicubic interpolation performs best in preserving spatial details and minimizing errors, and is therefore adopted in the subsequent image fusion process. A 3D grid was constructed based on the digital surface model (DSM), enabling grid-cell (pixel-aligned) spectral and thermal features to be mapped onto point-cloud units. Vegetation and thermal indices extracted from the mapped features were used as input variables. Combined with recursive feature elimination (RFE) and random forest (RF) models, the prediction of LEWT and LWC achieved R² values of 0.792 and 0.752, and rRMSE values of 13.84% and 9.68%, respectively. These results significantly outperformed those of partial least squares regression (PLSR), support vector machine (SVM), and extreme learning machine (ELM) models. By integrating the predicted results with the point cloud data, a 3D representation of canopy water parameters was constructed, revealing a typical top-down gradient of water loss. The experiment also revealed that nitrogen treatment significantly influenced the vertical distribution of water content. High-nitrogen application delayed water loss in the middle and lower canopy layers, highlighting the coupled regulation between nitrogen and water. Parameter comparisons showed that LEWT exhibited higher sensitivity than LWC across both temporal and spatial scales, making it a more robust indicator for canopy water monitoring. Additionally, point clouds generated from Cross-circling oblique (CCO) photogrammetry outperformed UAV LiDAR systems in terms of point density, structural completeness, and image fusion potential. In summary, this study validated the feasibility and effectiveness of integrating point cloud, MS, and thermal imagery via the proposed pixel-aligned co-registration and DSM-grid fusion framework for 3D crop water monitoring. The proposed method provides a reliable technical foundation for drought detection, irrigation management, and yield prediction in precision agriculture.

Why it matches plant phenotyping methodsUAVマルチスペクトル・熱画像・点群を融合し、綿花の葉水分状態を3D推定・可視化する手法の開発と検証が研究の中心である。

abstractThis study proposes a pixel-aligned co-registration and DSM-grid fusion framework that integrates high-resolution point clouds, multispectral (MS) images, and thermal imagery acquired by Unmanned Aerial Vehicles (UAVs) to enable three-dimensional prediction and visualization of cotton canopy leaf water content (LWC) and equivalent water thickness (LEWT).

3D crop reconstruction: A review of hyperspectral and multispectral approaches ↗

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

Dynamic light intensity improves light use efficiency of lettuce in vertical farming: quantifying light interception through 3D phenotyping analysis ↗

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.

Grapevine Canopy Volume Estimation from UAV Photogrammetric Point Clouds at Different Flight Heights ↗

Vegetation volume is a useful indicator for assessing canopy structure and supporting vineyard management tasks such as foliar applications and canopy management. The photogrammetric processing of imagery acquired using unmanned aerial vehicles (UAVs) enables the generation of dense point clouds suitable for estimating canopy volume, although point cloud quality depends on spatial resolution, which is influenced by flight height. This study evaluates the effect of three flight heights (30 m, 60 m, and 100 m) on grapevine canopy volume estimation using convex hull, alpha shape, and voxel-based models. UAV-based RGB imagery and field measurements were collected during three periods at different phenological stages in an experimental vineyard. The strongest agreement with field-measured volume occurred at 30 m, where point density was highest. Envelope-based methods showed reduced performance at higher flight heights, while voxel-based grids remained more stable when voxel size was adapted to point density. Estimator behavior also varied with canopy architecture and development. The results indicate appropriate parameter choices for different flight heights and confirm that UAV-based RGB imagery can provide reliable grapevine canopy volume estimates.

Why it matches plant phenotyping methodsUAV画像からブドウ樹のキャノピー体積を推定する手法を、飛行高度・推定モデル間で評価し、実測値と比較しているため、植物形質取得法の技術的検証が中心です。

abstractThis study evaluates the effect of three flight heights (30 m, 60 m, and 100 m) on grapevine canopy volume estimation using convex hull, alpha shape, and voxel-based models.

Photogrammetric software performance for estimating maize above-ground biomass using UAVs ↗

Estimating maize biomass is a manual, destructive method subject to variability. The use of unmanned aerial vehicles (UAVs) allows for the acquisition of high-resolution images of crop canopies and, through software, facilitates the estimation of above-ground biomass. The objective was to evaluate the performance of the photogrammetric software Agisoft Metashape and Pix4Dmapper in estimating above-ground maize biomass under field conditions. The experimental design adopted was a randomized complete block design (RCBD) with three replications, and the treatments consisted of eight maize hybrids (2A510 PW, B2360 PWU, B2433 PWU, B2612 PWU, B2688 PWU, CD3410 PW, DKB255 PRO3, DKB363 PRO3). The Agisoft Metashape (R2 = 0.89) and Pix4Dmapper (R2 = 0.82) software demonstrated high experimental precision in estimating above-ground biomass. The maize hybrids DKB255 PRO3 and B2433 PWU showed the highest and lowest biomass productivity, respectively. Integrating UAVs with photogrammetric techniques proves effective in estimating maize biomass under field conditions.

Why it matches plant phenotyping methodsUAV画像とフォトグラメトリックソフトウェアによるトウモロコシ地上部バイオマス推定という植物形質取得法を、複数ソフトウェアで性能評価しており、方法が研究の中心です。

abstractThe objective was to evaluate the performance of the photogrammetric software Agisoft Metashape and Pix4Dmapper in estimating above-ground maize biomass under field conditions.

Tracking Recovery: Temporally-Matched 3D Gaussian Splatting of Ecosystems after Prescribed Burns ↗

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.

Effects of Flight and Processing Parameters on UAS Image-Based Point Clouds for Plant Height Estimation ↗

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.

Sustainable Estimation of Tree Biomass and Volume Using UAV Imagery: A Comprehensive Review ↗

Accurate estimation of tree biomass and volume is essential for sustainable forest management, climate change mitigation, and ecosystem service assessment. Recent advances in unmanned aerial vehicle (UAV) technology enable the acquisition of ultra-high-resolution optical and three-dimensional data, providing a resource-efficient alternative to traditional field-based inventories. This review synthesizes 181 peer-reviewed studies on UAV-based estimation of tree biomass and volume across forestry, agricultural, and urban ecosystems, integrating bibliometric analysis with qualitative literature review. The results reveal a clear methodological shift from early structure-from-motion photogrammetry toward integrated frameworks combining three-dimensional canopy metrics, multispectral or LiDAR data, and machine learning or deep learning models. Across applications, tree height, crown geometry, and canopy volume consistently emerge as the most robust predictors of biomass and volume, enabling accurate individual-tree and plot-level estimates while substantially reducing field effort and ecological disturbance. UAV-based approaches demonstrate particularly strong performance in orchards, plantation forests, and urban environments, and increasing applicability in complex systems such as mangroves and mixed forests. Despite significant progress, key challenges remain, including limited methodological standardization, insufficient uncertainty quantification, scaling constraints beyond local extents, and the underrepresentation of biodiversity-rich and structurally complex ecosystems. Addressing these gaps is critical for the operational integration of UAV-derived biomass and volume estimates into sustainable land management, carbon accounting, and climate-resilient monitoring frameworks.

Why it matches plant phenotyping methodsUAV画像・3Dデータによる樹木のバイオマス、体積、樹高、樹冠形状などの植物形質推定手法を181研究から体系的にレビューしており、フェノタイピング手法が中心です。

abstractThis review synthesizes 181 peer-reviewed studies on UAV-based estimation of tree biomass and volume across forestry, agricultural, and urban ecosystems, integrating bibliometric analysis with qualitative literature review.

A review of forest biomass assessments based on remote sensing reveals progress in methodological quality—but major challenges remain ↗

Abstract Reliable forest biomass assessments are becoming increasingly important, as Parties to the Climate Convention are required to report changes in multiple carbon pools, including both above- and belowground biomass. In some regions, use of remote sensing is the only viable option for obtaining such estimates, whereas in other regions it bears potential to improve the accuracy of ground inventory-based biomass estimates. However, statistically rigorous estimation through remote sensing poses several challenges. This study systematically and comprehensively reviews the methodological quality of large-area biomass assessment studies from 1992 to 2022, based on core survey elements for successful biomass surveying assisted by remote sensing. For each element, we reviewed the studies in relation to “ideal standards” derived from the literature, which served as evaluation criteria. Our review revealed an increasing trend in use of remote sensing for biomass surveys, coupled with gradual improvements in methodological quality for all survey elements evaluated. For example, advances in remote sensing techniques, particularly the increased use of Light Detection and Ranging, Radio Detection and Ranging, and digital aerial photogrammetry, all technologies able to capture information on forest structure, have enhanced the reliability of biomass estimates. However, several problems remain, such as field data scarcity for model calibration, signal saturation in high-biomass regions, and misconceptions about the use of statistical methods. We identified five remaining key challenges for improving remote sensing assisted large-area biomass assessments. These include (i) obtaining sensor data that correlate stronger with biomass, (ii) acquiring larger sets of harmonized field data at the level of trees and plots for calibrating models, (iii) adequate use of statistical principles, (iv) developing methods for domain estimation, and (v) improved quality assurance and quality control. While upcoming new airborne technologies and satellite missions may mitigate some challenges, continued methodological innovation and further enhancement of the rigor of statistical and other procedures will remain essential for advancing remote sensing-based biomass assessments.

Why it matches plant phenotyping methods森林の植物バイオマスという明示的な形質を対象に、リモートセンシングによる推定手法の方法論的品質、校正、統計、精度向上を体系的に評価しており、単なるバイオマス測定の報告ではない。

abstractThis study systematically and comprehensively reviews the methodological quality of large-area biomass assessment studies from 1992 to 2022, based on core survey elements for successful biomass surveying assisted by remote sensing.

The application of intra-canopy photogrammetry for assessing crown health attributes in sugar maple ( Acer saccharum Marsh.) ↗

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.

Intelligent Evaluation of Rice Resistance to White-Backed Planthopper (Sogatella furcifera) Based on 3D Point Clouds and Deep Learning ↗

Accurate assessment of rice resistance to Sogatella furcifera (Horváth) is essential for breeding insect-resistant cultivars. Traditional assessment methods rely on manual scoring of damage severity, which is subjective and inefficient. To overcome these limitations, this study proposes an automated resistance evaluation approach based on multi-view 3D reconstruction and deep learning–based point cloud segmentation. Multi-view videos of rice materials with different resistance levels were collected over time and processed using Structure from Motion (SfM) and Multi-View Stereo (MVS) to reconstruct high-quality 3D point clouds. A well-annotated “3D Rice WBPH Damage” dataset comprising 174 samples (15 rice materials, three replicates each, 45 pots) was established, where each sample corresponds to a reconstructed 3D point cloud from a video sequence. A comparative study of various point cloud semantic segmentation models, including PointNet, PointNet++, ShellNet, and PointCNN, revealed that the PointNet++ (MSG) model, which employs a Multi-Scale Grouping strategy, demonstrated the best performance in segmenting complex damage symptoms. To further accurately quantify the severity of damage, an adaptive point cloud dimensionality reduction method was proposed, which effectively mitigates the interference of leaf shrinkage on damage assessment. Experimental results demonstrated a strong correlation (R2 = 0.95) between automated and manual evaluations, achieving accuracies of 86.67% and 93.33% at the sample and material levels, respectively. This work provides an objective, efficient, and scalable solution for evaluating rice resistance to S. furcifera, offering promising applications in crop resistance breeding.

Why it matches plant phenotyping methods3D画像再構成と深層学習によってイネの害虫被害症状・被害重症度を定量化する手法を開発・検証しており、植物表現型取得が研究の中心である。

abstractthis study proposes an automated resistance evaluation approach based on multi-view 3D reconstruction and deep learning–based point cloud segmentation.

An Efficient Additive Kolmogorov-Arnold Transformer for Point-Level Maize Localization in Unmanned Aerial Vehicle Imagery ↗

High-resolution UAV photogrammetry has become a key technology for precision agriculture, enabling centimeter-level crop monitoring and point-level plant localization. However, point-level maize localization in UAV imagery remains challenging due to (1) extremely small object-to-pixel ratios, typically less than 0.1%, (2) prohibitive computational costs of quadratic attention on ultra-high-resolution images larger than 3000 x 4000 pixels, and (3) agricultural scene-specific complexities such as sparse object distribution and environmental variability that are poorly handled by general-purpose vision models. To address these challenges, we propose the Additive Kolmogorov-Arnold Transformer (AKT), which replaces conventional multilayer perceptrons with Pade Kolmogorov-Arnold Network (PKAN) modules to enhance functional expressivity for small-object feature extraction, and introduces PKAN Additive Attention (PAA) to model multiscale spatial dependencies with reduced computational complexity. In addition, we present the Point-based Maize Localization (PML) dataset, consisting of 1,928 high-resolution UAV images with approximately 501,000 point annotations collected under real field conditions. Extensive experiments show that AKT achieves an average F1-score of 62.8%, outperforming state-of-the-art methods by 4.2%, while reducing FLOPs by 12.6% and improving inference throughput by 20.7%. For downstream tasks, AKT attains a mean absolute error of 7.1 in stand counting and a root mean square error of 1.95-1.97 cm in interplant spacing estimation. These results demonstrate that integrating Kolmogorov-Arnold representation theory with efficient attention mechanisms offers an effective framework for high-resolution agricultural remote sensing.

Why it matches plant phenotyping methodsUAV画像から個体位置を抽出する手法を開発し、個体数と株間距離という植物群落形質を推定しており、データセット構築と技術評価も中心的である。

abstractTo address these challenges, we propose the Additive Kolmogorov-Arnold Transformer (AKT)

Voxel-Based Leaf Area Estimation in Trellis-Grown Grapevines: A Destructive Validation and Comparison with Optical LAI Methods ↗

This study develops a voxel-based leaf area estimation framework and validates it using a three-year multi-temporal dataset (2022–2024) of pergola-trained grapevines. The workflow integrates 2D image analysis, ExGR-based leaf segmentation, and 3D reconstruction using Structure-from-Motion (SfM). Multi-angle canopy images were collected repeatedly during the growing seasons, and destructive leaf sampling was conducted to quantify true leaf area across multiple vines and years. After removing non-leaf structures with ExGR filtering, the point clouds were voxelized at a 1 cm3 resolution to derive structural occupancy metrics. Voxel-based leaf area showed strong within-vine correlations with destructively measured values (R2 = 0.77–0.95), while cross-vine variability was influenced by canopy complexity, illumination, and point-cloud density. In contrast, optical LAI tools (DHP and LAI–2000) exhibited negligible correspondence with true leaf area due to multilayer occlusion and lateral light contamination typical of pergola systems. This expanded, multi-year analysis demonstrates that voxel occupancy provides a robust and scalable indicator of canopy structural density and leaf area, offering a practical foundation for remote-sensing-based phenotyping, yield estimation, and data-driven management in perennial fruit crops.

Why it matches plant phenotyping methodsブドウ樹の葉面積・樹冠構造を推定する画像解析、SfM、ボクセル化ワークフローを開発し、破壊測定および既存LAI手法と比較検証しており、植物表現型取得法が研究の中心である。

abstractThis study develops a voxel-based leaf area estimation framework and validates it using a three-year multi-temporal dataset (2022–2024) of pergola-trained grapevines.

Combining UAV-SfM, SAR, MSI and field surveys for estimation of above ground biomass in mangrove forest of Chonburi, Thailand ↗

Mangrove biomass is a key indicator for quantifying carbon cycling in blue-carbon ecosystems, yet conventional approaches face significant challenges. To improve large-scale mangrove biomass assessment and provide a baseline for targeted conservation, present study proposes a Single-tree-Plot-Community-Region (AGB T/F~U~S ) upscaling method that integrates UAV-SfM, SAR, MSI, and field surveys, and applies it to Chonburi, Thailand. In 2023, total mangrove aboveground biomass in Chonburi Province was 145.24 kt, with a mean AGB density of 101.61 Mg/ha, slightly below the global mangrove average. Long-term records reveal an initial decline followed by post-2015 recovery to about 85% of the 1996 level. Relative to the conventional plot-satellite model, the AGB T/F~U~S framework substantially improves estimation performance and reduces prediction error (ΔR²≈0.47; ΔRMSE ≈ 66.03 Mg/ha), and remains robust under limited training data, with accuracy gains saturating once plot numbers exceed a moderate threshold. These results demonstrate that multi-scale upscaling provides a transferable pathway for mangrove biomass mapping in data-scarce regions and offers a practical baseline for blue-carbon accounting and targeted restoration planning.

Why it matches plant phenotyping methodsUAV-SfM、SAR、MSIと現地調査を統合し、マングローブの地上部バイオマスを推定するマルチスケール手法を開発・比較評価しており、植物形質の取得・推定が研究の中心です。

abstractpresent study proposes a Single-tree-Plot-Community-Region (AGB T/F~U~S ) upscaling method that integrates UAV-SfM, SAR, MSI, and field surveys

Plant-to-camera enabled 3D morphological reconstruction: A high-fidelity approach for plant phenotyping ↗

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

3D Reconstruction of deciduous Trees using low-cost UAV- and Crane-based Photogrammetry for Monitoring Shoot Elongation across entire Canopies ↗

Tree growth determines how much CO2 is sequestered from the atmosphere and temporarily stored in woody biomass. At the same time tree growth is affected by increasing temperatures, more frequent drought periods, late frosts and other extreme events associated with climate change. While continuous measurements of radial (secondary) tree growth using dendrometers are well established, monitoring of shoot elongation (primary growth) has largely been neglected because suitable measurement techniques are lacking. As a result, the effects of climate change on primary tree growth remain insufficiently understood. This work aims at reconstructing native deciduous trees in 3D as a basis for measuring and monitoring shoot elongation over entire tree canopies. Here we explored the use of low-cost UAV photogrammetry and of a multi-camera CraneCam system under real-world conditions. Data were collected in two study areas over an entire growing season. We present sensor evaluations, photogrammetric data acquisition and processing strategies. A special focus is placed on the analysis of the resulting photogrammetric 3D point clouds in terms of accuracy, resolution and completeness. Results demonstrate 3D point accuracies of 5-6 mm for entire trees using consumer-grade UAVs weighing less than 250 g and a 3D reconstruction completeness between 92% and 98% depending on the UAV type. The paper introduces a novel 3Dprinted ground-truth branch to evaluate the capability to reconstructing fine-detail structures such as thin tree shoots. Finally, we discuss operational challenges and initial experiments towards a skeletonization of entire trees based on photogrammetric point clouds.

Why it matches plant phenotyping methodsUAVおよびマルチカメラによる3D再構成を開発・評価し、樹冠全体のシュート伸長という植物形質の測定に用いる方法が中心である。

abstractThis work aims at reconstructing native deciduous trees in 3D as a basis for measuring and monitoring shoot elongation over entire tree canopies.

High throughput phenotyping of energy cane using uncrewed aircraft system (UAS) and machine learning ↗

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).

A novel three-dimensional canopy photosynthesis model for tea plant: integrating point cloud deep learning and ray tracing to optimize photosynthetic efficiency ↗

The tea plant (Camellia sinensis) is economically and nutritionally important because of its bioactive compounds. Photosynthesis directly affects tea's growth and productivity, requiring a detailed study of its relationship with cultivation outcomes. We developed a novel computational pipeline for constructing three-dimensional (3D) canopy photosynthesis models of tea plant, leveraging multi-view stereo 3D reconstruction. The ISBNet architecture was optimized for precise leaf–stem segmentation from point cloud data, achieving 0.897 average precision (AP) for leaves and 0.793 AP for stems. We then created a plant leaf morphology-adapted meshing algorithm optimized for plant leaf morphology, achieving an average mesh reduction of approximately 96% while maintaining morphological fidelity compared with conventional meshing methods. We generated multiple tea plant canopies representing distinct planting patterns, and used a ray tracing algorithm to simulate the spatiotemporal distribution of light within these structures. Canopy photosynthesis simulation revealed significant cultivar-specific differences, with 'Yuehuang 1' exhibiting the highest photosynthetic activity. Dense planting (10 cm spacing) significantly enhanced canopy photosynthetic rates compared with wider spacing (20 cm), and a strong linear correlation (r = 0.99) was identified between total leaf area and daily canopy photosynthetic rate across cultivars. This work establishes a methodological foundation for precision agriculture optimization in perennial crops, providing quantitative guidance for maximizing tea plantations' productivity through optimal cultivar selection and spatial configuration.

Why it matches plant phenotyping methods茶樹キャノピーの3D再構築、葉・茎セグメンテーション、形態適応メッシュ化、光線追跡による光合成推定を統合した方法開発が中心であり、植物形態・光合成状態の定量化に直接つながる。

abstractWe developed a novel computational pipeline for constructing three-dimensional (3D) canopy photosynthesis models of tea plant, leveraging multi-view stereo 3D reconstruction.

Advanced workflows for UAV-based crop height estimation using structure from motion (SfM) point clouds ↗

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

A 3D–2D Dual-Modal Collaborative Framework Based on UAV Oblique Photogrammetry for Automated Measurement of Canopy Volume and Porosity in Banana Plantations ↗

Banana plant canopies exhibit pronounced three-dimensional heterogeneity due to their large, sparse, and overlapping leaves. Under dense planting and severe occlusion, conventional single-modality approaches commonly suffer from insufficient information and limited recognition accuracy, highlighting the necessity of cross-modal complementarity and collaborative modeling. This study proposes a 3D-2D dual-modal collaborative framework based on low-cost UAV oblique imagery to enable end-to-end estimation of canopy volume and porosity in banana plantations. The framework integrates a task-oriented YOLO-SPES model to improve the detection of irregular and overlapping canopies and combines it with the SoftGroup instance segmentation model for three-dimensional structural extraction. At the data level, a cross-modal coordinate interaction strategy (PCI-LLCM) is introduced to achieve precise alignment between point clouds and orthomosaics. At the structural level, a consistent indexing scheme between 3D instances and 2D detection boxes is established, upon which a 3D-2D collaborative modeling algorithm (SGP-YS DMCA) and a multi-scale volume differencing algorithm (MSVDA) are developed for canopy volume and porosity estimation. At the decision level, an adaptive canopy volume completion module (CVC-AOML) leverages 2D detection information to correct and supplement errors and omissions in 3D segmentation, thereby ensuring the accuracy and completeness of large-scale automated measurements. In addition, the coupling performance of multiple geometric algorithms and collaborative models is systematically evaluated. Experimental results demonstrate that the proposed dual-modal collaborative framework achieves a coefficient of determination (R$^{2}$) of 0.885 for canopy volume estimation, representing an average improvement of 0.15 over single-modality baseline methods, with a corresponding mean absolute percentage error (MAPE) of 6.4%. For canopy porosity estimation, an R$^{2}$of 0.65 is obtained with a MAPE of 1.05%. These results not only overcome the limitations of single-modality approaches in phenotypic analysis of complex banana canopies but also provide a low-cost and scalable solution for large-scale agricultural monitoring and precision management of tropical fruit crops.

Why it matches plant phenotyping methodsUAV画像・点群を用いてバナナの樹冠体積と多孔性という明示的な植物形質を自動推定する3D–2D手法を開発し、ベースライン比較と精度評価を行っているため、フェノタイピング手法が中心である。

abstractThis study proposes a 3D-2D dual-modal collaborative framework based on low-cost UAV oblique imagery to enable end-to-end estimation of canopy volume and porosity in banana plantations.

Integrated assessment of tree health and structural stability using UAV multispectral imaging, 3D photogrammetry, VTA and acoustic tomography: A case study of a lime tree ↗

Accurate assessment of the physiological and mechanical condition of trees in urban environments represents a key component of risk management and the planning of protection measures. This study presents the integration of four methodological approaches - multispectral UAS (drone) analysis, a photogrammetrically generated 3D model, Visual Tree Assessment (VTA) and acoustic tomography (Arbotom) - applied to an old lime tree (Tilia platyphyllos) located in the courtyard of the Bishop's Palace of the Šabac Eparchy. Multispectral analysis was used to calculate the NDRE index of physiological activity, while the 3D trunk model was employed for precise positioning of the Arbotom sensors. Tomographic measurements performed at heights of 40 cm and 200 cm identified degradation zones with a reduction in load-bearing cross-sectional area of 27-39% (lower section) and 43-52% (upper section). The NDRE index indicated localized areas of reduced physiological activity within the crown, while the VTA method confirmed the presence of fungi of the genus Ganoderma. The integrated results indicate that the tree currently maintains a stable mechanical structure, with localized degradation zones that do not yet affect its static stability. The presented multi-sensor approach is highlighted as an efficient tool for detection, evaluation, and risk management in urban forestry, however as this research was conducted on a single Tillia platyphyllos specimen, the findings should be interpreted as a case study and methodological demonstration rather than as results directly generalizable to a broader population of urban trees.

Why it matches plant phenotyping methods樹木の生理状態と構造安定性を推定するため、UAVマルチスペクトル画像、3Dフォトグラメトリ、音響トモグラフィーなどを統合した測定手法が中心であり、方法論的実証として報告されている。

abstractThis study presents the integration of four methodological approaches - multispectral UAS (drone) analysis, a photogrammetrically generated 3D model, Visual Tree Assessment (VTA) and acoustic tomography (Arbotom) - applied to an old lime tree (Tilia platyphyllos) located in the courtyard of the Bishop's Palace of the Šabac Eparchy.

A Novel 3D Imaging Approach to Study Evolutionary Innovations in Solanaceae Inflorescences ↗

Understanding the relatedness of angiosperms and the evolution of their inflorescence remains challenging, as these structures are highly modified and prone to convergent evolution. The current research describing inflorescence architecture, whether genetically or morphologically focused, typically relies on text based explanations, 2D images, or 3D based models created with CAD modeling software. This creates a disparity in the reader's understanding of these models since descriptions rely heavily on the author's interpretation of the inflorescence. The goal of our study is to bridge this disconnection by producing anatomically and color-correct 3D inflorescence models using Solanaceae flowers that readers can explore directly, which will allow readers to view and rotate the inflorescence structure in real time. The Solanaceae clade serves as an excellent platform to demonstrate this concept, as it is an active area of floral research and exhibits high inflorescence diversity within the clade. Its ancestral scorpioid cyme-like morphology is presently thought to have been the result of convergent evolution from a currently unknown driving force. Developing 3D models of extant Solanaceae species could provide valuable insights into these evolutionary patterns and help clarify the mechanisms underlying inflorescence diversification. Here, we use a novel photogrammetry approach to create 3D renderings of the inflorescences of Juanulloa sp., in the Solanaceae clade. This process will involve taking high-resolution 360° photos at various angles using a camera. Photos will be processed with Agisoft Metashape software, which generates 3D models using photographs. It is expected that the rendered 3D images will accurately reflect specimen dimensions with precise color. This will serve as a way to study and provide a larger 3D inflorescence library that can bridge the gap between authors and readers within the literature.

Why it matches plant phenotyping methodsSolanaceaeの花序形態を対象に、フォトグラメトリと3D再構成による植物形質の取得・可視化手法を開発しており、方法が研究の中心である。

abstractHere, we use a novel photogrammetry approach to create 3D renderings of the inflorescences of Juanulloa sp., in the Solanaceae clade.

Evaluation of Unmanned Aerial Vehicle-Based Structure-from-Motion (UAV-SfM)-Derived Plant Height for Yield Estimation and Individual Selection in Cool-Season Grass Breeding ↗

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

Estimation of Carbon Storage Based on AAV Multispectral Photogrammetry: An Offshore Islands Study in Pearl River Delta ↗

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

Broad-scale monitoring of canopy structure with multi-source remote sensing and alternative reference data ↗

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

Chapter Four - Operational and analytical frameworks of Unoccupied Aerial Vehicles (UAV) for precision agriculture applications ↗

Agricultural systems are entering an era defined not just by mechanization but by real-time, spatially aware, data driven production practices. This shift which has rapidly evolved from novelty to necessity in precision agriculture. At the center of this shift Unoccupied Aerial Vehicles (UAV) or drones have operationalized high-resolution aerial data into sustainable agricultural outcomes. This chapter provides an application-focused roadmap for integrating UAV into modern agronomic workflows as pre, during, and post flight operations for agronomic flight planning. It bridges the engineering of flight platforms and sensors with the applications in crop stress detection, variable-rate input application, and predictive yield modeling. We explore UAV architecture and their implications for data resolution, field scale, and operational complexity. Sensor systems use the portions of electromagnetic spectrum (RGB, multispectral, hyperspectral, thermal, LiDAR) and are examined through their applications for plant physiology and soil interactions. Ground sampling distance, spectral calibration, geospatial accuracy and photogrammetry are not treated as ancillary steps, but as critical determinants of agronomic utility. We describe the full data pipeline from FAA (Federal Aviation Administration) regulations to machine learning-driven analytics, acquisition, orthomosaic generation, digital surface modeling, vegetation index extraction, and the development of actionable prescription maps. In the context of AI evolution, we emphasize how AI-ML methods classification, regression, clustering, and dimensionality reduction help with integrating complex patterns in time-series UAV imagery, enabling early and precise management of nutrients, water, weeds, and disease. By synthesizing global regulatory frameworks and field-based use cases, the chapter concludes UAV as tools that transforms data into agronomic decisions.

Why it matches plant phenotyping methodsUAVセンサー、校正、フォトグラメトリ、オルソモザイク、植生指数抽出、機械学習解析を含む一連の植物状態・ストレス推定ワークフローをレビューしており、単なる生物学的実験の測定ではなく、取得・解析手法とプラットフォームが中心です。

abstractThis chapter provides an application-focused roadmap for integrating UAV into modern agronomic workflows as pre, during, and post flight operations for agronomic flight planning.

Unsupervised Tree Detection from UAV Imagery and 3D Point Clouds via Distance Transform-Based Circle Estimation and AIC Optimization ↗

This work proposes a novel tree detection methodology, named DTCD (Distance Transform Circle Detection), based on a fast circle detection method via Distance Transform and Akaike Information Criterion (AIC) optimization. More specifically, a visible-band vegetation index (RGBVI) is calculated to enhance canopy regions, followed by morphological filtering to delineate individual tree crowns. The Euclidean Distance Transform is then applied, and the local maxima of the smoothed distance map are extracted as candidate tree locations. The final detections are iteratively refined using the AIC to optimize the number of trees with respect to canopy coverage efficiency. Additionally, this work introduces DTCD-PC, a modified algorithm tailored for point clouds, which significantly enhances detection accuracy in complex environments. This work makes a significant contribution to tree detection by (1) creating a tree detection framework entirely based on an unsupervised technique, which outperforms state-of-the-art unsupervised and supervised tree detection methods, and (2) introducing a new urban dataset, named AgiosNikolaos-3, that consists of orthomosaics and photogrammetrically reconstructed 3D point clouds, allowing the assessment of the proposed method in complex urban environments. The proposed DTCD approach was evaluated on the Acacia-6 dataset, consisting of UAV images of six-month-old Acacia trees in Southeast Asia, demonstrating superior detection performance compared to existing state-of-the-art techniques, both unsupervised and supervised. Additional experiments were conducted in the custom-developed Urban Dataset, confirming the robustness and generalizability of the DTCD-PC method in heterogeneous environments.

Why it matches plant phenotyping methodsUAV画像・3D点群から個体樹冠を抽出・ delineateする新規手法を開発し、複数データセットで性能評価しているため、植物の樹冠形態・個体構造の画像ベース計測として中心的です。

abstractThis work proposes a novel tree detection methodology, named DTCD (Distance Transform Circle Detection), based on a fast circle detection method via Distance Transform and Akaike Information Criterion (AIC) optimization.

GAPose-GS: Globally adaptive pose-optimized gaussian splatting for plant 3D reconstruction towards more precise phenotyping ↗

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.

Assessing multiple remotely sensed data and model-assisted inference for biomass estimation of an Atlantic Forest fragment ↗

Forest biomass quantification (Mg ha⁻¹) is essential for ecosystem monitoring, especially in areas under anthropogenic pressure, such as Atlantic Forest fragments. This study aimed to compare remote sensors in biomass mapping and population stock estimation of an Atlantic Forest fragment. Ten 0.1 ha plots were randomly distributed within a 17 ha fragment. Data from Sentinel-1 (S1), Sentinel-2 (S2), digital aerial photogrammetry (DAP), and their fusion were evaluated for the construction of predictive models. Linear models with two predictors were fitted: one for each sensor and another using data fusion, selecting the best predictors among all. The models were applied to estimate stand biomass using a regression estimator. The data fusion model showed the best predictive performance (RMSE = 41%), while the DAP-based model had the highest error (RMSE = 64%). However, the most accurate population estimate was obtained with the S2-based model (SE = 21 Mg ha-1), with a relative efficiency 7% higher compared to the traditional inventory (SE = 22 Mg ha-1). Estimates based on DAP, S1, and fusion were less accurate than those from the field inventory. The selected metrics, such as vegetation indices (S2) and textural metrics (S1), reflected the sensors' sensitivity to canopy structure and foliage abundance. DAP showed limitations, possibly due to its low canopy penetration. It is concluded that although the data fusion between DAP and S2 produced the best model for biomass mapping, S2 alone proved more advantageous for population estimates in forest fragments with limited sampling.

Why it matches plant phenotyping methods複数のリモートセンシング手法とデータ融合を比較し、森林キャノピー由来のバイオマス推定モデルを構築・性能評価している。植物群落の明示的な形質推定が中心であり、単なる生物学的実験の routine 測定ではない。

abstractThis study aimed to compare remote sensors in biomass mapping and population stock estimation of an Atlantic Forest fragment.

Improving tree diameter measurements above irregularities in Central African forests: a Close-Range Photogrammetric approach ↗

Accurate measurement of tree diameter in forests is essential for sustainable management of forest resources, ecological assessment, and scientific research. However, most trees in tropical forests have irregularities at the base of the trunk, making it challenging to measure the trunk diameter above them with a tape measure. To meet the increasing demand for data accuracy and reliability, approaches using three-dimensional (3D) point clouds offer a valuable new source of data for tree measurements. This study examines the accuracy of diameter measurements above irregularities using the Close-Range Photogrammetric approach, with diameter tape serving as the reference. A total of 212 trees measured in the north of the Republic of Congo were reconstructed in three dimensions (3D), including 128 trees in semi-deciduous forest and 84 trees in evergreen forest. Comparisons were made in terms of dependence (simple linear regression), correlation (Pearson, Kendall, and Spearman tests), agreement (Bland and Altman method), and difference (Mean Absolute Error - MAE, Root Mean Square Error - RMSE, bias - BIAS, and coefficient of variation - CV). In addition to a near perfect match, a strong association of diameter measurements and a good degree of agreement, the results indicated the presence of differences between diameter measurement approaches in semi-deciduous forest (MAE = 9.25 cm, RMSE = 16.95 cm, BIAS = 7.45 cm) and evergreen forest (MAE = 3.88 cm, RMSE = 8.47 cm, BIAS = 2.37 cm). These differences are minor in the evergreen forest. The magnitude of the differences found is mostly due to the size of the large-diameter classes. In addition, the coefficients of variation (CV) of diameter obtained from the Close-Range Photogrammetric approach were lower than those obtained from the classic conventional approach in both forests, indicating the higher accuracy of the former approach. Further studies could use larger data samples to provide more accurate estimates and verify the limits of these applications’ measurement capabilities.

Why it matches plant phenotyping methods樹木直径という植物形態形質を近距離写真測量で取得し、従来法を基準に精度・一致度を検証しており、フェノタイピング手法が中心である。

abstractThis study examines the accuracy of diameter measurements above irregularities using the Close-Range Photogrammetric approach, with diameter tape serving as the reference.

Medición de atributos forestales de especies de coníferas mediante fotogrametría digital con drones ↗

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.

On the compatibility of single‐scan terrestrial LiDAR with digital photogrammetry and field inventory metrics of vegetation structure in forest and agroforestry landscapes ↗

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

Deep learning-based canopy gap detection using a cross-technological approach with airborne laser scanning and aerial imagery data ↗

Canopy gaps are crucial structural elements of forests, supporting biodiversity and influencing forest dynamics and ecosystem health. Airborne laser scanning (ALS) is commonly used for forest gap analysis and typically outperforms digital aerial photogrammetry (DAP), especially in detecting smaller gaps. However, ALS data availability remains limited compared to DAP. Given the broader availability and cost-effectiveness of DAP, this study aimed to overcome its technical drawbacks in canopy gap detection by applying a cross-technological approach with multiple data sources. This involves ALS-derived reference data fused with spectral and height information from DAP. We developed a deep learning-based method, employing a convolutional neural network (CNN), specifically the U-Net architecture, for detecting canopy gaps. The U-Net was trained using gap polygons automatically generated from ALS-derived canopy height models (CHMs), combined with true digital orthophotos (TDOPs) and DAP-based CHMs. Adding spectral information from TDOPs was intended to help detect shadows typically associated with smaller canopy gaps, which are often missed in DAP-based CHMs. The model was tested in the Solling, a forest area in a low mountain range in Central Germany. Performance was evaluated in independent test areas representing a gradient of structural heterogeneity. Overall, our model achieved moderate to high segmentation performance (IoU: 0.67–0.77; F1-score: 0.56–0.74). Once trained, it can be applied to image-derived inputs, improving canopy gap detection F1-score by on average 0.08 compared to using DAP-based CHMs alone. Our results demonstrate a novel approach for detecting canopy gaps without ALS data, suggesting applications across broader spatial and temporal scales.

Why it matches plant phenotyping methods森林キャノピーのギャップという植物群落の構造状態を、航空画像・レーザーデータとCNNで直接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中心である。

abstractWe developed a deep learning-based method, employing a convolutional neural network (CNN), specifically the U-Net architecture, for detecting canopy gaps.

An investigation of three machine learning models using UAV derived photogrammetry for detecting and measuring tree stumps ↗

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.

High throughput phenotyping of energy cane using uncrewed aircraft system (UAS) and machine learning ↗

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. • Ensemble models (RF, LightGBM) outperform CNN in UAS phenotyping at plot scale. • Canopy volume and percentile heights identified as key biomass predictors. • Fusion of UAS lidar and RGB-SfM improves energy cane biomass estimation. • Sensor fusion mitigates RGB underestimation and lidar overestimation biases. • UAS-HTP supports energy cane cultivar screening for bioenergy applications.

Why it matches plant phenotyping methodsUAS lidar・RGB-SfMによるバイオマス推定と、特徴抽出・機械学習・センサ融合の検証が研究の中心であり、再利用可能な高スループット表現型計測手法を評価している。

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).

Monitoring tropical forests with light drones: ensuring spatial and temporal consistency in stereophotogrammetric products ↗

Light drones provide a cheap and effective tool to monitor forest canopy, especially in tropical and equatorial contexts, where infrastructure and resources are limiting. In these regions, good quality optical satellite images are rare, yet the stakes are maximal to characterize forest function, dynamics, diversity, and phenology, and more generally the vegetation-climate interplay. We describe a complete processing chain based on photogrammetric tools that seeks to optimize the spatial and spectral coherence between repeat image mosaics at centimetric resolution. Our target is to allow individual tree-level monitoring over tens to hundreds of hectare scales with consumer grade equipment (i.e., quadcopter with stabilized RGB camera, standard GNSS positioning). We demonstrate the increase in spatial accuracy achieved using Time-SIFT and Arosics algorithms, which allow (individually and synergistically) to reduce global and local spatial misalignment between mosaics from several meters to a few centimeters. Time-SIFT provides the advantage of increased robustness in initial image alignment and 3D reconstruction, and hence reduces occasional distortions or data gaps. Using Agisoft's color and white balance corrections combined with the use of vegetation indices provides meaningful quantitative signal despite considerable changes in acquisition conditions. In particular, indices that are less sensitive to illumination changes, like the green chromatic coordinate (GCC), allowed evidencing a seasonal signal over four years of monitoring in the evergreen moist forest at Paracou in French Guiana. The signal was decorrelated from obvious geometrical effect (sun height), and provided information on the vegetative stage at tree, species, and stand levels.

Why it matches plant phenotyping methodsドローン画像のステレオフォトグラメトリ処理チェーンを開発・検証し、個体樹木レベルで植生状態や季節変動を定量化する方法を中心的に扱っている。

abstractWe describe a complete processing chain based on photogrammetric tools that seeks to optimize the spatial and spectral coherence between repeat image mosaics at centimetric resolution.

3D Reconstruction Method for Strawberry Plants Based on 3D Gaussian Splatting and Edge Detection ↗

During the 3D reconstruction of strawberry plants, methods based on 3D Gaussian Splatting (3DGS) face significant challenges due to motion-induced image blur. Such blurring substantially reduces the feature matching accuracy in Structure from Motion (SfM) algorithms and compromises the reliability of camera pose estimation, thereby degrading the quality of subsequent 3DGS reconstruction. This ultimately manifests as geometric distortion and loss of texture details in the reconstructed models. The issue is particularly severe on the surface of strawberry fruits: under blurred image conditions, point cloud registration fails, resulting in the loss of high-frequency details in the high-density achene regions, which blurs seed contours and degrades reconstruction accuracy. To address this technical bottleneck, this study proposes an optimized reconstruction scheme integrated with 3DGS. By incorporating the Canny edge detection algorithm to filter h i gh-quality i n put i m ages, t h e a c curacy of the reconstructed model is significantly improved. The optimized approach achieves remarkable results on the strawberry plant dataset: the average Peak Signal-To-Noise Ratio (PSNR) of the 3DGS model reaches 35.99, representing a 15.2% improvement over the baseline 3DGS. The morphology of high-density achenes on the fruit surface is clearly distinguishable, supporting the accurate monitoring of phenotypic parameters in strawberry plants.

Why it matches plant phenotyping methodsイチゴ植物の3D再構成精度を向上させる画像処理・3DGS手法を開発し、果実表面形態などの表現型パラメータ監視に直接利用するため、方法開発が中心である。

abstractTo address this technical bottleneck, this study proposes an optimized reconstruction scheme integrated with 3DGS.

Towards autonomous photogrammetric forest inventory using a lightweight under-canopy robotic drone ↗

Drones are increasingly used in forestry to capture high-resolution remote sensing data, supporting enhanced monitoring, assessment, and decision-making processes. While operations above the forest canopy are already highly automated, flying inside forests remains challenging, primarily relying on manual piloting. In dense forests, relying on the Global Navigation Satellite System (GNSS) for localization is not feasible. In addition, the drone must autonomously adjust its flight path to avoid collisions. Recently, advancements in robotics have enabled autonomous drone flights in GNSS-denied obstacle-rich areas. In this article, a step towards autonomous forest data collection is taken by building a prototype of a robotic under-canopy drone utilizing state-of-the-art open source methods and validating its performance for data collection inside forests. Specifically, the study focused on camera-based autonomous flight under the forest canopy and photogrammetric post-processing of the data collected with the low-cost onboard stereo camera. The autonomous flight capability of the prototype was evaluated through multiple test flights in boreal forests. The tree parameter estimation capability was studied by performing diameter at breast height (DBH) estimation. The prototype successfully carried out flights in selected challenging forest environments, and the experiments showed promising performance in forest 3D modelling with a miniaturized stereoscopic photogrammetric system. The DBH estimation achieved a root mean square error (RMSE) of 3.33 - 3.97 cm (10.69 - 12.98 %) across all trees. For trees with a DBH less than 30 cm, the RMSE was 1.16 - 2.56 cm (5.74 - 12.47 %). The results provide valuable insights into autonomous under-canopy forest mapping and highlight the critical next steps for advancing lightweight robotic drone systems for mapping complex forest environments.

Why it matches plant phenotyping methods森林内ドローンとステレオ画像による3D計測・DBH推定を開発および性能評価しており、樹木形質の取得方法が研究の中心である。

abstractbuilding a prototype of a robotic under-canopy drone utilizing state-of-the-art open source methods and validating its performance for data collection inside forests

Integrating UAV Multi-Temporal Imagery and Machine Learning to Assess Biophysical Parameters of Douro Grapevines ↗

The accurate estimation of grapevine biophysical parameters is important for decision support in precision viticulture. This study addresses the use of unmanned aerial vehicle (UAV) multispectral data and machine learning (ML) techniques to estimate leaf area index (LAI), pruning wood biomass, and yield, across mixed-variety vineyards in the Douro Region of Portugal. Data were collected at three phenological stages, from veraison to maturation and two modeling approaches were tested: one using only spectral features, and another combining spectral and geometric features derived from photogrammetric elevation data. Multiple linear regression (MLR) and five ML algorithms were applied, with feature selection performed using both forward and backward selection procedures. Logarithmic transformations were used to mitigate data skewness. Overall, ML algorithms provided better predictive performance than MLR, particularly when geometric features were included. At harvest-ready, Random Forest achieved the highest accuracy for LAI (R2 = 0.83) and yield (R2 = 0.75), while MLR produced the most accurate estimates for pruning wood biomass (R2 = 0.83). Among geometric variables, canopy area was the most informative. For spectral data, the Modified Soil-Adjusted Vegetation Index (MSAVI) and the Soil-Adjusted Vegetation Index (SAVI) were the most relevant. The models performed well across grapevine varieties, indicating that UAV-based monitoring can serve as a practical, non-invasive, and scalable approach for vineyard management in heterogeneous vineyards.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像と機械学習により、ブドウのLAI、バイオマス、収量を推定する手法を開発・比較しており、表現型取得と推定が研究の中心である。

abstractThis study addresses the use of unmanned aerial vehicle (UAV) multispectral data and machine learning (ML) techniques to estimate leaf area index (LAI), pruning wood biomass, and yield

A 3D phenotyping pipeline for peanut plants using point cloud ↗

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

Comparative evaluation of 3D data acquisition approaches for rail-driven field plant phenotyping platforms ↗

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.

Plot-scale peanut yield estimation using a phenotyping robot and transformer-based image analysis ↗

Peanuts rank as the seventh-largest crop in the United States with a farm value exceeding $1 billion. Conventional peanut yield estimation methods involve digging, harvesting, transporting, and weighing, which are labor-intensive and inefficient for large-scale research operations. This inefficiency is particularly pronounced in peanut breeding, which requires precise pod yield estimations of each plot in order to compare genetic potential for yield to select new, high-performing breeding lines. To improve efficiency and throughput for accelerating genetic improvement, we proposed an automated robotic imaging system to predict peanut yields in the field after digging and inversion of plots. A workflow was developed to estimate yield accurately across different genotypes by counting the pods from stitched plot-scale images. After the robotic scanning in the field, the sequential images of each peanut plot were stitched together using the Local Feature Transformer (LoFTR)-based feature matching and estimated translation between adjusted images, which avoided replicated pod counting in overlapped image regions. Additionally, the Real-Time Detection Transformer (RT-DETR) was customized for pod detection by integrating partial convolution into a lightweight ResNet-18 backbone and refining the up-sampling and down-sampling modules in cross-scale feature fusion. The customized detector achieved a mean Average Precision (mAP50) of 89.3% and a mAP95 of 55.0%, improving by 3.3% and 5.9% over the original RT-DETR model with lighter weights and less computation. To determine the number of pods within the stitched plot-scale image, a sliding window-based method was used to divide it into smaller patches to improve the accuracy of pod detection. In a case study of a total of 68 plots across 19 genotypes in a peanut breeding yield trial, the result presented a correlation (R 2 =0.47) between the yield and predicted pod count, better than the structure-from-motion (SfM) method. The yield ranking among different genotypes using image prediction achieved an average consistency of 84.8% with manual measurement. When the yield difference between two genotypes exceeded 12%, the consistency surpassed 90%. Overall, our robotic plot-scale peanut yield estimation workflow showed promise to replace the human measurement process, reducing the time and labor required for yield determination and improving the efficiency of peanut breeding.

Why it matches plant phenotyping methodsロボット撮像と画像解析により圃場区画の落花生莢数・収量を推定するワークフローを開発し、検出精度や手動測定との整合性を検証しており、フェノタイピング手法が中心である。

abstractwe proposed an automated robotic imaging system to predict peanut yields in the field after digging and inversion of plots.

Geometry-based point cloud fusion of dual-layer UAV photogrammetry and a modified unsupervised generative adversarial network for 3D tree reconstruction in semi-arid forests ↗

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.

Photogrammetry-based 3D plant root imaging and phenotyping: Platforms, technologies, algorithms, and future directions ↗

Abstract has not been obtained from indexed metadata or an accessible article page.

Why it matches plant phenotyping methods植物根の3D画像化と表現型解析に関する技術・アルゴリズム・プラットフォームを扱うレビューであり、植物フェノタイピング手法が中心です。

titlePhotogrammetry-based 3D plant root imaging and phenotyping: Platforms, technologies, algorithms, and future directions

Research on the Possibilities of Determining the Structural Characteristics of Plant Communities Dominated by Reeds Using High-Resolution Satellite Imagery, Ground Measurements, and Unmanned Aerial Vehicles ↗

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.

A Comparative Analysis of Low-Cost Devices for High-Precision Diameter at Breast Height Estimation ↗

Forestry is essential for environmental sustainability, biodiversity conservation, carbon sequestration, and renewable resource management. Traditional methods for forest inventory, particularly the manual measurement of diameter at breast height (DBH), are labor-intensive and prone to error. Recent advancements in proximal sensing, including lidar and photogrammetry, have paved the way for more efficient approaches, yet high costs remain a barrier to widespread adoption. This study investigates the potential of close-range photogrammetry (CRP) using low-cost devices, such as smartphones, cameras, and specialized handheld laser scanners (Stonex and LIVOX prototype), to generate 3D point clouds for accurate DBH estimation. We compared these devices by assessing their agreement and efficiency when compared to conventional methods in diverse forest conditions across multiple tree species. Additionally, we analyze factors influencing measurement errors and propose a comprehensive decision-making framework to guide technology selection in forest inventory. The results show that the lowest-cost devices and photogrammetric methods achieved the highest agreement with the conventional (caliper-based) measurements, while mobile applications were the fastest and least expensive but also the least accurate. Photogrammetry provided the most accurate DBH estimates (error ≈ 0.7 cm) but required the highest effort; handheld laser scanners achieved an average accuracy of about 1.5 cm at substantially higher cost, while mobile applications were the fastest and least expensive but also the least accurate (3–3.5 cm error). The outcomes of this research aim to facilitate more accessible, reliable, and sustainable forest management practices.

Why it matches plant phenotyping methods低コストの画像・レーザーセンシング手法を比較検証し、樹木の胸高直径（DBH）という明示的な植物形質を推定することが研究の中心である。

abstractThis study investigates the potential of close-range photogrammetry (CRP) using low-cost devices, such as smartphones, cameras, and specialized handheld laser scanners (Stonex and LIVOX prototype), to generate 3D point clouds for accurate DBH estimation.

Circlegrammetry for drone imaging: Evaluating a novel technique for mission planning and 3D mapping ↗

Circlegrammetry is a new drone photogrammetry technique that utilizes circular flight paths. This approach promises higher efficiency for 3D modelling compared to traditional grid-based methods. This study evaluates its performance in a Christmas tree (Balsam fir) field, a complex agricultural environment characterized by intricate vegetation geometry. Experiments were conducted in a 2-ha orchard located in Truro, Nova Scotia, using a DJI Matrice 300 RTK equipped with a high-resolution optical camera. Three Circlegrammetry missions with varying overlaps (25 and 50 %) and flight heights (40 and 60 m) were compared against standard oblique and smart oblique drone missions flown at an flight heights of 60 m. Mission assessments focused on flight efficiency, processing performance and reconstruction accuracy. The point density of the tree canopy, generated from dense point clouds, was also evaluated against different survey methods. Results demonstrated that Circlegrammetry significantly reduced flight times and the number of images required, particularly at lower overlap configurations. For example, Circlegrammetry with a 25 % overlap achieved mission completion in about half the time required for smart oblique methods and in approximately one-third the duration of standard oblique missions. Processing efficiency was similarly favoured by Circlegrammetry (25 % overlap), with notable reductions in processing times. In terms of reconstruction quality, Circlegrammetry produced spatially accurate models with ground-control RMSE values ranging from 1.38 to 1.53 cm. These results were comparable to those of traditional oblique methods, despite not utilizing nadir imagery. However, Circlegrammetry showed limitations in capturing lower canopy details on the tree, with an average point density higher than that of other methods. For example, Circle 25 % performed the worst, with an average point spacing of 15.79 points per millimetre for the lower canopy. In contrast, the standard oblique approach performed the best, with an average point spacing of 11.89 points per millimetre. This suggested some constraints inherent to the inward-facing of the camera and higher oblique-angle flight paths on Cirlegrammetry missions. Overall, Circlegrammetry emerges as a promising method for precision agriculture applications by striking a balance between flight efficiency and reconstruction detail. Circlegrammetry with a 50 % overlap was demonstrated to be a comparable alternative to the smart oblique acquisition method. Future research should focus on optimizing overlap percentages and flight configurations to improve lower canopy coverage further and generalize these findings across diverse agricultural contexts.

Why it matches plant phenotyping methods植物キャノピーの3D再構成と点密度を対象に、ドローン画像取得法を比較・検証しており、植物形態の計測手法が中心です。

abstractThis study evaluates its performance in a Christmas tree (Balsam fir) field

Evaluating the Potential of Digital Aerial Photogrammetry (DAP) versus Airborne Laser Scanning (ALS) for Individual Tree Detection and Segmentation in Subtropical Chir Pine ( Pinus Roxburghii ) Forests ↗

Individual tree detection (ITD) algorithms have often relied on airborne laser scanning (ALS) data for delineating trees. Digital Aerial Photogrammetry (DAP) has emerged as a viable alternative to ALS, leveraging sophisticated image-matching algorithms for 3D point cloud generation and subsequent ITD. However, so far, few studies have compared ITD results between ALS and DAP 3D data. We present a detailed comparison of five ITD algorithms using both ALS and DAP data in a subtropical Chir Pine forests, Pakistan. Our analysis, which included 284 field-measured trees, assessed two categories of ITD algorithms: those applied to raster-based Canopy Height Models (CHMs) and those which are directly applied to the point clouds. We evaluated work-flows using fixed window size (FWS) and variable window size (VWS) as well as, unsmoothed and smoothed CHMs generated from ALS and DAP data. Among window sizes, 3 × 3 FWS and 2 × 2 FWS performed best, yielding F Scores of 0.66 and 0.63 using unsmoothed CHMs from ALS and DAP data, respectively. Among point cloud methods, mean shift algorithms consistently outperformed others, achieving F Scores of 0.67 and 0.61 with ALS and DAP data, respectively. The Dalponte2016 algorithm exhibited superior performance in crown segmentation, consistently producing crown radii within 0.5 m of the reference field measured crowns, for ALS data and under 0.6 m for DAP data. Overall, both ALS and DAP achieved comparable results; however, ALS data yielded slightly higher F scores in tree matching and exhibited a stronger correlation with field data compared to DAP. Our findings suggest that DAP-derived point clouds, when normalized by precise DTMs such as those obtained from ALS data, can be effectively utilized for ITD in Chir Pine forests, offering compatibility comparable to ALS data.

Why it matches plant phenotyping methodsALS・DAPによる個体樹検出と樹冠セグメンテーション手法を比較評価し、樹冠半径などの植物形態を現地測定値と検証しているため、植物フェノタイピング手法が中心である。

abstractWe present a detailed comparison of five ITD algorithms using both ALS and DAP data in a subtropical Chir Pine forests, Pakistan.

Tomato Growth Monitoring and Phenological Analysis Using Deep Learning-Based Instance Segmentation and 3D Point Cloud Reconstruction ↗

Accurate and nondestructive monitoring of tomato growth is essential for large-scale greenhouse production; however, it remains challenging for small-fruited cultivars such as cherry tomatoes. Traditional 2D image analysis often fails to capture precise morphological traits, limiting its usefulness in growth modeling and yield estimation. This study proposes an automated phenotyping framework that integrates deep learning-based instance segmentation with high-resolution 3D point cloud reconstruction and ellipsoid fitting to estimate fruit size and ripeness from daily video recordings. These techniques enable accurate camera pose estimation and dense geometric reconstruction (via SfM and MVS), while Nerfacto enhances surface continuity and photorealistic fidelity, resulting in highly precise and visually consistent 3D representations. The reconstructed models are followed by CIELAB color analysis and logistic curve fitting to characterize the growth dynamics. When applied to real greenhouse conditions, the method achieved an average size estimation error of 8.01% compared to manual caliper measurements. During summer, the maximum growth rate (gmax) of size and ripeness were 24.14%, and 95.24% higher than in winter, respectively. Seasonal analysis revealed that winter-grown tomatoes matured approximately 10 days later than summer-grown fruits, highlighting environmental influences on phenological development. By enabling precise, noninvasive tracking of size and ripeness progression, this approach is a novel tool for smart and sustainable agriculture.

Why it matches plant phenotyping methods深層学習によるインスタンスセグメンテーション、3D再構成、色解析を統合し、トマト果実のサイズと成熟度を推定するフェノタイピング手法の開発・評価が中心である。

abstractThis study proposes an automated phenotyping framework that integrates deep learning-based instance segmentation with high-resolution 3D point cloud reconstruction and ellipsoid fitting to estimate fruit size and ripeness from daily video recordings.

Evaluating Mechanically-caused Crop Damage Using Two Simple UAV-based Assessment Techniques ↗

Abstract. The increasing frequency of hydrometeorological extremes, such as torrential rainfall, strong winds, and hailstorms, often causes widespread mechanical damage to crops. This study evaluates the potential of cost-effective unmanned aerial vehicle (UAV) photogrammetry with a standard RGB camera for quantifying crop damage. A maize field with mechanical damage caused by wild boar activity was used as an analogue for storm-induced damage. Two approaches were applied: (i) a 3D structural method based on Canopy Surface Models (CSMs) derived from Structure-from-Motion (SfM) photogrammetry, and (ii) automated image classification using a Support Vector Machine (SVM) combined with Object-Based Image Analysis (OBIA). The accuracy of the damage assessment was compared using two terrain inputs: a UAV-derived DEM (UAV DEM) and the official Czech national LiDAR-based DEM (DEM 5G). The results showed high consistency between both methods and datasets. The relative crop damage rate was 29.25% with the UAV DEM and 26.76% with the DEM 5G, with a spatial agreement exceeding 95%. Jaccard similarity coefficients confirmed strong concordance (0.8953 and 0.9207). The findings highlight the applicability of UAV-based 3D structural analysis for late-stage crop monitoring, when spectral indices lose reliability. They also emphasise that the official DEM 5G can serve as a suitable substitute for a UAV-derived DEM in damage assessment. The methodology thus represents a rapid, cost-effective, and operationally feasible solution for agricultural monitoring, insurance claims, and environmental management.

Why it matches plant phenotyping methodsUAV画像から作物の機械的損傷を定量化する2手法を比較・検証しており、植物状態の取得方法が研究の中心です。

abstractThis study evaluates the potential of cost-effective unmanned aerial vehicle (UAV) photogrammetry with a standard RGB camera for quantifying crop damage.

Seed 3D Phenotyping Across Multiple Crops Using 3D Gaussian Splatting ↗

This study introduces a versatile seed 3D reconstruction method that is applicable to multiple crops—including maize, wheat, and rice—and designed to overcome the inefficiency and subjectivity of manual measurements and the high costs of laser-based phenotyping. A panoramic video of the seed is captured and processed through frame sampling to extract multi-view images. Structure-from-Motion (SFM) is employed for sparse reconstruction and camera pose estimation, while 3D Gaussian Splatting (3DGS) is utilized for high-fidelity dense reconstruction, generating detailed point cloud models. The subsequent point cloud preprocessing, filtering, and segmentation enable the extraction of key phenotypic parameters, including length, width, height, surface area, and volume. The experimental evaluations demonstrated a high measurement accuracy, with coefficients of determination (R2) for length, width, and height reaching 0.9361, 0.8889, and 0.946, respectively. Moreover, the reconstructed models exhibit superior image quality, with peak signal-to-noise ratio (PSNR) values consistently ranging from 35 to 37 dB, underscoring the robustness of 3DGS in preserving fine structural details. Compared to conventional multi-view stereo (MVS) techniques, the proposed method can achieve significantly improved reconstruction accuracy and visual fidelity. The key outcomes of this study confirm that the 3DGS-based pipeline provides a highly accurate, efficient, and scalable solution for digital phenotyping, establishing a robust foundation for its application across diverse crop species.

Why it matches plant phenotyping methods3DGSを用いた種子の3D再構成・点群処理・形質抽出パイプラインを開発し、精度を評価しており、植物表現型取得手法が研究の中心である。

abstractThis study introduces a versatile seed 3D reconstruction method that is applicable to multiple crops

Non-destructive Biomass and SPAD Estimation Based on 3D Photogrammetry for Faba Bean ↗

Faba bean (Vicia faba L.) is a valuable legume crop with high protein content and the ability to fix atmospheric nitrogen through symbiotic bacteria in its root nodules, contributing significantly to both human nutrition and agricultural sustainability. Chlorophyll concentration in leaves serves as a reliable indicator of nitrogen status and photosynthetic capacity, while biomass production reflects overall plant growth and resource use efficiency. This study aims to develop a nondestructive and accurate method for simultaneously estimating chlorophyll content meter (SPAD) and above-ground biomass in faba bean using three-dimensional (3D) photogrammetric imaging combined with deep learning techniques. Point cloud data were obtained from hand-held camera scans of five faba bean genotypes and processed using the PointNet neural network architecture. Results showed that SPAD estimation achieved high accuracy (7.52% relative error) based solely on 3D structural features, while biomass prediction benefited from the integration of real and synthetic datasets, reducing relative error significantly from 24.15 % to 18.04 %. The study highlights the potential of 3D imaging and point cloud-based photogrammetry as effective tools for plant phenotyping, offering a scalable and non-invasive approach for monitoring physiological traits and genotype performance in faba bean.

Why it matches plant phenotyping methods3DフォトグラメトリとPointNetを用いて、ソラマメのSPADと地上部バイオマス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。

abstractThis study aims to develop a nondestructive and accurate method for simultaneously estimating chlorophyll content meter (SPAD) and above-ground biomass in faba bean using three-dimensional (3D) photogrammetric imaging combined with deep learning techniques.

High-throughput plant height measurement for the field peanuts from low-cost UAV photogrammetry ↗

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.

Smartphone-based 3D imaging for canopy and berry cluster volume estimation in wine grapes ↗

• A smartphone-based 3D imaging approach was developed for canopy and berry cluster volume estimation in vineyards. • Machine learning-based segmentation using Gradient Boosting for canopy and YOLO11 with Structure from Motion (SfM) for clusters enabled accurate feature extraction. • Point cloud processing was utilized for accurate surface reconstruction and volume estimation. • The method provides an affordable and accessible alternative to traditional high-cost sensors for precision viticulture applications. Accurate estimation of vine canopy and berry cluster volumes is essential for precision viticulture, as it supports better vineyard management, yield prediction, and resource allocation. Traditional methods, such as manual measurements or expensive sensor-based systems, are often inaccessible to small and mid-scale growers. This study explores the use of smartphone-based 3D imaging and advanced machine learning techniques as an affordable and accessible alternative for estimating wine grape canopy and berry cluster volumes. In this study, point cloud data was collected using an iPhone 14 Pro Max to capture the spatial structure of grape canopies and berry clusters. Two separate datasets were used to evaluate canopy and cluster volumes independently, ensuring comprehensive analysis and validation. Canopy volume estimation involved segmentation using the Gradient Boosting Classifier, followed by computation of 3D point volumes, achieving an RMSE of 0.23 m³ and 98% classification accuracy for canopy point clouds. Berry clusters were segmented using YOLO11, and 3D point clouds were reconstructed using Structure from Motion (SfM) to create watertight meshes. Cluster volumes validated by water‑displacement ground truth yielded an RMSE of 14.68 cm. These findings demonstrate the potential of smartphone-based solutions to support precision viticulture through accurate estimation of vine canopies and berry clusters, which is expected to enhance vineyard productivity and berry quality.

Why it matches plant phenotyping methodsスマートフォン3D画像、機械学習セグメンテーション、SfM、点群処理を用いてブドウ樹冠・果房体積を推定し、実測値で検証する手法開発が中心である。

abstractA smartphone-based 3D imaging approach was developed for canopy and berry cluster volume estimation in vineyards.

Morphology-based correction improves plant height estimation from drone RGB SfM photogrammetry in dense salt marsh canopy ↗

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 .

Comparison of digital aerial photogrammetry, lidar, and Sentinel-2 for evaluating forest fire effects ↗

Wildfires have the potential to profoundly alter forest structure, which can leave land managers with severe information gaps when making post-fire forest management decisions. We assess point clouds derived from aerial lidar (16.8 points/m²) and digital aerial photogrammetry (DAP, 13.9 points/m²) for the purposes of generating post-fire forest attribute maps using 82 permanent field plots. Low-cost aerial lidar and DAP data can enable predictive mapping after fires occur, and an assessment of the utility of DAP in this context is warranted given its lower cost. We evaluated the performance of models constructed from post-fire lidar and DAP data collected over a 158,000 ha area formerly dominated by Douglas-fir in Oregon, United States for four forest attributes: live aboveground biomass, standing dead aboveground biomass, live stem density, and relative basal area change. Both data sources were augmented with Sentinel-2 pre- and post-fire images. DAP models had relative RMSEs that were 10–47 % larger than lidar models. Augmenting the models with Sentinel-2 narrowed this error differential to 3–16 %. We found that the difference between lidar and DAP heights is explained by lidar cover and canopy intensity, suggesting that consumption of canopy fuels can adversely affect the quality of DAP heights. Our results indicate that DAP models augmented with Sentinel-2 present an attractive prospect for fire footprints lacking lidar data owing to DAP’s broader spatial and temporal availability; however, uncertainties may exist for some response variables in areas with high levels of canopy fuel consumption.

Why it matches plant phenotyping methods航空LiDAR・デジタル航空写真測量・Sentinel-2を比較し、森林プロットのバイオマス、幹密度、基底面積変化などの植物属性推定性能を検証しており、測定手法が中心である。

abstractWe assess point clouds derived from aerial lidar (16.8 points/m²) and digital aerial photogrammetry (DAP, 13.9 points/m²) for the purposes of generating post-fire forest attribute maps using 82 permanent field plots.

Estimation de la densite des ligneux avec des images drones dans les ecosystemes Saheliens du Senegal ↗

Les avancées récentes des drones et du traitement des données permettent aujourd’hui de produire des images hautes résolution et des modèles 3D utiles pour évaluer les attributs des arbres. Cette étude a été menée à Widou thiengoly dans la localité du Ferlo, Nord du Sénégal, avec comme objectif général d’appliquer une approche photogrammétrique pour la mesure de la densité des tiges (tiges/ha) avec des images drones. Une méthode de l’approche arbre basée sur un modèle numérique de hauteur d'une zone d'étude de 10 hectares a été mise en œuvre, ce modèle a été construit à partir d'images obtenues par des drones. Au total, 92 arbres de référence ont été comptés dans le cadre de cette étude et l'algorithme a détecté 75 arbres, ce qui donne une précision supérieure à 90 % (score F de 0,93). Dans l'ensemble, l'algorithme a manqué 10 arbres (erreurs d'omission) et a faussement détecté 3 arbres (erreurs de commission), ce qui donne un compte total de 88 arbres. Cette étude suggère que l'algorithme de filtrage des maxima locaux combiné avec des tailles de fenêtre optimale, appliqués sur un Modèle Numérique de Hauteur construit par photogrammétrie est capable d’effectuer des comptages d'arbres avec une précision acceptable (F > 0,90) dans la zone sahélienne. Recent advances in drone technology and data processing now make it possible to generate high-resolution images and 3D models that are useful for assessing tree attributes. This study was conducted in Widou Thiengoly, in the Ferlo area of northern Senegal, with the overall objective of applying a photogrammetric approach to measure stem density (stems/ha) using drone imagery. A tree-based method was implemented on a Digital Height Model covering a 10-hectare study area, constructed from drone images. In total, 92 reference trees were counted during the study, and the algorithm detected 75 trees, resulting in an accuracy above 90% (F-score of 0.93). Overall, the algorithm missed 10 trees (omission errors) and falsely detected 3 trees (commission errors), giving a total count of 88 trees. This study suggests that the local maxima filtering algorithm, combined with optimal window sizes and applied to a photogrammetrically derived Digital Height Model, can perform tree counts with acceptable accuracy (F > 0.90) in the Sahelian zone.

Why it matches plant phenotyping methodsドローン画像とフォトグラメトリによる樹木密度の推定手法を開発・評価し、Fスコアで精度検証しているため、植物形質取得が研究の中心である。

abstractavec comme objectif général d’appliquer une approche photogrammétrique pour la mesure de la densité des tiges (tiges/ha) avec des images drones.

Deriving structural and biochemical crop traits from one UAV sensor: Investigating a multiband VNIR/SWIR imaging system for crop trait monitoring ↗

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.

Applying UAV-based crop height to monitor biomass, N-concentration, and N-uptake in winter wheat ↗

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

Under-canopy UAV Solutions for Forest Inventory – Challenges and Opportunities ↗

Abstract. Forest inventory underpins every facet of ecosystem management and monitoring by providing accurate, spatially explicit data on stand structure, species composition, and site conditions. Yet traditional inventories are frequently constrained by logistical challenges, financial limitations, methodological inconsistencies, and institutional hurdles that undermined the accuracy, completeness, and timeliness of these essential datasets. Over the past two decades, close-range sensing technologies have markedly reduced manual field effort while enhancing the digitization and automation of plot-level measurements. However, these systems remain reliant on human operators for deployment, limiting their ability to fully overcome logistical and technical constraints. Recent advances in under-canopy unmanned aerial vehicles (UAVs) have begun to address these limitations by integrating lightweight, UAV-borne LiDAR and photogrammetric sensors capable of semi-autonomous or autonomous flights beneath dense canopy cover. Such platforms extend the reach of close-range sensing into previously inaccessible forest interiors, enabling rapid, repeatable acquisition of tree- and stand-level metrics without the need for extensive ground crews. In this review, we dissect the technical architectures, sensor configurations, and performance metrics of emerging under-canopy UAV systems for forest inventory. We further identify the principal engineering and operational challenges to guide future research directions and accelerate the adoption of UAV-based forest monitoring solutions.

Why it matches plant phenotyping methods森林の樹木・林分レベル指標を取得するUAV搭載LiDAR・写真測量システムの技術構成と性能をレビューしており、植物状態の計測法が中心である。

abstractIn this review, we dissect the technical architectures, sensor configurations, and performance metrics of emerging under-canopy UAV systems for forest inventory.

Tree stem diameter estimation using inexpensive UAV photogrammetric data and Monte Carlo methods ↗

Abstract. Accurate diameter estimation from point cloud data allows for characterizing stem volume and shape without resorting to destructive methods. Typically, circles are fitted at various stem heights using statistical techniques. However, these techniques are susceptible to noise and occlusion in the point cloud, often caused by obstacles or weather phenomena. This susceptibility reduces the feasibility of applying such methods to point clouds captured by low-cost sensors, which tend to be less precise and noisier. Photogrammetry, however, can be used together with consumer-grade cameras and inexpensive UAVs to generate high-quality point clouds from under-canopy data. This study presents MACiF (Morphology-Aware Circle Fit), a novel method to accurately estimate diameters at various heights from noisy point clouds. Our approach uses robust statistical methods and Monte Carlo simulation to filter the point cloud. We also leverage how stems vary gradually to iteratively correct erroneous estimates. This iterative correction enables estimating diameters with an error lower than -3.34 cm, even when data quality limits the use of other methods. These results support the use of undercanopy low-cost photogrammetry as a viable source of data for automatic stem characterization.

Why it matches plant phenotyping methodsUAVフォトグラメトリの点群から樹幹直径を推定する新規手法を開発しており、植物形態形質の取得が研究の中心である。

abstractThis study presents MACiF (Morphology-Aware Circle Fit), a novel method to accurately estimate diameters at various heights from noisy point clouds.

Applications of 3D Reconstruction Techniques in Crop Canopy Phenotyping: A Review ↗

Amid growing challenges to global food security, high-throughput crop phenotyping has become an essential tool, playing a critical role in genetic improvement, biomass estimation, and disease prevention. Unlike controlled laboratory environments, field-based phenotypic data collection is highly vulnerable to unpredictable factors, significantly complicating the data acquisition process. As a result, the choice of appropriate data collection equipment and processing methods has become a central focus of research. Currently, three key technologies for extracting crop phenotypic parameters are Light Detection and Ranging (LiDAR), Multi-View Stereo (MVS), and depth camera systems. LiDAR is valued for its rapid data acquisition and high-quality point cloud output, despite its substantial cost. MVS offers the potential to combine low-cost deployment with high-resolution point cloud generation, though challenges remain in the complexity and efficiency of point cloud processing. Depth cameras strike a favorable balance between processing speed, accuracy, and cost-effectiveness, yet their performance can be influenced by ambient conditions such as lighting. Data processing techniques primarily involve point cloud denoising, registration, segmentation, and reconstruction. This review summarizes advances over the past five years in 3D reconstruction technologies—focusing on both hardware and point cloud processing methods—with the aim of supporting efficient and accurate 3D phenotype acquisition in high-throughput crop research.

Why it matches plant phenotyping methods作物キャノピーの3D形質取得に用いるLiDAR、MVS、深度カメラと点群処理を中心に扱うレビューであり、植物フェノタイピング手法が主題。

titleApplications of 3D Reconstruction Techniques in Crop Canopy Phenotyping: A Review

Multimodal phenotyping reveals structural–physiological coordination mechanisms underlying light-use efficiency in lettuce ↗

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.).

3D thermal volume mapping to assess the biological and physical characteristics of olive crops using remote sensing and photogrammetric methods ↗

Abstract Drones, as well as ground-based and satellite platforms, offer the possibility to carry sensors able to obtain timely and precise indications about vegetation health conditions. These systems can serve as tools for agricultural monitoring and the management of crops. Nowadays, Unmanned Aerial Vehicles (UAV) systems are equipped with sophisticated sensors, such as those operating in the Thermal InfraRed spectral range, which can provide indications about the water content of vegetation at very-high spatial resolution. This study explores the feasibility of exploiting drone-based thermal imagery and Structure-from-Motion (SfM) photogrammetry to derive 3-D representations in Precision Agriculture. The health condition of olive trees was evaluated using thermal observations collected by a UAV system over an olive orchard located in the Basilicata region (Southern Italy). Following the SfM pipeline, accurate 2-D/3-D thermal photogrammetric products have been created, and analyzed by means of the Normalized Relative Canopy Temperature (NRCT) index. The goal was to explore how 3D thermal volume analysis can enhance the detection and interpretation of early signs of water stress and related plant health descriptors. Although evident symptoms of stress were not yet visible during the survey, our preliminary results highlight the added value of 3D thermal information over traditional 2D approaches, particularly in capturing spatial variability within individual tree canopies. These findings demonstrate the potential of UAV-based 3D thermal analysis as a valuable tool for advanced monitoring in Precision Agriculture and Smart Farming practices.

Why it matches plant phenotyping methodsUAV熱画像とSfMによる3D熱画像から、樹冠温度・水ストレスなどのオリーブ樹の状態を抽出する手法が中心であり、2D手法との比較を含む実質的なフェノタイピング手法研究である。

abstractThis study explores the feasibility of exploiting drone-based thermal imagery and Structure-from-Motion (SfM) photogrammetry to derive 3-D representations in Precision Agriculture.

Iterative Motion Compensation for Canonical 3D Reconstruction from UAV Plant Images Captured in Windy Conditions ↗

3D phenotyping of plants plays a crucial role for understanding plant growth, yield prediction, and disease control. We present a pipeline capable of generating high-quality 3D reconstructions of individual agricultural plants. To acquire data, a small commercially available UAV captures images of a selected plant. Apart from placing ArUco markers, the entire image acquisition process is fully autonomous, controlled by a self-developed Android application running on the drone's controller. The reconstruction task is particularly challenging due to environmental wind and downwash of the UAV. Our proposed pipeline supports the integration of arbitrary state-of-the-art 3D reconstruction methods. To mitigate errors caused by leaf motion during image capture, we use an iterative method that gradually adjusts the input images through deformation. Motion is estimated using optical flow between the original input images and intermediate 3D reconstructions rendered from the corresponding viewpoints. This alignment gradually reduces scene motion, resulting in a canonical representation. After a few iterations, our pipeline improves the reconstruction of state-of-the-art methods and enables the extraction of high-resolution 3D meshes. We will publicly release the source code of our reconstruction pipeline. Additionally, we provide a dataset consisting of multiple plants from various crops, captured across different points in time.

Why it matches plant phenotyping methodsUAV画像から植物個体の高解像度3D形状を再構成する手法と、風による葉の動きを補正する技術を開発しており、植物表現型取得が中心です。データセット提供も含みます。

abstractWe present a pipeline capable of generating high-quality 3D reconstructions of individual agricultural plants.

Integrating UAV-based multispectral imaging with ground-truth soil nitrogen content for precision agriculture: A case study on paddy field yield estimation using machine learning and plant height monitoring ↗

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

Research on cotton plant type identification method based on multidimensional vision ↗

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.

Implementation of an SfM-MVS-based photogrammetry approach for detailed 3D reconstruction of plants ↗

In recent years, non-destructive and non-invasive methods for 3D plant reconstruction have gained increasing importance in plant phenotyping. Morphological traits reflect the physiological status of a plant and serve as key indicators for precision agriculture, crop protection, and food quality assessment. Accurate and efficient 3D modelling enables objective and repeatable monitoring of plant development and health, thus supporting data-driven decision-making in agricultural and food research. This study presents a novel, cost-effective, and flexible photogrammetric apparatus for the routine analysis of plant morphological traits under controlled laboratory conditions. Existing systems often rely on expensive instrumentation and provide limited adaptability, whereas the platform described here combines affordability with high precision and robustness. A key innovation is the use of a robotic arm to control an industrial RGB camera, providing substantial flexibility in image acquisition. This mobility ensures comprehensive coverage of plants of different sizes and architectures while minimising occlusions. Another distinctive feature is the implementation of an optimised parameter tweak in the photogrammetric pipeline, which markedly improves the reconstruction of thin and delicate plant parts such as leaves, petioles, and fine stems. In combination with optimised acquisition parameters, including an exposure time of 50 milliseconds, a tweak value of 0.9, and a camera-to-object distance of 16 centimetres, the system achieves consistent model fidelity across diverse plant structures. Efficiency was further enhanced through automation and an optimised scanning procedure. Comparative testing showed that using a larger number of camera positions with fewer frames per position improved throughput, with the best configuration consisting of three height levels and 40 frames each. These improvements reduced the processing time by 75%, decreasing the average scan duration from 8 min to only 2.7 min per plant, while maintaining accuracy and reliability. Overall, the developed apparatus constitutes a reliable and low-cost solution that integrates robotic-assisted flexibility, improved reconstruction through the parameter tweak, and markedly reduced scanning time. The combination of precision, affordability, and efficiency makes the system competitive with existing approaches and, due to its accessibility and detailed methodological description, provides a distinctive contribution to the phenotyping community.

Why it matches plant phenotyping methods植物形態形質の3D取得を目的とするSfM-MVS撮像・再構成プラットフォームを開発し、精度、処理時間、撮像条件を比較検証しており、フェノタイピング手法が中心である。

abstractThis study presents a novel, cost-effective, and flexible photogrammetric apparatus for the routine analysis of plant morphological traits under controlled laboratory conditions.

P3DFusion: A cross-scene and high-fidelity 3D plant reconstruction framework empowered by vision foundation models and 3D Gaussian splatting ↗

Efficient acquisition of 3D plant structures is crucial for investigating growth mechanisms and phenotype analysis. Traditional 3D reconstruction methods exhibited significant limitations when faced with complex background interference, leading to low reconstruction efficiency and compromised result integrity. To address these challenges, a cross-scene 3D plant reconstruction framework P3DFusion was proposed with two key technological modules: (1) GSAM2 multi-view image processing method with Vision Foundation Models, which combines Grounding DINO and Segment Anything Model 2 (SAM2) to achieve high-precision plant segmentation under zero-shot conditions; (2) High-fidelity modeling based on 3D Gaussian splatting (3DGS) to generate high-quality, measurable meshes optimized for plant structural analysis. We evaluated P3DFusion using two datasets: Dataset1 (greenhouse-potted plants) and Dataset2 (open-field sugar beets). The P3DFusion exhibited significant improvements in reconstruction efficiency (SfM-Time reductions of 8.5 %/47.9 %, Total processing time reductions of 60.9 %/65.2 %) and quality metrics (SSIM increases of 12.9 %/19.8 %, PSNR increases of 11.8 %/13 % reaching 24.26 dB/24.75 dB, and LPIPS reductions of 70 %/85 %) for Dataset 1 and Dataset 2, respectively, compared to the original 3DGS. The P3DFusion outperforms InstantNGP (PSNR: 22.3 %/32.9 % increase) and COLMAP (PSNR: 231.4 %/266.1 % increase). Phenotype trait extraction from reconstructed models shows strong consistency with ground truth measurements (R² > 0.93). The proposed method not only provides an effective solution for cross-scene 3D plant reconstruction but also establishes a robust technical foundation for advanced plant phenotype research.

Why it matches plant phenotyping methods植物の3D再構成・セグメンテーションと形質抽出を中核とする手法開発および比較検証であり、植物フェノタイピング手法として明確に該当する。

abstractEfficient acquisition of 3D plant structures is crucial for investigating growth mechanisms and phenotype analysis.

Geometry-based point cloud fusion of dual-layer UAV photogrammetry and a modified unsupervised generative adversarial network for 3D tree reconstruction in semi-arid forests ↗

Abstract has not been obtained from indexed metadata or an accessible article page.

Why it matches plant phenotyping methodsUAVフォトグラメトリとGANを用いた樹木の3D再構成手法が研究の中心であり、樹木の構造・形態という植物表現型の取得に該当する。

titleGeometry-based point cloud fusion of dual-layer UAV photogrammetry and a modified unsupervised generative adversarial network for 3D tree reconstruction in semi-arid forests

Accurate plant 3D reconstruction and phenotypic traits extraction via stereo imaging and multi-view point cloud alignment ↗

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

OmniPlantSeg: Species Agnostic 3D Point Cloud Organ Segmentation for High-Resolution Plant Phenotyping Across Modalities ↗

Accurate point cloud segmentation for plant organs is crucial for 3D plant phenotyping. Existing solutions are designed problem-specific with a focus on certain plant species or specified sensor-modalities for data acquisition. Furthermore, it is common to use extensive pre-processing and down-sample the plant point clouds to meet hardware or neural network input size requirements. We propose a simple, yet effective algorithm KDSS for sub-sampling of biological point clouds that is agnostic to sensor data and plant species. The main benefit of this approach is that we do not need to down-sample our input data and thus, enable segmentation of the full-resolution point cloud. Combining KD-SS with current state-of-the-art segmentation models shows satisfying results evaluated on different modalities such as photogrammetry, laser triangulation and LiDAR for various plant species. We propose KD-SS as lightweight resolution-retaining alternative to intensive pre-processing and down-sampling methods for plant organ segmentation regardless of used species and sensor modality.

Why it matches plant phenotyping methods植物器官の3D点群セグメンテーションと、センサー・種に依存しないサブサンプリング手法を開発・評価しており、植物フェノタイピングのための形態抽出手法が中心である。

abstractAccurate point cloud segmentation for plant organs is crucial for 3D plant phenotyping.

Development of allometric equations for estimating Pinus Brutia and Pinus Nigra trees biomass using regional field measurements and high-resolution crown area and height imagery ↗

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.

Study on Prediction of Potato Above-Ground Biomass and Yield Based on UAV Visible Light Image ↗

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.

Predicting grain yield and designing density-tolerant maize ideotypes through 3D architectural phenotyping at silking ↗

Accurate prediction of maize ( Zea mays L.) yield and design of density-tolerant ideotypes are crucial for crop management and yield improvement. Three-dimensional (3D) phenotypic traits are closely related to light interception efficiency and yield formation, theoretically serving as important indicators for yield prediction and plant architecture optimization. However, studies utilizing 3D phenotypic traits for yield prediction and ideotype design remain limited. In this study, a two-year (2023–2024) field experiment was conducted with 10 maize hybrids grown under three planting densities (37,500 (low density, LD), 67,500 (medium density, MD), and 97,500 (high density, HD)) plants ha -1 . Plant 3D phenotypic traits at the silking stage were captured using the MVS-Pheno platform. The results showed that increasing planting density led to more compact plant architecture and significant changes in 3D phenotypic traits. A partial least squares regression model integrating 3D phenotypic traits with canopy light interception data achieved high prediction accuracy for yield (R 2 = 0.91, RMSE = 0.49 Mg ha -1 ). Feature sensitivity and correlation analyses further identified projected area (PJA) and plant side width (PSW) as critical indicators for designing varieties tolerant to high density. Furthermore, a strategy is proposed to match plant ideotype to different planting densities: under MD, the leaf area per plant (LAP) and PJA increased, whereas the PSW and leaf orientation value (LOV) decreased; under HD, the LAP, PJA, and PSW decreased, whereas the LOV increased. These findings provide an effective model for yield prediction and a valuable reference for breeding maize with optimal architecture for high-density cultivation. • Partial least squares regression model combining point cloud parameters with canopy light interception predicted yield well. • Plant side width and projected area stand out as pivotal traits influencing maize grain yield. • Plant type optimization under 37,500 and 67,500 plants ha -1 aimed to enhance population light interception. • Plant type optimization focused on improving canopy light distribution under 97,500 plants ha -1 .

Why it matches plant phenotyping methodsMVS-Phenoによる3D形態形質の取得と、収量予測・品種設計への解析が研究の中心であり、形質抽出および予測手法を実質的に評価している。

abstractPlant 3D phenotypic traits at the silking stage were captured using the MVS-Pheno platform.

A study on the response of planting density to 3D plant shape plasticity and population light transmittance of maize ↗

1. During vegetative growth, maize exhibited enhanced vertical and horizontal development under increased planting density. 2. At silking stage, plants showed reduced spatial occupancy with pronounced lateral growth inhibition under increased planting density. 3. The light transmission model based on support vector regression achieved reliable prediction accuracy. Traditional two-dimensional analyses of maize ( Zea mays . L) plant architecture plasticity and canopy light transmission under varying planting densities have limitations in capturing spatial heterogeneity. This study utilized structure-from-motion technology with a multi-view three-dimensional (3D) phenotyping platform to investigate architectural plasticity across different maize varieties and planting densities. Seven novel 3D architectural parameters were developed, and 3D canopy models were constructed for light distribution simulation. At V9 stage, medium planting density (67,500 plants ha⁻¹, MD) increased plant side width and convex hull volume by 7.2 and 11.4%, respectively, compared to low planting density (37,500 plants ha⁻¹, LD). High planting density (97,500 plants ha⁻¹, HD) increased by 4.2 and 17.8% compared with MD. Similar changes were maintained at V13 stage. At silking stage, number of voxel volume plant (NVP) and projected area (PJA) decreased by 6.2 and 11.9% (LD to MD), and 4.9 and 3.6% (MD to HD). Under different densities, MC812 and JNK728 showed 17.2-20.0% and 6.2-7.6% decrease in PJA, and 20.0-26.5% and 15.4-21.1% decrease in NVP compared to ZD958. A bottom light transmittance estimation model combining point cloud parameters with support vector regression achieved reliable prediction ( R ²=0.76, RMSE=2.89%). The 3D canopy model effectively simulated population light distribution ( R ²=0.83, RMSE=8.53%). NVP and PJA were identified as critical parameters affecting bottom canopy light transmittance, suggesting their potential as 3D selection indices for maize density tolerance breeding. These findings provide insights into stage-specific architectural plasticity and light interception, supporting molecular design breeding of density-tolerant maize.

Why it matches plant phenotyping methodsSfMマルチビュー3Dフェノタイピングプラットフォームを用い、7つの3D形態パラメータ、光透過推定モデル、キャノピー光分布シミュレーションを開発・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 utilized structure-from-motion technology with a multi-view three-dimensional (3D) phenotyping platform to investigate architectural plasticity across different maize varieties and planting densities.

Photogrammetric and LiDAR Scanning with iPhone 13 Pro: Accuracy, Precision and Field Application on Hazelnut Trees ↗

Accurate estimation of tree structural and morphological parameters is essential in precision fruit farming, supporting optimised irrigation management, biomass estimation and carbon stock assessment. While traditional field-based measurements remain widely used, they are often time-consuming and subject to operator-induced errors. In recent years, Terrestrial Laser Scanning (TLS) and UAV-based photogrammetry have been successfully employed to generate high-resolution 3D reconstructions of plants; however, their cost and operational constraints limit their scalability in routine field applications. This 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. Cylindrical targets with known geometric dimensions were scanned using both the LiDAR and photogrammetric (Photo) modes of the Polycam© application, with accuracy and precision assessed by comparing extracted measurements to reference values. Field applicability was also tested on hazelnut trees, assessing height, stem diameter and leaf area: the Photo mode delivered the highest accuracy (systematic error of 0.007 m and R 2 = 0.99) and strong agreement with manual leaf measurements (R 2 = 0.93). These results demonstrate that smartphone-based 3D scanning can provide a practical, low-cost approach for structural characterisation in fruit orchards, supporting more efficient crop monitoring.

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.

Open cotton boll detection using LiDAR point clouds and RGB images from unmanned aerial systems ↗

Accurate quantification of open bolls and their distribution is crucial for understanding cotton growth, development, and yield in optimized crop management and enhanced plant breeding. Manual boll counting methods are time-consuming, labor-intensive, and subjective. Leveraging the potential of high-resolution images for high-throughput phenotyping offers a promising avenue for efficient trait quantification. The objectives of this study were to develop methods to detect and count open cotton bolls using LiDAR point cloud and RGB images and to compare the effectiveness of these two data sources. A DJI Phantom 4 RTK Unmanned Aerial System (UAS) equipped with a 4 K RGB camera was used to acquire high-resolution RGB images, and a DJI Matrice 300 RTK with a Zenmuse L1 sensor was used to acquire LiDAR point cloud data. The RGB images were converted to point cloud using photogrammetry by measuring multiple points of overlapping images. The boll detection workflow involved data filtering and clustering using the density-based spatial clustering of applications with noise (DBSCAN) method. Evaluation of the methods involved 48 plots representing small, medium, and large plant sizes using metrics including mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (r²). The methods using both data sources performed well in estimating open bolls, with LiDAR point cloud data slightly outperforming those derived from RGB images. Generally, the performance of the DBSCAN method in boll detection improved with decreasing plant sizes. Specifically, LiDAR data yielded MAPE values of 5.03 %, 8.05 %, and 13.46 %, RMSE values of 7.26, 14.33, and 23.40 bolls per m², and r² values of 0.93, 0.84, and 0.84 for small, medium, and large plant sizes, respectively. RGB image-based data exhibited MAPE values of 7.21 %, 6.49 %, and 16.41 %, RMSE values of 11.05, 13.66, and 26.49 bolls per m², and r² values of 0.82, 0.74, and 0.83 for small, medium, and large plant sizes, respectively. The method demonstrates the potential of RGB imagery and LiDAR data for estimating boll counts, offering valuable tools for enhanced plant phenotyping in plant breeding and site-specific crop management. Both data sources underestimated boll counts, with smaller plants showing less undercounting, likely due to improved light penetration and separation of bolls. These findings highlight the influence of plant structure on boll detection accuracy and the need to address challenges posed by dense canopies to enhance detection reliability.

Why it matches plant phenotyping methodsLiDARとRGB画像を用いた綿花の開花ボール数の検出・計数手法を開発し、比較評価した研究であり、植物表現型取得が中心です。

abstractThe objectives of this study were to develop methods to detect and count open cotton bolls using LiDAR point cloud and RGB images and to compare the effectiveness of these two data sources.

UAV-multispectral imaging and machine learning for Brown Spot Needle Blight severity assessment in southeastern US pine forests ↗

Abstract Brown Spot Needle Blight (BSNB) poses a significant threat to loblolly pine (Pinus taeda) forests in the southeastern United States, reducing timber yields, biodiversity, and overall forest health. Traditional detection methods rely on field-based assessments, which are time-intensive and impractical for large areas. Unmanned aerial vehicle (UAV)-based multispectral imaging presents a potential alternative, but its effectiveness for BSNB detection remains largely unexplored. To address this gap, we developed a UAV-based remote sensing framework to detect and map BSNB severity using multispectral imagery and machine learning. Specifically, we aimed to (i) classify and map BSNB severity using Support Vector Machines (SVM) and Artificial Neural Networks (ANN) and (ii) quantify the density of healthy and BSNB-infected trees using point cloud-derived metrics from UAV-based Structure from Motion (SfM). Field-based assessments across fourteen loblolly pine-dominated sites in the state of Alabama, provided BSNB-verified observations for model training and testing, and high-resolution UAV-based multispectral imagery were acquired using a DJI Mavic 3M. Spectral analysis of processed image bands and derived indices identified the Normalized Difference Vegetation Index (NDVI) and Soil-Adjusted Vegetation Index (SAVI) as optimal predictors of BSNB presence. Classification models achieved high accuracy, with SVM and ANN reaching 94.79% and 94.00% accuracy in Washington County, and 94.94% and 94.89% in Cullman County, respectively. Kappa coefficients ranged from 0.80 to 0.92 for SVM and 0.80 to 0.89 for ANN. This study provides one of the first systematic UAV-based approaches for BSNB detection and severity mapping, demonstrating the potential of combining multispectral imagery, SfM-derived metrics, and machine learning for operational forest health assessments.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像、SfM、機械学習を用いて、マツの病害状態と重症度を直接推定・マッピングする方法を開発し、精度検証しているため、植物フェノタイピング手法が中心である。

abstractwe developed a UAV-based remote sensing framework to detect and map BSNB severity using multispectral imagery and machine learning

PWM offline variable application based on UAV remote sensing 3D prescription map ↗

Precision application in orchards enhancing deposition uniformity and environmental sustainability by accurately matching nozzle output with canopy parameters. This study provides a pipeline for creating 3D prescription maps using a UAV and performing offline variable application. It also evaluates the accuracy of ground altitude measurements at various flight heights. At a flight height of 30 m, with a three-dimensional reconstruction method without phase-control points, the root mean square error (RMSE) for ground altitude measurement was 0.214 m and the mean absolute error (MAE) was 0.211 m; for the canopy area, these values were 0.591 m and 0.541 m, respectively. As flight height increased, the accuracy of altitude measurements declined and tended to be underestimated. Moreover, during offline variable spraying, the shape of the spray area influenced deposition accuracy, with collision detection area of a line segment achieving greater precision than conical ones. Field tests showed that the offline variable application method reduced pesticide usage by 32.43 % and enhanced spray uniformity. This newly developed process does not require costly sensors on each sprayer and has potential for field applications.

Why it matches plant phenotyping methodsUAVによる3D再構成で果樹冠パラメータ（樹冠面積・高度）を抽出し、その精度を評価するパイプラインが中心であり、単なる散布試験ではない。

abstractThis study provides a pipeline for creating 3D prescription maps using a UAV and performing offline variable application.

Methodology for Using Georeferenced Digital Maps to Manage the Canopy Height of Elephant Grass Genotypes ↗

Objective: The objective of this study was to develop a methodology to accurately report the average canopy height formed by elephant grass genotypes, without carrying out numerous field evaluations. Theoretical Framework: The research is based on the concepts of digital agriculture and photogrammetry for the potential use of remotely piloted aircraft (RPAs), which are low-altitude remote sensing platforms. These platforms, equipped with several high-precision sensors, can be used to obtain diverse information about elephant grass. Method: The methodology adopted for this research involves aerial mapping of elephant grass patches in the José Henrique Bruschi experimental area, Coronel Pachecho, MG – for data collection using a PRA. Photogrammetric processing of the aerial images, statistical analysis of the data and the maps generated were performed. Results and Discussion: According to the results obtained, it is clear that the height of elephant grass can be estimated well using precision GPS and ARP images. The correlation of 0.9 observed between the average heights obtained demonstrates that the methodology using ARP and precision GPS has the potential to replace or complement, efficiently and accurately, measurements using a graduated ruler. Research Implications: The practical and theoretical implications of this research are discussed, providing insights into how the results can be applied in the field of PRA use. Their use in agricultural systems is increasingly important in crop monitoring, being relevant to the environment and agriculture. Originality/Value: This study contributes to the literature by presenting a methodological proposal to accurately report the average canopy height of elephant grass genotypes. The relevance and value of this research are evidenced by showing that the proposed methodology was more representative than the average height obtained by the field method.

Why it matches plant phenotyping methodsドローン画像と写真測量・GPSを用いて、象草遺伝子型の草冠高を推定する測定手法を開発・検証しており、植物形質取得が研究の中心である。

abstractThe objective of this study was to develop a methodology to accurately report the average canopy height formed by elephant grass genotypes, without carrying out numerous field evaluations.

Assessment of Structural Differences in a Low-Stature Mediterranean-Type Shrubland Using Structure-From-Motion (SfM) ↗

Structural traits of vegetation, derived from the three-dimensional distribution of plant elements, are closely linked to ecosystem functions such as productivity and habitat provision. While extensively studied in forest ecosystems, these traits remain understudied in low-stature systems such as Mediterranean-type shrublands. In this study we explore the use of structural metrics derived from small unmanned aerial system (UAS)-based 3D point clouds, generated using the structure-from-motion (SfM) photogrammetry technique, to assess post-fire vegetation structure and biodiversity in the fynbos biome of the Cape Floristic Region (CFR), South Africa. Fynbos is a fire-adapted shrubland that represents nearly 80% of plant species in the CFR, making post-disturbance monitoring critical for conservation. We extracted three structural metrics—canopy height, top rugosity, and surface gap ratio—and achieved ~85% accuracy in classifying 5 × 5 m subplots by burn year using a Multi-Layer Perceptron (MLP), with canopy height as the strongest predictor. Additionally, top rugosity and gap ratio significantly contributed to modeling percentage cover-based species diversity. Our findings demonstrate that UAS-derived structural metrics provide valuable information for characterizing vegetation recovery and biodiversity patterns in low-stature, fire-prone ecosystems. This approach can support ecological monitoring and inform conservation strategies in Mediterranean-type shrublands.

Why it matches plant phenotyping methodsUAS-SfMによる3次元点群から植物群落の樹冠高・凹凸・ギャップ率を抽出し、植生構造や回復を評価する手法が研究の中心であるため。

abstractwe explore the use of structural metrics derived from small unmanned aerial system (UAS)-based 3D point clouds, generated using the structure-from-motion (SfM) photogrammetry technique, to assess post-fire vegetation structure and biodiversity

A review on advancing agricultural practices using photogrammetric images ↗

Photogrammetry is a technique that involves the extraction of geometric information from two-dimensional images (2D). It is widely utilized in various fields for the creation of digital elevation models (DEM), orthomosaics and three-dimensional (3D) reconstructions of landscapes. In agriculture it is applied to obtain accurate and detailed spatial data for field variability mapping. It serves as a powerful tool in modern agriculture, contributing to high throughput phenotyping, monitoring growth patterns, pest attacks and nutrient deficiencies further helping in efficient resource management and decision-making about important farming operations. Real time monitoring further enhances its applicability in agriculture through the integration of photogrammetry with other technologies like drones, artificial intelligence and remote sensing. By harnessing the power of photogrammetry, stakeholders in the agricultural sector can unlock new possibilities for precision agriculture, resource optimization and ecosystem stewardship. Totally 300 articles were collected related to the topic of review from various sources in that nearly 100 articles were used to explore about photogrammetry progression, principles and software for processing images and mainly underscores the applications that offer farmers to enhance productivity by reducing environmental impact. The potential challenges and future directions in photogrammetric applications in agriculture are also discussed, highlighting the need for continued research and innovation to address evolving agricultural demands and sustainability goals.

Why it matches plant phenotyping methods農業フォトグラメトリの原理・画像処理ソフトウェア・応用を体系的にレビューし、高スループット表現型解析や生育パターン監視を明示的に扱うため、植物表現型計測手法のレビューとして適格。

abstractPhotogrammetry is a technique that involves the extraction of geometric information from two-dimensional images (2D).

Agronomic Information Extraction from UAV-Based Thermal Photogrammetry Using MATLAB ↗

Thermal cameras are becoming popular in several applications of precision agriculture, including crop and soil monitoring, for efficient irrigation scheduling, crop maturity, and yield mapping. Nowadays, these sensors can be integrated as payloads on unmanned aerial vehicles, providing high spatial and temporal resolution, to deeply understand the variability of crop and soil conditions. However, few commercial software programs, such as PIX4D Mapper, can process thermal images, and their functionalities are very limited. This paper reports on the implementation of a custom MATLAB® R2024a script to extract agronomic information from thermal orthomosaics obtained from images acquired by the DJI Mavic 3T drone. This approach enables us to evaluate the temperature at each point of an orthomosaic, create regions of interest, calculate basic statistics of spatial temperature distribution, and compute the Crop Water Stress Index. In the authors’ opinion, the reported approach can be easily replicated and can serve as a valuable tool for scientists who work with thermal images in the agricultural sector.

Why it matches plant phenotyping methodsUAV熱画像から温度分布とCrop Water Stress Indexを抽出するMATLAB手法を開発・提示しており、植物の水ストレス状態を定量化する方法が中心である。

abstractThis paper reports on the implementation of a custom MATLAB® R2024a script to extract agronomic information from thermal orthomosaics obtained from images acquired by the DJI Mavic 3T drone.

Implementation of an SfM-MVS-based photogrammetry approach for detailed 3D reconstruction of plants ↗

Abstract In recent years, non-destructive and non-invasive methods for 3D plant reconstruction have gained importance in plant phenotyping. Morphological traits reflect a plant’s physiological status and serve as key indicators for precision agriculture, crop protection, and food quality assessment. Accurate and efficient 3D modelling enables objective, repeatable monitoring of plant development and health, supporting data-driven decision-making in agricultural and food research. This study presents a cost-effective and flexible photogrammetric methodology for analysing plant morphological traits under controlled laboratory conditions. The system includes an industrial RGB camera mounted on a robotic arm, a rotating platform with an adjustable plant holder, and stable illumination. The key steps involved camera calibration, exposure optimisation, fine-tuning of evaluation algorithm parameters (tweaks), setting the optimal camera-to-object distance, and reducing computational load for 3D model evaluation. Comparative testing revealed that the most effective calibration strategy integrated simultaneous calibration, pre-calibrated parameters, and adaptive fitting, ensuring high reconstruction accuracy and consistent model quality. The optimal acquisition parameters were a 50 milliseconds exposure time, a tweak value of 0.9, and a 16 cm camera-to-object distance. Using more camera positions with fewer frames per position proved more efficient than the reverse. The optimal configuration consisted of three height levels with 40 frames each. Automation and data reduction led to a 75% decrease in processing time, reducing the scan time from 8 minutes to 2.7 minutes per plant. The developed method proved to be a reliable, reproducible, and affordable tool for routine 3D analysis of plant morphology via close-range photogrammetry.

Why it matches plant phenotyping methods植物形態を取得するSfM-MVSフォトグラメトリ法と撮像・校正・処理条件を開発、比較検証しており、植物フェノタイピング手法が研究の中心である。

abstractThis study presents a cost-effective and flexible photogrammetric methodology for analysing plant morphological traits under controlled laboratory conditions.

TomatoWUR: An annotated dataset of tomato plants to quantitatively evaluate segmentation, skeletonisation, and plant-trait extraction algorithms for 3D plant phenotyping ↗

Plant phenotyping involves the measurements of plant traits to gain more insight into the interaction between the genotype (G), environment (E) and crop management strategies (M). To improve plant phenotyping, accurate measurements are crucial. Manual measurements are biased, time-intensive, and therefore limited to only a few plants. Especially measurements of 3D phenotypic traits, such as plant architecture, internode length, and leaf area are difficult to extract manually. To enhance the speed and accuracy of phenotyping, there is a need for automatic digital plant phenotyping solutions. The presented dataset contains 3D point clouds of tomato plants, which will enable researchers to develop novel methods to extract 3D phenotypic traits. Converting 3D point clouds to plant traits is also known as 3D plant phenotyping. This process can be subdivided into three steps: point cloud segmentation, skeletonisation to extract plant architecture, and plant-traits extraction. Those three steps need to be analysed properly to indicate bottlenecks and improve 3D phenotyping algorithms. Currently, the development of 3D phenotyping algorithms is inhibited by the availability of comprehensive datasets and algorithms to analyse all steps. To our best knowledge only five annotated datasets exist for testing and validating 3D phenotyping algorithms. However, these datasets mainly focus on the segmentation step. Skeletonisation and manual measured plant traits are frequently not included. To improve 3D plant phenotyping, a novel dataset, TomatoWUR, is presented. This comprehensive dataset consists of 44 point clouds of single tomato plants imaged by fifteen cameras to create a point cloud using the shape-from-silhouette methodology. The dataset includes annotated point clouds, skeletons, and manual reference measurements. In addition, the dataset includes software for comprehensive evaluation and comparison of phenotyping methods, which is expected to benefit the development of 3D phenotyping algorithms. The related software can be found our GIT: https://github.com/WUR-ABE/TomatoWUR.

Why it matches plant phenotyping methods3D植物フェノタイピング用の注釈付きデータセットと評価ソフトウェアを提供し、セグメンテーション、骨格化、形質抽出アルゴリズムの開発・検証を中心に扱っている。

abstractThe presented dataset contains 3D point clouds of tomato plants, which will enable researchers to develop novel methods to extract 3D phenotypic traits.

Multi-Trait Phenotypic Analysis and Biomass Estimation of Lettuce Cultivars Based on SFM-MVS ↗

To address the problems of traditional methods that rely on destructive sampling, the poor adaptability of fixed equipment, and the susceptibility of single-view angle measurements to occlusions, a non-destructive and portable device for three-dimensional phenotyping and biomass detection in lettuce was developed. Based on the Structure-from-Motion Multi-View Stereo (SFM-MVS) algorithms, a high-precision three-dimensional point cloud model was reconstructed from multi-view RGB image sequences, and 12 phenotypic parameters, such as plant height, crown width, were accurately extracted. Through regression analyses of plant height, crown width, and crown height, and the R2 values were 0.98, 0.99, and 0.99, respectively, the RMSE values were 2.26 mm, 1.74 mm, and 1.69 mm, respectively. On this basis, four biomass prediction models were developed using Adaptive Boosting (AdaBoost), Support Vector Regression (SVR), Gradient Boosting Decision Tree (GBDT), and Random Forest Regression (RFR). The results indicated that the RFR model based on the projected convex hull area, point cloud convex hull surface area, and projected convex hull perimeter performed the best, with an R2 of 0.90, an RMSE of 2.63 g, and an RMSEn of 9.53%, indicating that the RFR was able to accurately simulate lettuce biomass. This research achieves three-dimensional reconstruction and accurate biomass prediction of facility lettuce, and provides a portable and lightweight solution for facility crop growth detection.

Why it matches plant phenotyping methodsレタスの3次元画像計測、形質抽出、バイオマス推定を行う携帯型フェノタイピング手法を開発し、精度検証まで実施しており、方法自体が研究の中心である。

abstracta non-destructive and portable device for three-dimensional phenotyping and biomass detection in lettuce was developed

Biomass phenotyping of oilseed rape through UAV multi-view oblique imaging with 3DGS and SAM model ↗

Biomass estimation of oilseed rape is crucial for optimizing crop productivity and breeding strategies. While UAV-based imaging has advanced high-throughput phenotyping, current methods often rely on orthophoto images, which struggle with overlapping leaves and incomplete structural information in complex field environments. This study integrates 3D Gaussian Splatting (3DGS) with the Segment Anything Model (SAM) for precise 3D reconstruction and biomass estimation of oilseed rape. UAV multi-view oblique images from 36 angles were used to perform 3D reconstruction, with the SAM module enhancing point cloud segmentation. The segmented point clouds were then converted into point cloud volumes, which were fitted to ground-measured biomass using linear regression. The results showed that 3DGS (7 k and 30 k iterations) provided high accuracy, with peak signal-to-noise ratios (PSNR) of 27.43 and 29.53 and training times of 7 and 49 min, respectively. This performance exceeded that of structure from motion (SfM) and mipmap Neural Radiance Fields (Mip-NeRF), demonstrating superior efficiency. The SAM module achieved high segmentation accuracy, with a mean intersection over union (mIoU) of 0.961 and an F1-score of 0.980. Additionally, a comparison of biomass extraction models found the point cloud volume model to be the most accurate, with an determination coefficient (R²) of 0.976, root mean square error (RMSE) of 2.92 g/plant, and mean absolute percentage error (MAPE) of 6.81 %, outperforming both the plot crop volume and individual crop volume models. This study highlights the potential of combining 3DGS with multi-view UAV imaging for improved biomass phenotyping.

Why it matches plant phenotyping methodsUAV多視点画像、3D再構成、SAMによる分割、体積からのバイオマス推定を統合・比較検証した、植物表現型取得法が研究の中心である。

abstractThis study integrates 3D Gaussian Splatting (3DGS) with the Segment Anything Model (SAM) for precise 3D reconstruction and biomass estimation of oilseed rape.

Three-dimensional reconstruction and parameters extraction of walnut (Juglans regia L.) branches based on Neural Radiation Fields ↗

Three-dimensional (3D) reconstruction is important for obtaining morphological information and making intelligent management decisions for fruit trees. Thus, a method for the 3D reconstruction and parameters extraction of branches based on Neural Radiation Fields (NeRF) was proposed for walnut (Juglans regia L.) trees. This approach combined Structure from Motion (SfM) with NeRF and used multi-view images to reconstruct branches. First, a dataset of multi-view images of walnut trees was built and camera poses were obtained using SfM. Second, WalnutNeRF was optimized by incorporating hash encoding, piecewise sampler and appearance embedding features to address challenges associated with complex outdoor environments and accurately reconstruct branches. A scale recovery method using calibration objects was employed to extract branch parameters. The effectiveness of WalnutNeRF was evaluated by analyzing rendering performance, reconstruction efficiency, point cloud quality, and the accuracy of extracted branch parameters. WalnutNeRF outperformed existing methods in terms of the quality of rendered images and the accuracy of estimated depth, as determined using PSNR, SSIM, LPIPS, and other metrics. WalnutNeRF resulted in a branch reconstruction accuracy of 90.94 %, with a training time that was 9-time faster than that of SfM-MVS. Compared with SfM-MVS, WalnutNeRF decreased reconstruction errors for the main branches, lateral branches, and watershoots by 72 %, 67 %, and 57 %, respectively, and decreased the errors in length by 7.09 %, 4.33 %, and 65.07 %, respectively. Accordingly, WalnutNeRF decreased the reconstruction time, while increasing accuracy, providing robust support for the development of intelligent management applications (e.g., intelligent pruning) for walnut trees.

Why it matches plant phenotyping methodsNeRFとSfMを用いてクルミ枝の3D形状を再構成し、枝パラメータを抽出・精度評価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。

abstracta method for the 3D reconstruction and parameters extraction of branches based on Neural Radiation Fields (NeRF) was proposed for walnut (Juglans regia L.) trees.

Unmanned aerial vehicle (UAV) derived structure-from-motion photogrammetry point clouds for oil palm (Elaeis guineensis) canopy segmentation and height estimation ↗

The vast size of oil palm (Elaeis guineensis) plantations has led to lightweight unmanned aerial vehicles (UAVs) being identified as cost effective tools to generate inventories for improved plantation management, with proximal aerial data capable of resolving single palm canopies at potentially, centimetric resolution. If acquired with sufficient overlap, aerial data from UAVs can be processed within structure-from-motion (SfM) photogrammetry workflows to yield volumetric point cloud representations of the scene. Point cloud-derived structural information on individual palms can benefit not only plantation management but is also of great environmental research interest, given the potential to deliver spatially contiguous quantifications of aboveground biomass, from which carbon can be accounted. Using lightweight UAVs we captured data over plantation plots of varying ages (2, 7 and 10 years) at peat soil sites in Sarawak, Malaysia, and we explored the impact of changing spatial resolution and image overlap on spatially variable uncertainties in SfM derived point clouds for the ten year old plot. Point cloud precisions were found to be in the decimetre range (mean of 26.7 31 cm) for a 10 year old plantation plot surveyed at 100 m flight altitude and >75% image overlap. Derived canopy height models were used and evaluated for automated palm identification using local height maxima. Metrics such as maximum canopy height and stem height, derived from segmented single palm point clouds were tested relative to ground validation data. Local maximum identification performed best for palms which were taller than surrounding undergrowth but whose fronds did not overlap significantly (98.2% mapping accuracy for 7 year old plot of 776 palms). Stem heights could be predicted from point cloud derived metrics with root-mean-square errors (RMSEs) of 0.27 m (R2= 0.63) for 7 year old and 0.45 m (R2=0.69) for 10 year old palms. It was also found that an acquisition designed to yield the minimal required overlap between images (60%) performed almost as well as higher overlap acquisitions (>75%) for palm identification and basic height metrics which is promising for operational implementations seeking to maximise spatial coverage and minimise processing costs. We conclude that UAV-based SfM can provide reliable data not only for oil palm inventory generation but allows the retrieval of basic structural parameters which may enable per-palm above-ground biomass estimations.

Why it matches plant phenotyping methodsUAV-SfM点群を用いて個体ごとの樹冠分割・樹高などの植物構造形質を抽出し、精度評価・地上検証まで行っており、フェノタイピング手法が研究の中心である。

abstractPoint cloud-derived structural information on individual palms

Analyzing Post-fire Vegetation Dynamics with Ultra-high Resolution Remote Sensing Data ↗

Abstract. Monitoring post-fire vegetation dynamics is essential for understanding forest recovery processes and informing management strategies. UAV-based ultra-high resolution multi-temporal imagery, combined with the Structure-from-Motion andMulti-View Stereo (SfM-MVS) workflow, provides a cost-effective and scalable solution for forest monitoring. However, challenges remain in co-aligning multi-temporal datasets, segmenting individual trees in dense canopies, and ensuring classification accuracy. This study presents a comprehensive workflow for post-fire forest monitoring using UAV imagery, covering data acquisition, co-alignment, tree segmentation, species classification, and biophysical parameter estimation using growth models. The workflow was tested on three sites in Central Yakutia, with varying post-fire regeneration scenarios. Co-alignment was applied to multi-temporal UAV datasets, and tree segmentation was performed using the algorithms developed for Airborne Laser Scanning (ASL) forest point clouds. Tree species classification relied on statistical spatial variables of point clouds, and growth models were used to estimate parameters such as tree height, age, canopy area, above-ground biomass, and net primary productivity. The results demonstrated that co-alignment enabled consistent multi-temporal analysis, but performance was sensitive to flight planning consistency and lighting conditions. Tree segmentation accuracy was high in open-canopy areas but decreased in dense canopies. The classification of larch and birch species achieved relatively high precision and recall values, while dead trees showed lower classification accuracy due to challenging lighting conditions. Growth models successfully estimated biophysical parameters, but further validation using dendrochronological methods is required. This study highlights the potential of UAV-based multi-temporal monitoring for post-fire forest assessment. Future research should focus on improving tree segmentation of SfM-MVS point clouds in dense canopies, optimizing co-alignment under varying environmental conditions, and integrating additional point cloud classification methods to improve accuracy in areas with complex species distribution.

Why it matches plant phenotyping methodsUAV画像とSfM-MVSを用いて個体樹木を分割・分類し、樹高、樹冠面積、バイオマスなどの植物形質を推定するワークフローが研究の中心であり、精度評価も行っている。

abstractThis study presents a comprehensive workflow for post-fire forest monitoring using UAV imagery, covering data acquisition, co-alignment, tree segmentation, species classification, and biophysical parameter estimation using growth models.

Wykorzystanie teledetekcji i fotogrametrii w urządzaniu lasu ↗

The study examines the practical applications of remote sensing and photogrammetry in forest management planning, using the Korpele Forest District as a case study. The research focuses on analysing data for phytosociological documentation. The study demonstrated the effectiveness of remote sensing in delineating forest and non-forest areas, mapping linear features, monitoring natural succession, and visualizing terrain relief variations. The research also revealed that tree height data, processed through a hexagonal grid system, effectively reflects site productivity and helps distinguish plant communities. There is a correlation between Scots pine height variations and forest habitat boundaries, especially in substitute communities where pine grows on sites not naturally suited for this species. The study confirms that remote sensing techniques serve as valuable supplementary tools in forest management planning, though their implementation requires specialist knowledge and data validation.

Why it matches plant phenotyping methods森林管理を主題とするが、リモートセンシング・写真測量を用いて樹高という植物形質を抽出し、生産性や植物群落境界との対応を評価しており、植物形質測定の実質的な応用が含まれる。

abstractThe study examines the practical applications of remote sensing and photogrammetry in forest management planning

Mapping and Estimating Blue Carbon in Mangrove Forests Using Drone and Field-Based Tree Height Data: A Cost-Effective Tool for Conservation and Management ↗

Mangrove forests are vital blue carbon (BC) ecosystems that significantly contribute to climate change mitigation through carbon sequestration. Accurate, scalable, and cost-effective methods for estimating carbon stocks in these environments are essential for conservation planning. In this study, we assessed the potential of drones, also known as unmanned aerial vehicles (UAVs), for estimating above-ground biomass (AGB) and BC in Avicennia marina stands by integrating drone-based canopy measurements with field-measured tree heights. Using structure-from-motion (SfM) photogrammetry and a consumer-grade drone, we generated a canopy height model and extracted structural parameters from individual trees in the Melgonze mangrove patch, southern Iran. Field-measured tree heights served to validate drone-derived estimates and calibrate an allometric model tailored for A. marina. While drone-based heights differed significantly from field measurements (p 0.05), demonstrating that crown area (CA) and model formulation effectively compensate for height inaccuracies. This study confirms that drones can provide reliable estimates of BC through non-invasive means—eliminating the need to harvest, cut, or physically disturb individual trees—supporting their application in mangrove monitoring and ecosystem service assessments, even under challenging field conditions.

Why it matches plant phenotyping methodsドローンSfMによる個体樹冠高・構造パラメータから樹木の地上部バイオマスを推定し、実測値で検証・モデル較正しており、植物個体の形態計測手法が中心である。

abstractUsing structure-from-motion (SfM) photogrammetry and a consumer-grade drone, we generated a canopy height model and extracted structural parameters from individual trees

Research on Single-Tree Segmentation Method for Forest 3D Reconstruction Point Cloud Based on Attention Mechanism ↗

The segmentation of individual trees holds considerable significance in the investigation and management of forest resources. Utilizing smartphone-captured imagery combined with image-based 3D reconstruction techniques to generate corresponding point cloud data can serve as a more accessible and potentially cost-efficient alternative for data acquisition compared to conventional LiDAR methods. In this study, we present a Sparse 3D U-Net framework for single-tree segmentation which is predicated on a multi-head attention mechanism. The mechanism functions by projecting the input data into multiple subspaces—referred to as “heads”—followed by independent attention computation within each subspace. Subsequently, the outputs are aggregated to form a comprehensive representation. As a result, multi-head attention facilitates the model’s ability to capture diverse contextual information, thereby enhancing performance across a wide range of applications. This framework enables efficient, intelligent, and end-to-end instance segmentation of forest point cloud data through the integration of multi-scale features and global contextual information. The introduction of an iterative mechanism at the attention layer allows the model to learn more compact feature representations, thereby significantly enhancing its convergence speed. In this study, Dongsheng Bajia Country Park and Jiufeng National Forest Park, situated in Haidian District, Beijing, China, were selected as the designated test sites. Eight representative sample plots within these areas were systematically sampled. Forest stand sequential photographs were captured using an iPhone, and these images were processed to generate corresponding point cloud data for the respective sample plots. This methodology was employed to comprehensively assess the model’s capability for single-tree segmentation. Furthermore, the generalization performance of the proposed model was validated using the publicly available dataset TreeLearn. The model’s advantages were demonstrated across multiple aspects, including data processing efficiency, training robustness, and single-tree segmentation speed. The proposed method achieved an F1 score of 91.58% on the customized dataset. On the TreeLearn dataset, the method attained an F1 score of 97.12%.

Why it matches plant phenotyping methodsスマートフォン画像から生成した森林点群を用いて個体樹木を分割する計算手法を開発し、独自データセットと公開データセットで性能・汎化性を検証しているため、植物状態の取得・抽出法が中心である。

abstractIn this study, we present a Sparse 3D U-Net framework for single-tree segmentation which is predicated on a multi-head attention mechanism.

Open cotton boll detection using LiDAR point clouds and RGB images from unmanned aerial systems ↗

Accurate quantification of open bolls and their distribution is crucial for understanding cotton growth, development, and yield in optimized crop management and enhanced plant breeding. Manual boll counting methods are time-consuming, labor-intensive, and subjective. Leveraging the potential of high-resolution images for high-throughput phenotyping offers a promising avenue for efficient trait quantification. The objectives of this study were to develop methods to detect and count open cotton bolls using LiDAR point cloud and RGB images and to compare the effectiveness of these two data sources. A DJI Phantom 4 RTK Unmanned Aerial System (UAS) equipped with a 4 K RGB camera was used to acquire high-resolution RGB images, and a DJI Matrice 300 RTK with a Zenmuse L1 sensor was used to acquire LiDAR point cloud data. The RGB images were converted to point cloud using photogrammetry by measuring multiple points of overlapping images. The boll detection workflow involved data filtering and clustering using the density-based spatial clustering of applications with noise (DBSCAN) method. Evaluation of the methods involved 48 plots representing small, medium, and large plant sizes using metrics including mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (r²). The methods using both data sources performed well in estimating open bolls, with LiDAR point cloud data slightly outperforming those derived from RGB images. Generally, the performance of the DBSCAN method in boll detection improved with decreasing plant sizes. Specifically, LiDAR data yielded MAPE values of 5.03 %, 8.05 %, and 13.46 %, RMSE values of 7.26, 14.33, and 23.40 bolls per m², and r 2 values of 0.93, 0.84, and 0.84 for small, medium, and large plant sizes, respectively. RGB image-based data exhibited MAPE values of 7.21 %, 6.49 %, and 16.41 %, RMSE values of 11.05, 13.66, and 26.49 bolls per m², and r 2 values of 0.82, 0.74, and 0.83 for small, medium, and large plant sizes, respectively. The method demonstrates the potential of RGB imagery and LiDAR data for estimating boll counts, offering valuable tools for enhanced plant phenotyping in plant breeding and site-specific crop management. Both data sources underestimated boll counts, with smaller plants showing less undercounting, likely due to improved light penetration and separation of bolls. These findings highlight the influence of plant structure on boll detection accuracy and the need to address challenges posed by dense canopies to enhance detection reliability. • The study developed methods to count open cotton bolls using LiDAR point clouds and RGB images. • LiDAR slightly outperformed the RGB image-derived point cloud, with better accuracy for smaller plants due to less canopy density. • Dense canopies reduced detection accuracy, highlighting the influence of plant structure.

Why it matches plant phenotyping methodsLiDARとRGB画像を用いて綿花の開花ボール数を検出・計数する手法を開発し、精度比較・検証しており、植物表現型取得が研究の中心です。

abstractThe objectives of this study were to develop methods to detect and count open cotton bolls using LiDAR point cloud and RGB images and to compare the effectiveness of these two data sources.

Comparative analysis of high-resolution UAV photogrammetry and terrestrial laser scanning for detecting and quantifying urban vegetation changes ↗

Abstract. Extensive urban expansion has significantly impacted green spaces leading to the degradation of urban vegetation. Hence, monitoring variations in vegetation using remote sensing methods is essential. However, 2D remote sensing methods have drawbacks as they lack vertical structures in urban areas, shadows caused by buildings, cloud cover and require substantial preprocessing to encounter these limitations. This study focuses on identifying and quantifying changes in Malminkartano, Helsinki during the leaf-off and leaf-on seasons for the year 2022. The research utilized terrestrial laser scanning (TLS) and UAV-photogrammetry datasets for change detection in urban vegetation and point cloud-based algorithms for seasonal variations such as C2C, C2M, and M3C2. Notably, many existing methods involve rasterizing point clouds as DSM which results in the loss of significant information. Therefore, this paper investigates the potential of utilized datasets in detecting changes directly on point clouds. However, there are uncertainties associated with point clouds including data registration, point density, weather effects, and misalignment therefore this study aims to take these limitations into account. The results from TLS and UAV-photogrammetry demonstrated competence in identifying the maximum growth of urban vegetation up to 2.0 m and 2.8 m respectively. However, the accuracy assessment of data corresponded to a 4 cm difference in both datasets at a 95% confidence threshold and potential vertical height differences accounted for the difference in change detection. This study underscores data processing uncertainties associated with registration, vertical height, and data noise and proposes the integration of point clouds with different sensors for completeness and improved change detection in urban vegetation.

Why it matches plant phenotyping methodsUAVフォトグラメトリと地上レーザースキャンを比較し、点群処理によって都市植生の成長量・高さ変化を定量化する技術的評価が中心である。

abstractThis study focuses on identifying and quantifying changes in Malminkartano, Helsinki during the leaf-off and leaf-on seasons for the year 2022.

Monitoring tropical forests with light drones: ensuring spatial and temporal consistency in stereophotogrammetric products ↗

O_LILight drones provide a cheap and effective tool to monitor forest canopy, especially in tropical and equatorial contexts, where infrastructure and resources are limiting. In these regions, good quality optical satellite images are rare, yet the stakes are maximal to characterize forest function, dynamics, diversity, and phenology, and more generally the vegetation-climate interplay. C_LIO_LIWe describe a complete processing chain based on photogrammetric tools that seeks to optimize the spatial and spectral coherence between repeat image mosaics at centimetric resolution. Our target is to allow individual tree-level monitoring over tens to hundreds of hectare scales with consumer grade equipment (i.e., quadcopter with stabilized RGB camera, standard GNSS positioning). C_LIO_LIWe demonstrate the increase in spatial precision achieved using Time-SIFT and Arosics algorithms, which allow (individually and synergistically) to reduce global and local spatial misalignment between mosaics from several meters to a few centimeters. Time-SIFT provides the advantage of increased robustness in initial image alignment and 3D reconstruction, and hence reduces occasional distortions or data gaps. Using Agisofts color and white balance corrections combined with the use of vegetation indices provides meaningful quantitative signal despite considerable changes in acquisition conditions. C_LIO_LIIn particular, indices that are less sensitive to illumination changes, like the green chromatic coordinate (GCC), allowed evidencing a seasonal signal over four years of monitoring in the evergreen moist forest at Paracou in French Guiana. The signal was decorrelated from obvious geometrical effect (sun height), and provided information on the vegetative stage at tree, species, and stand levels. C_LI Data/Code for peer reviewThe complete processing chain, as well as the data and scripts used for producing the analyses presented here, are available for review on Zenodo: https://zenodo.org/records/15449377?token=eyJhbGciOiJIUzUxMiJ9.eyJpZCI6IjdjNWExMzIzLThkNDMtNDllNy1iYWY4LTY2MGZlZjkyZmQ3OCIsImRhdGEiOnt9LCJyYW5kb20iOiI5MGQ4NTE2YTE4OGViNDQ3YTFiZmMyYTFkZDlhZTZmMiJ9.CsJ0VRuQ90A1qzO1VJC1Q9eXFSp1N5UpeJlyr6otgXRPlf-I-jcwBJ6ytiBbbu8enCNJ2Ke6-oxNV8aeJ_AWIw

Why it matches plant phenotyping methodsドローン画像のステレオフォトグラメトリ処理、画像整合化、色補正、植生指数を組み合わせた処理チェーンを開発・実証し、個体樹木から林分レベルの植生状態・季節性を定量化しているため、植物フェノタイピング手法が中心である。

abstractWe describe a complete processing chain based on photogrammetric tools that seeks to optimize the spatial and spectral coherence between repeat image mosaics at centimetric resolution.

Laboratory and field comparison of onboard and remote sensors for canopy characterisation in vineyards ↗

Accurate canopy characterisation is crucial for the targeted application of plant protection products following the variable rate application (VRA) concept. In this study, two different canopy measurement systems were compared: ultrasonic (US) sensors and UAV-based photogrammetry. A specific device was developed to host a series of US sensors that could conduct a fully automatic canopy characterisation of two vine rows in a single pass. The results of canopy characterisation (canopy width, canopy height, leaf wall area, and tree row volume) were compared with those obtained after complete data processing of the images obtained using a multispectral camera embedded on a UAV. Results indicated that no significant differences have been obtained in the definition of main canopy parameters. Field tests indicated that US sensors offered stable canopy height readings but exhibited variability in width measurements due to factors like ground conditions and sensor placement. Compared to with UAV photogrammetry, US sensors provided comparable results for canopy height and width at a lower cost and with less precision. Therefore, the choice between US sensors and UAVs should consider the resolution requirements, cost, and field conditions. Field data were collected from two commercial vineyards in the Penedès region close to Barcelona (Spain). Before this, laboratory tests were performed using an artificial target to achieve an accurate evaluation of the US sensors. Overall, this study highlighted the potential of ground-based sensing systems for precise and repeatable canopy measurements, contributing to improved vineyard management practices and advanced technological integration for agricultural monitoring.

Why it matches plant phenotyping methodsブドウ樹冠の形態形質を取得する超音波センサーとUAV画像法を開発・比較検証しており、フェノタイピング手法が研究の中心である。

abstractA specific device was developed to host a series of US sensors that could conduct a fully automatic canopy characterisation of two vine rows in a single pass.

Morphometric analysis of turfgrass using digital three‐dimensional technology and its application in breeding ↗

Abstract Advancements in digital three‐dimensional (3D) imaging technology have enabled precise, high‐throughput, and non‐destructive phenotyping of plant morphology. In this study, we developed a digital phenotyping system specifically tailored for zoysiagrass ( Zoysia species), integrating image‐based 3D model reconstruction, machine learning, and computational trait analysis. By employing a structure from motion approach, we reconstructed detailed 3D models of zoysiagrass using four industrial cameras and an automated imaging platform. A machine learning algorithm was applied to accurately isolate plant components from non‐plant elements. From these segmented models, we extracted key morphological traits—height, spread area, color, and volume—providing a comprehensive dataset for breeding applications. As a digitally derived trait, volume offers new potential in characterizing plant architecture and assessing yield‐related traits non‐destructively. Additionally, we developed a small‐scale, low‐cost prototype system using Raspberry Pi and LEGO‐based components, demonstrating the scalability and adaptability of 3D phenotyping systems across various experimental settings and budgets. Although 3D phenotyping under controlled conditions using potted plants is not directly transferable to field‐based evaluation, it provides essential, reproducible data that bridge early‐stage screening and later field validation in breeding programs. These digital morphological measurements are expected to enhance the precision, repeatability, and objectivity of turfgrass evaluation. As 3D technologies continue to evolve and integrate with genomic and environmental data, digital phenotyping will play an increasingly important role in accelerating turfgrass improvement and promoting data‐driven plant breeding.

Why it matches plant phenotyping methods植物形態を対象とする3D画像フェノタイピングシステムを開発し、機械学習による分離と形態形質抽出、さらに低コスト試作機まで扱っており、フェノタイピング手法が研究の中心である。

abstractwe developed a digital phenotyping system specifically tailored for zoysiagrass ( Zoysia species), integrating image‐based 3D model reconstruction, machine learning, and computational trait analysis.

The assessment of individual tree canopies using drone-based intra-canopy photogrammetry ↗

With many forests experiencing rapidly declining health, effective management requires increasingly accurate and precise tools to measure tree attributes across scales. Tree health, especially in deciduous species, is strongly correlated with crown condition, specifically crown transparency and dieback. Present-day assessment of these attributes is undertaken using ground-based visual approaches, which can be imprecise and subjective. Here we evaluate the feasibility of applying drone-based digital aerial photogrammetry (DAP) below, within, and above the tree canopy to estimate tree height, diameter at breast height, canopy transparency, and canopy spread. Video imagery was acquired across 18 deciduous trees under leaf-off and leaf-on conditions in Metro Vancouver, British Columbia, Canada, using small, lightweight first-person-view drones. Images were extracted and processed into coloured 3D point clouds using digital Structure-from-Motion Multiview-Stereo photogrammetry. Photogrammetry estimates were compared with field measurements and above-canopy drone-based aerial Light Detection and Ranging (lidar) estimates. The DAP estimates explained significant variance in the field observations and were strongly correlated with both ground-based measurements and lidar estimates, with correlations of height (DAP vs. ground: r = 0.93, RMSE = 1.54 m; DAP vs. lidar: r = 0.94), DBH (DAP vs. ground: r = 0.98, RMSE = 2.90 cm), transparency (DAP vs. ground: r = 0.66, RMSE = 12.61 %), and crown spread (DAP vs. ground: r = 0.88, RMSE = 3.35 m; DAP vs. lidar: r = 0.89). The reconstruction time for each tree using the drone footage was strongly correlated with tree size and seasonal condition, with minimal influence from crown form. This work suggests that first-person view drones can provide accurate information on individual tree attributes associated with tree health, offering a reliable alternative or complement to both ground-based methods and lidar for tree-level measurements in ongoing forest health assessment programs.

Why it matches plant phenotyping methodsドローン画像とSfM-MVSフォトグラメトリにより、樹冠透明度・枯れ込み・樹高・樹冠広がりなどの個体樹木形質を推定し、地上測定およびLiDARと比較検証しているため、手法が中心的である。

abstractHere we evaluate the feasibility of applying drone-based digital aerial photogrammetry (DAP) below, within, and above the tree canopy to estimate tree height, diameter at breast height, canopy transparency, and canopy spread.

Cotton3DGaussians: Multiview 3D Gaussian Splatting for boll mapping and plant architecture analysis ↗

Cotton is an economically important crop cultivated worldwide for textile production. Breeding programs focus on selecting genotypes with favorable traits for high yields. This study introduced 3D Gaussian Splatting (3DGS) to reconstruct high-fidelity three-dimensional (3D) models and developed a segmentation workflow, Cotton3DGaussians, to analyze cotton bolls and extract architectural traits from single plants. Cotton plants were scanned 360° using a smartphone, and photogrammetry was used to estimate camera parameters and reconstruct a sparse point cloud, which was then optimized into a 3DGS model. In Cotton3DGaussians, 2D masks of bolls segmented from four views were mapped to 3D space, and redundant bolls were removed through cross-view clustering. YOLOv11x and a foundation model, segment anything model (SAM), were compared to obtain 2D masks, with YOLOv11x achieving an F1-score 5.9 % higher than SAM. Phenotypic traits such as boll number, volume, plant height, and canopy size were estimated. The 3DGS model exhibited superior rendering quality, achieving a peak signal-to-noise ratio (PSNR) that was 6.91 higher than NeRF. Cotton3DGaussians effectively segmented 3D bolls from multiple views, with mean absolute percentage errors (MAPE) of 9.23 % for boll number, 3.66 % for canopy size, 2.38 % for plant height, and 8.17 % for boll volume compared to LiDAR ground truth. The regression analysis between convex boll volume and boll weight showed a 19.3 % weight error per plant. This study demonstrates the potential of 3DGS for low-cost, high-fidelity 3D modeling, enabling high-resolution phenotyping and advancing cotton breeding programs. The methodology can also be applied to other crops for improved 3D trait measurement research and enhanced productivity.

Why it matches plant phenotyping methods3Dガウシアンスプラッティングと多視点画像・セグメンテーションを組み合わせ、ワタのボールおよび植物体形質を抽出・検証する手法が研究の中心である。

abstractThis study introduced 3D Gaussian Splatting (3DGS) to reconstruct high-fidelity three-dimensional (3D) models and developed a segmentation workflow, Cotton3DGaussians, to analyze cotton bolls and extract architectural traits from single plants.

Multi-temporal analysis of urban vegetation using deep learning and 3D reconstruction ↗

Abstract Context Urban green spaces play a vital role in enhancing environmental quality and human well-being. However, traditional assessment methods, such as the green view index, primarily quantify green coverage while neglecting vegetation diversity, color richness, and seasonal dynamics, which are critical for urban livability. Objectives This study develops a multi-temporal and multi-perspective analysis framework for urban green space visualization, introducing the Seasonal Species-Specific Plant View Index (S3PVI) to quantify plant coverage at the species level, capturing seasonal changes and visual diversity. Methods The framework integrates computer vision, deep learning, and 3D reconstruction technologies, including structure from motion and 3D Gaussian splatting. To validate the S3PVI, case studies were conducted in Suita City, Japan, analyzing real-world seasonal vegetation patterns and testing the framework in a virtual park environment to assess its applicability in urban design. Results The S3PVI effectively captured species-specific seasonal patterns, with cherry blossoms peaking at 45.61% visibility in spring and maples at 56.78% in autumn. Comparative analysis revealed distinctive vegetation strategies between streets, with Sanshikisaido showing higher seasonal amplitude but lower consistency than Nakayoshido. Virtual simulations confirmed that multi-species schemes optimally balanced seasonal impact with year-round visual stability. Conclusions The S3PVI framework advances urban vegetation assessment by providing species-specific and seasonally dynamic visual data, supporting evidence-based urban planning for ecological sustainability and livability. Potential applications include brownfield redevelopment, virtual park planning, and urban design simulations.

Why it matches plant phenotyping methods植物の種別・季節別被覆を定量化するS3PVIを開発し、深層学習・コンピュータビジョン・3D再構成で検証しており、植物状態の取得・抽出手法が中心である。

abstractThis study develops a multi-temporal and multi-perspective analysis framework for urban green space visualization, introducing the Seasonal Species-Specific Plant View Index (S3PVI) to quantify plant coverage at the species level, capturing seasonal changes and visual diversity.

Efficient and cost-effective monitoring of urban green spaces using combination of photogrammetry, LiDAR, and RTK in an iPhone-Based approach ↗

Abstract Urban green spaces (UGS) provide various ecological, cultural, aesthetic, and psychological functions contributing to public health and well-being. To ensure proper management and the optimal performance of all these functions, it is essential to closely monitor their structure: number of trees, species composition, tree size, health status, location and surrounding objects. Traditionally, parameters are measured using conventional methods: calipers, altimeters, and diameter tapes. However, while modern 3D data collection technologies such as terrestrial and mobile laser scanners or photogrammetry have been employed for monitoring UGS, their use is often challenging. Therefore, we aimed to test and create a methodology using a novel device (Pix4D & Emlid Scanning Kit), which uniquely combines photogrammetry and light detection and ranging with real-time kinematics in a smartphone. Since the solution is smartphone-based, it provides relatively low-cost employability. The research was conducted in Adolf Priesol Park in Zvolen (Slovakia) over an area of approximately 6,000 m 2 . We focused on the device’s positional accuracy in measuring footpaths, park amenities, and trees, as well as its tree detection rate and accuracy in determining tree diameter. The results demonstrated that the device exhibited high positional accuracy (horizontal RMSE = 0.08 m, vertical RMSE = 0.07 m), a 100% tree detection rate, and exceptional diameter at the breast height accuracy, with an RMSE of 1.1 cm in the area-based approach and 0.42 cm in the individual approach. Notably, the 6,000 m 2 area was covered in 80 minutes, including collecting almost 85 trees, all footpaths and all park amenities.

Why it matches plant phenotyping methodsスマートフォン搭載の写真測量・LiDAR・RTKを組み合わせた樹木計測手法を開発・検証し、樹木検出率と胸高直径の精度を評価しているため、植物形質取得法が研究の中心である。

abstractwe aimed to test and create a methodology using a novel device (Pix4D & Emlid Scanning Kit), which uniquely combines photogrammetry and light detection and ranging with real-time kinematics in a smartphone.

Using DAP-RPA Point Cloud-Derived Metrics to Monitor Restored Tropical Forests in Brazil ↗

Monitoring forest structure, diversity, and biomass in restoration areas is both expensive and time-consuming. Metrics derived from digital aerial photogrammetry (DAP) may offer a cost-effective and efficient alternative for monitoring forest restoration. The main objective of this study was to use metrics derived from digital aerial photogrammetry (DAP) point clouds obtained by remotely piloted aircraft (RPA) to estimate aboveground biomass (AGB), species diversity, and structural variables for monitoring restored secondary tropical forest areas. The study was conducted in three active and one passive forest restoration systems located in a secondary forest in Sergipe state, Brazil. A total of 2507 tree individuals from 36 plots (0.0625 ha each) were identified, and their total height (ht) and diameter at breast height (dbh) were measured in the field. Concomitantly with the field inventory, the plots were mapped using an RPA, and traditional height-based point cloud metrics and Fourier transform-derived metrics were extracted for each plot. Regression models were developed to calculate AGB, Shannon diversity index (H′), ht, dbh, and basal area (ba). Furthermore, multivariate statistical analyses were used to characterize AGB and H′ in the different restoration systems. All fitted models selected Fourier transform-based metrics. The AGB estimates showed satisfactory accuracy (R2 = 0.88; RMSE = 31.2%). The models for H′ and ba also performed well, with R2 values of 0.90 and 0.67 and RMSEs of 24.8% and 20.1%, respectively. Estimates of structural variables (dbh and ht) showed high accuracy, with RMSE values close to 10%. Metrics derived from the Fourier transform were essential for estimating AGB, species diversity, and forest structure. The DAP-RPA-derived metrics used in this study demonstrate potential for monitoring and characterizing AGB and species richness in restored tropical forest systems.

Why it matches plant phenotyping methodsDAP-RPA点群から森林のAGB、構造形質、種多様性を推定する方法を開発・検証しており、植物状態の取得・推定が研究の中心です。

abstractMetrics derived from digital aerial photogrammetry (DAP) may offer a cost-effective and efficient alternative for monitoring forest restoration.

Comparison of Advanced Terrestrial and Aerial Remote Sensing Methods for Above-Ground Carbon Stock Estimation—A Comparative Case Study for a Hungarian Temperate Forest ↗

The increasing pace of climate-driven changes in forest ecosystems calls for reliable remote sensing techniques for quantifying above-ground carbon storage. In this article, we compare the methodology and results of traditional field surveys, mobile laser scanning, optical drone imaging and photogrammetry, and both drone-based and light aircraft-based aerial laser scanning to determine forest stand parameters, which are suitable to estimate carbon stock. Measurements were conducted at four designated sampling points established during a large-scale project in deciduous and coniferous tree stands of the Dudles Forest, Hungary. The results of the surveys were first compared spatially and quantitatively, followed by a summary of the advantages and disadvantages of each method. The mobile laser scanner proved to be the most accurate, while optical surveying—enhanced with a new diameter measurement methodology based on detecting stem positions from the photogrammetric point cloud and measuring the diameter directly on the orthorectified images—also delivered promising results. Aerial laser scanning was the least accurate but provided coverage over large areas. Based on the results, we recommend adapting our carbon stock estimation methodology primarily to mobile laser scanning surveys combined with aerial laser scanned data.

Why it matches plant phenotyping methods森林の立木径や林分パラメータをリモートセンシングで推定する手法を比較・評価し、画像点群から幹位置を検出して直径を測る新手法も提示しているため、植物形質取得法が中心である。

abstractwe compare the methodology and results of traditional field surveys, mobile laser scanning, optical drone imaging and photogrammetry, and both drone-based and light aircraft-based aerial laser scanning to determine forest stand parameters

Comparing airborne laser scanning and UAV photogrammetry for estimating aboveground biomass of individual urban trees in Helsinki ↗

The rapid urbanization trend is posing a threat to urban green spaces (UGS) worldwide. Consequently, scientific research has addressed the need to develop monitoring methods for UGS and their attributes. One example is the estimation of Aboveground biomass (AGB) using Airborne laser scanning (ALS) and UAV photogrammetry , which are applied to measure attributes of urban vegetation from three-dimensional (3D) point clouds. The aim of this study is to examine and compare the use of ALS and drone photogrammetry with allometric models to estimate the AGB of urban trees on a neighbourhood-level city area. 3D point clouds generated by means of ALS and UAV photogrammetry were used to study a residential area located in Helsinki, Finland. The point clouds were classified and automatic Individual tree detection ( ) method was utilized to extract urban trees. In-situ measurements were conducted to estimate DBH and assess the accuracy of the tree detection. Species-specific and generic allometric models were used to determine the AGB of urban vegetation. results revealed an overestimation of detected trees from both datasets, and differences in DBH, tree height, and volume were observed in both datasets. Notably, AGB estimation results indicated that species-specific models showed a lower estimation compared to generic allometric models. Overall, the results suggested that ALS and UAV photogrammetry can be used for mapping biomass in urban residential areas, as both methods are cost-effective and time-efficient. However, future research should focus on monitoring low vegetation types such as shrubs and estimating the biomass as existing methods are specific for certain tree species and not suitable for broader vegetation types beyond individual trees.

Why it matches plant phenotyping methodsALSとUAVフォトグラメトリによる3D点群から個体樹木の検出、DBH・樹高・体積・地上部バイオマスを推定し、精度比較を行うことが研究の中心であるため、植物表現型計測手法の実質的な適用・評価に該当する。

abstractThe aim of this study is to examine and compare the use of ALS and drone photogrammetry with allometric models to estimate the AGB of urban trees on a neighbourhood-level city area.

Shoot architectural analysis for olive cultivar characterization: an automatic internode measurement procedurent procedure ↗

Tree architecture, defined as the arrangement in space of the elements above the ground, is closely related to the biological and physiological processes of the tree. In particular, the quantitative study of the branch consists of classifying branches into orders, estimating lengths, insertion angles, diameters and volumes. In the case of the olive tree (Olea europaea L.), the knowledge of its architecture is important to determine which varieties are suitable for high-density planting systems and to guide canopy pruning, which allow simplification of field management and reduction of costs. Up to date, measurements are mainly done manually, using measuring tape and caliper, with high time expense and operator-related uncertainty. In this study, the analysis is extended to the three-dimensional case using photogrammetry. Next, the point cloud is processed using a modified version of the open-source code TreeQSM (version 2.4.1). Moreover, a new methodology, based on photogrammetry and branch segmentation using TreeQSM, is proposed to measure not only average branch diameter, but also node diameter and internodal distance along the principal axis of the twig point cloud. The main characteristics of the principal axis of the twig are obtained to prove the validity of the proposed method. The node average diameter is 2.94 mm with a standard deviation of 1.20 mm while the average internode is 14.38 mm with a standard deviation of 7.78 mm.

Why it matches plant phenotyping methodsオリーブ樹の枝構造・節径・節間距離を、フォトグラメトリ、点群処理、TreeQSMによって自動抽出する植物表現型計測法を開発・検証しており、方法が研究の中心である。

abstracta new methodology, based on photogrammetry and branch segmentation using TreeQSM, is proposed to measure not only average branch diameter, but also node diameter and internodal distance along the principal axis of the twig point cloud.

Advancing plant biomass measurements: Integrating smartphone‐based 3D scanning techniques for enhanced ecosystem monitoring ↗

Abstract New technological developments open novel possibilities for widely applicable methods of ecosystem analyses. We investigated a novel approach using smartphone‐based 3D scanning for non‐destructive, high‐resolution monitoring of above‐ground plant biomass. This method leverages Structure from Motion (SfM) techniques with widely accessible smartphone apps and subsequent computing to generate detailed ecological data. By implementing a streamlined pipeline for point cloud processing and voxel‐based analysis, we enable frequent, cost‐effective and accessible monitoring of vegetation structure and plant community biomass. Conducted in long‐term experimental grasslands, our study reveals a high correlation ( R 2 up to 0.9) between traditional biomass harvesting and 3D volume estimates derived from smartphone‐generated point clouds, validating the method's accuracy and reliability. Additionally, results indicate significant effects of plant species richness and fertilization on biomass production and volume estimates, underscoring the potential for high‐resolution temporal and spatial analyses of vegetation dynamics. This method's innovation extends beyond traditional practices with implications for future integration of AI to automate species segmentation, ecological trait extraction and predictive modelling. The simplicity and accessibility of the smartphone‐based approach facilitate broader engagement in ecosystem monitoring, encouraging citizen science participation and enhancing data collection efforts. Future research will make it possible to refine the accuracy of point cloud processing, expand applications across diverse vegetation types and explore new possibilities in ecological monitoring, modelling and its application in ecosystem analyses and biodiversity research.

Why it matches plant phenotyping methodsスマートフォンの3Dスキャン、SfM、点群・ボクセル解析によって植物バイオマスを推定する手法を開発・検証しており、植物形質取得が研究の中心である。

abstractWe investigated a novel approach using smartphone‐based 3D scanning for non‐destructive, high‐resolution monitoring of above‐ground plant biomass.

HALF: Histogram of Angles in Linked Features for 3D Point Cloud Data Segmentation of Plants for Robust Sensing ↗

This paper presents a novel method, Histogram of Angles in Linked Features (HALF), designed for the segmentation of 3D point cloud data of plants for robust sensing. The proposed method leverages local angular features extracted from 3D measurements obtained via sensing technologies such as laser scanning, LiDAR, or photogrammetry. HALF enables efficient identification of plant structures-leaves, stems, and knots-without requiring large-scale labeled datasets, making it highly suitable for applications in plant phenotyping and structural analysis. To enhance robustness and interpretability, we extend HALF to a convolution-based mathematical framework and introduce the Sequential Competitive Segmentation Algorithm (SCSA) for phytomer-level classification. Experimental results using 3D point cloud data of soybean plants demonstrate the feasibility of our method in sensor-based plant monitoring systems. By providing a low-cost and efficient approach for plant structure analysis, HALF contributes to the advancement of sensor-driven plant phenotyping and precision agriculture.

Why it matches plant phenotyping methods植物の3D点群から葉・茎・節などの構造を分割・分類する新規センシング手法を開発し、植物フェノタイピングへの適用可能性を実証しているため。

abstractThis paper presents a novel method, Histogram of Angles in Linked Features (HALF), designed for the segmentation of 3D point cloud data of plants for robust sensing.

UAV-enabled evaluation of forestry plantations: A comprehensive assessment of laser scanning and photogrammetric approaches ↗

The use of unmanned aerial vehicles (UAVs), particularly with high-density point clouds obtained through UAV laser scanning (ULS) and UAV structure from motion (UAV-SfM) techniques, offer cost-effective alternatives for forest inventory. However, the literature lacks comprehensive assessments of their limitations across diverse ranges of age classes and site conditions. This study addressed this gap by evaluating the estimation accuracy of crucial tree attributes, diameter at breast height (DBH) and tree height, in a range of age classes within forest plantations. In addition, this study thoroughly evaluated the performance of ULS and UAV-SfM in diverse site conditions using point clouds obtained from Pinus radiata D. Don plantations, a widely planted commercial timber species worldwide. To achieve this, UAV and field data were gathered from twelve sites, including multitemporal data for four sites. By employing an automated data processing pipeline, individual trees were segmented and structural metrics extracted from tree segments to estimate DBH and tree height at an individual tree level. Results indicated that UAV-SfM and ULS performed comparably in estimating DBH over the entire dataset, with R 2 values of 0.67 and 0.74 and RMSE values of 2.05 cm (11%) and 2.13 cm (11%) respectively. However, ULS generally outperformed UAV-SfM at the site level, achieving higher R 2 values (0.46-0.90 vs 0.21-0.85) and RMSE values (0.33–7.24 cm at 7–24% vs. 0.35–6.15 cm at 8–17%). ULS also consistently outperformed UAV-SfM in tree height measurements across sites, with an average per site RMSE of 0.68 m (5.4%) compared with 1.21 m (11.59%), demonstrating its robustness in diverse conditions. Site-specific factors such as stand maturity and logging debris affected measurement reliability in both datasets, with accuracy improving for younger sites, sites with a more open canopy and more favourable site conditions (less logging debris and weed cover). The study also indicated a moderate relationship between ground sampling distance (GSD) of the imagery and UAV-SfM accuracy. The findings highlight the significance of considering site-specific variables when choosing UAV technologies for conducting forest inventory, ensuring informed decisions in UAV-based forest inventory practices. Consequently, the insights gained from this research hold significant importance for practical forestry applications.

Why it matches plant phenotyping methodsULSとUAV-SfMを比較検証し、個体木レベルでDBHと樹高を推定する自動処理パイプラインの精度・頑健性を評価しており、植物形質取得法が研究の中心である。

abstractThis study addressed this gap by evaluating the estimation accuracy of crucial tree attributes, diameter at breast height (DBH) and tree height, in a range of age classes within forest plantations.

Assessment of grapevine bunch withering: advances in fruit 3D morphology and colour evaluation ↗

The agro-industrial sector is experiencing a new wave of innovation driven by goods-inspecting devices designed to optimise operations, improve product quality, and reduce yield losses. Grape withering is widely used to concentrate berry juice for raisin and sweet wine production. While this process alters wine characteristics, it also introduces costs and risks, as pathogen infections can compromise the quality of the final product. This study presents a reliable three-dimensional analysis for assessing grape colour and bunch morphology to evaluate infection risk and drying performance. Twenty Vitis vinifera bunches were dried under environmental conditions. Colour analysis focused on the distribution of colours in healthy versus rotten bunches. Three-dimensional digital replicas were generated with two methods: i) photogrammetry, and ii) a recently developed artificial intelligence model. The point clouds and meshes output from the two approaches were compared, and morphometric traits were directly measured, including volume, surface area, and both horizontal and vertical sections for each bunch. Key geometrical descriptors of the bunch's horizontal sections and individual berries were found to be relevant for classifying the risk of bunch rot. Additionally, morphometric traits related to bunch compactness were linked to drying speed. A linear model incorporating three-dimensional descriptors was developed to estimate weight loss during withering, achieving an R² value of 0.98 and a relative error of 0.07. The artificial intelligence-based technique produced lower-quality models for grape reconstruction, but the selected morphometric traits remained effective.

Why it matches plant phenotyping methodsブドウ房の3D形態・色を画像から取得し、形態形質による腐敗リスクと乾燥性能の評価手法を開発・比較しているため、フェノタイピング手法が中心です。

abstractThis study presents a reliable three-dimensional analysis for assessing grape colour and bunch morphology to evaluate infection risk and drying performance.

Soybean yield estimation and lodging discrimination based on lightweight UAV and point cloud deep learning ↗

The unmanned aerial vehicle (UAV) platform has emerged as a powerful tool in soybean (Glycine max (L.) Merr.) breeding phenotype research due to its high throughput and adaptability. However, previous studies have predominantly relied on statistical features like vegetation indices and textures, overlooking the crucial structural information embedded in the data. Feature fusion has often been confined to a one-dimensional exponential form, which can decouple spatial and spectral information and neglect their interactions at the data level. In this study, we leverage our team's cross-circling oblique (CCO) route photography and Structure-from-Motion with Multi-View Stereo (SfM-MVS) techniques to reconstruct the three-dimensional (3D) structure of soybean canopies. Newly point cloud deep learning models SoyNet and SoyNet-Res were further created with two novel data-level fusion that integrate spatial structure and color information. Our results reveal that incorporating RGB color and vegetation index (VI) spectral information with spatial structure information, leads to a significant reduction in root mean square error (RMSE) for yield estimation (22.55 ​kg ​ha⁻¹) and an improvement in F1-score for five-class lodging discrimination (0.06) at S7 growth stage. The SoyNet-Res model employing multi-task learning exhibits better accuracy in both yield estimation (RMSE: 349.45 ​kg ​ha⁻¹) when compared to the H2O-AutoML. Furthermore, our findings indicate that multi-task deep learning outperforms single-task learning in lodging discrimination, achieving an accuracy top-2 of 0.87 and accuracy top-3 of 0.97 for five-class. In conclusion, the point cloud deep learning method exhibits tremendous potential in learning multi-phenotype tasks, laying the foundation for optimizing soybean breeding programs.

Why it matches plant phenotyping methodsUAV・SfM-MVSによる植物キャノピー3D再構成と、収量推定・倒伏識別のための新規点群深層学習モデル開発が研究の中心であり、植物表現型取得・推定手法に該当する。

abstractIn this study, we leverage our team's cross-circling oblique (CCO) route photography and Structure-from-Motion with Multi-View Stereo (SfM-MVS) techniques to reconstruct the three-dimensional (3D) structure of soybean canopies.

FS-MVSNet: A Multi-View Image-Based Framework for 3D Forest Reconstruction and Parameter Extraction of Single Trees ↗

With the rapid advancement of smart forestry, 3D reconstruction and the extraction of structural parameters have emerged as indispensable tools in modern forest monitoring. Although traditional methods involving LiDAR and manual surveys remain effective, they often entail considerable operational complexity and fluctuating costs. To provide a cost-effective and scalable alternative, this study introduces FS-MVSNet—a multi-view image-based 3D reconstruction framework incorporating feature pyramid structures and attention mechanisms. Field experiments were performed in three representative forest parks in Beijing, characterized by open canopies and minimal understory, creating the optimal conditions for photogrammetric reconstruction. The proposed workflow encompasses near-ground image acquisition, image preprocessing, 3D reconstruction, and parameter estimation. FS-MVSNet resulted in an average increase in point cloud density of 149.8% and 22.6% over baseline methods, and facilitated robust diameter at breast height (DBH) estimation through an iterative circle-fitting strategy. Across four sample plots, the DBH estimation accuracy surpassed 91%, with mean improvements of 3.14% in AE, 1.005 cm in RMSE, and 3.64% in rRMSE. Further evaluations on the DTU dataset validated the reconstruction quality, yielding scores of 0.317 mm for accuracy, 0.392 mm for completeness, and 0.372 mm for overall performance. The proposed method demonstrates strong potential for low-cost and scalable forest surveying applications. Future research will investigate its applicability in more structurally complex and heterogeneous forest environments, and benchmark its performance against state-of-the-art LiDAR-based workflows.

Why it matches plant phenotyping methods単木の3D再構成とDBHという明示的な植物構造形質の推定を行う画像ベース手法を開発し、ベースラインおよびデータセットで技術検証しているため。

abstractthis study introduces FS-MVSNet—a multi-view image-based 3D reconstruction framework incorporating feature pyramid structures and attention mechanisms.

Comparing Canopy Height Models from Regional-Scale Aerial Photogrammetry with Global Spaceborne Lidar-Derived Data for Estimating Forest Volume and Biomass ↗

Abstract has not been obtained from indexed metadata or an accessible article page.

Why it matches plant phenotyping methods航空写真測量と衛星LiDAR由来データを比較し、森林の樹冠高モデルから森林体積・バイオマスを推定する測定法の技術比較・検証が中心であるため。

titleComparing Canopy Height Models from Regional-Scale Aerial Photogrammetry with Global Spaceborne Lidar-Derived Data for Estimating Forest Volume and Biomass

The Role of Land Use Intensity on Vegetation Recovery Gradients After Land Abandonment in the Semiarid SE Alentejo, Portugal ↗

Abstract. Understanding how land use history shapes secondary succession is crucial for semiarid regions, where human pressures intersect with climatic variability. This study examines the Serra de Serpa e Mértola, in SE Alentejo, Portugal, an area marked by extensive land conversion and degradation over the 20th century. Drawing on long-term monitoring of experimental plots at the Vale Formoso Soil Erosion Centre, we leverage UAV-based photogrammetry to quantify vegetation recovery dynamics from 2018 to 2024. By focusing on canopy height models (CHM), we move beyond traditional spectral indices to directly assess biomass accumulation over time. Preliminary findings reveal clear differences in recovery trajectories linked to historical land use intensity and climate conditions. Plots with lower historical disturbance, such as light grazing or spontaneous vegetation, exhibit faster recovery, while intensively managed plots show slower biomass gains and signs of ecological thresholds. Our results highlight the value of UAV monitoring for understanding how abandonment starting points influence ecosystem resilience under varying climatic pressures, offering insights for restoration strategies in Mediterranean semiarid landscapes.

Why it matches plant phenotyping methodsUAVフォトグラメトリで植生キャノピー高を推定し、植生回復とバイオマス蓄積という植物状態をプロット単位で定量化する手法適用が研究の中心です。

abstractwe leverage UAV-based photogrammetry to quantify vegetation recovery dynamics from 2018 to 2024.

Use of low-cost RGB images to estimate nitrogen, chlorophyll, and leaf area status in basil ‘Nufar’ ↗

Nitrogen is a critical nutrient for basil (Ocimum basilicum L.), significantly influencing chlorophyll synthesis, leaf area development, and plant productivity. Traditional nitrogen assessment methods are destructive, time-consuming, and costly, limiting their practicality. Low-cost red-green-blue (RGB) imaging offers a promising alternative for the rapid, nondestructive estimation of plant nutritional status; however, its applicability in aromatic herbs like basil remains underexplored. This study evaluated using RGB imaging to estimate leaf area, nitrogen, and chlorophyll status in basil ‘Nufar’ as a tool for precision nutrient management. Using the Steiner nutrient solution, the basil plants were grown in a greenhouse under five nitrogen levels (0, 4, 8, 12, and 16 [mEq∙L−1] of NO3−NO3−). Weekly RGB images were acquired and processed through photogrammetric restitution to generate scaled orthomosaics. Seventeen spectral vegetation indices were obtained and correlated with reference measurements of nitrogen, chlorophyll, and leaf area by regression analysis. Results showed that a nitrogen level of 16 mEq∙L−1 NO3− significantly enhanced plant growth, development, and green coloration. Among the indices evaluated, the Normalized Green-Red Difference Index (NGRDI), Red Index (RI), and Color Index of Vegetation Extraction (CIVE) exhibited the strongest correlations with nitrogen concentration (r = 0.92–0.93), chlorophyll concentration (r = 0.94–0.97), and leaf area (r = 0.93–0.97). These findings confirm that low-cost RGB imaging provides an accurate and efficient method for monitoring nitrogen status, chlorophyll content, and leaf area in basil ‘Nufar.’ This approach offers a valuable tool for optimizing nutrient management and yield prediction in this economically important medicinal and aromatic species.

Why it matches plant phenotyping methods低コストRGB画像とフォトグラメトリによる植物栄養状態・葉面積・クロロフィル推定が研究の中心であり、画像取得・処理と指標の技術評価を実施している。

abstractLow-cost red-green-blue (RGB) imaging offers a promising alternative for the rapid, nondestructive estimation of plant nutritional status

A 3D Modeling Framework for Quantifying Variation in Soybean Root Structure Architecture ↗

Root system architecture (RSA) underpins plant access to water and nutrients, making its characterization critical for improving crop performance in environments with limited soil fertility. However, current methods for quantifying root features face several challenges. They may rely on 2D images that suffer from occlusion, use expensive sensing technologies like X-ray computed tomography, or depend on 3D modeling approaches with assumptions about branching that make them difficult to generalize. To address these challenges, we introduce an open-source Python framework for quantifying RSA samples from 3D point clouds generated from low-cost photogrammetry. Critically, this method incorporates no assumptions about taxon-specific branching orientation, making it both well-suited for modeling naturally grown annual dicots such as soybean and generalizable across species. Using field-grown soybean as a test case, we demonstrate the utility of this framework to extract biologically meaningful 3D features of divergent root systems sampled across developmental stages and soil environments, and enable new analyses not possible with 2D approaches, such as modeling metabolic scaling relationships. Results indicate that, in our soybean samples, while certain individual features like taproot tortuosity are potentially influenced by the soil environment, and while roots in sandy loam exhibited greater feature plasticity, fundamental scaling properties remain consistent. By combining low-cost photogrammetry with 3D reconstruction of root systems from point clouds, this approach provides the plant science community with new opportunities for more comprehensive root studies.

Why it matches plant phenotyping methods植物根系構造を3D点群から定量化するオープンソース手法の開発が中心であり、低コスト写真測量と3D再構成による形態形質抽出を実証している。

abstractwe introduce an open-source Python framework for quantifying RSA samples from 3D point clouds generated from low-cost photogrammetry.

Estimating canopy leaf angle from leaf to ecosystem scale: a novel deep learning approach using unmanned aerial vehicle imagery. ↗

Leaf angle distribution (LAD) impacts plant photosynthesis, water use efficiency, and ecosystem primary productivity, which are crucial for understanding surface energy balance and climate change responses. Traditional LAD measurement methods are time-consuming and often limited to individual sites, hindering effective data acquisition at the ecosystem scale and complicating the modeling of canopy LAD variations. We present a deep learning approach that is more affordable, efficient, automated, and less labor-intensive than traditional methods for estimating LAD. The method uses unmanned aerial vehicle images processed with structure-from-motion point cloud algorithms and the Mask Region-based convolutional neural network. Validation at the single-leaf scale using manual measurements across three plant species confirmed high accuracy of the proposed method (Pachira glabra: R 2 = 0.87, RMSE = 7.61°; Ficus elastica: R 2 = 0.91, RMSE = 6.72°; Schefflera macrostachya: R 2 = 0.85, RMSE = 5.67°). Employing this method, we efficiently measured leaf angles for 57 032 leaves within a 30 m × 30 m plot, revealing distinct LAD among four representative tree species: Melodinus suaveolens (mean inclination angle 34.79°), Daphniphyllum calycinum (31.22°), Endospermum chinense (25.40°), and Tetracera sarmentosa (30.37°). The method can efficiently estimate LAD across scales, providing critical structural information of vegetation canopy for ecosystem modeling, including species-specific leaf strategies and their effects on light interception and photosynthesis in diverse forests.

Why it matches plant phenotyping methodsUAV画像と深層学習を用いて葉角度分布を推定する手法を開発し、手動測定で検証した植物フェノタイピング研究。

abstractWe present a deep learning approach that is more affordable, efficient, automated, and less labor-intensive than traditional methods for estimating LAD.

Estimating the Diameter at Breast Height of Trees in a Forest from RGB ↗

Forest inventories rely on accurate measurements of the diameter at breast height (DBH) for ecological monitoring, resource management, and carbon accounting. While LiDAR-based techniques can achieve centimeter-level precision, they are cost-prohibitive and operationally complex. We present a low-cost alternative that only needs a consumer-grade 360 video camera. Our semi-automated pipeline comprises of (i) a dense point cloud reconstruction using Structure from Motion (SfM) photogrammetry software called Agisoft Metashape, (ii) semantic trunk segmentation by projecting Grounded Segment Anything (SAM) masks onto the 3D cloud, and (iii) a robust RANSAC-based technique to estimate cross section shape and DBH. We introduce an interactive visualization tool for inspecting segmented trees and their estimated DBH. On 61 acquisitions of 43 trees under a variety of conditions, our method attains median absolute relative errors of 5-9% with respect to "ground-truth" manual measurements. This is only 2-4% higher than LiDAR-based estimates, while employing a single 360 camera that costs orders of magnitude less, requires minimal setup, and is widely available.

Why it matches plant phenotyping methodsRGB映像から樹木のDBHという明示的な植物形態形質を推定する半自動パイプラインを開発し、手動測定およびLiDARと比較して精度検証しているため、植物フェノタイピング手法が中心である。

abstractWe present a low-cost alternative that only needs a consumer-grade 360 video camera.

A Method for Identifying Picking Points in Safflower Point Clouds Based on an Improved PointNet++ Network ↗

To address the challenge of precise picking point localization in morphologically diverse safflower plants, this study proposes PointSafNet—a novel three-stage 3D point cloud analysis framework with distinct architectural and methodological innovations. In Stage I, we introduce a multi-view reconstruction pipeline integrating Structure from Motion (SfM) and Multi-View Stereo (MVS) to generate high-fidelity 3D plant point clouds. Stage II develops a dual-branch architecture employing Star modules for multi-scale hierarchical geometric feature extraction at the organ level (filaments and frui balls), complemented by a Context-Anchored Attention (CAA) mechanism to capture long-range contextual information. This synergistic feature learning approach addresses morphological variations, achieving 86.83% segmentation accuracy (surpassing PointNet++ by 7.37%) and outperforming conventional point cloud models. Stage III proposes an optimized geometric analysis pipeline combining dual-centroid spatial vectorization with Oriented Bounding Box (OBB)-based proximity analysis, resolving picking coordinate localization across diverse plants with 90% positioning accuracy and 68.82% mean IoU (13.71% improvement). The experiments demonstrate that PointSafNet systematically integrates 3D reconstruction, hierarchical feature learning, and geometric reasoning to provide visual guidance for robotic harvesting systems in complex plant canopies. The framework’s dual emphasis on architectural innovation and geometric modeling offers a generalizable solution for precision agriculture tasks involving morphologically diverse safflowers.

Why it matches plant phenotyping methods3D画像再構成・点群解析・器官セグメンテーションを統合し、植物器官の位置を推定する方法が研究の中心である。

abstractthis study proposes PointSafNet—a novel three-stage 3D point cloud analysis framework

Monitoring the structure of restored forests and assessing aboveground carbon density through canopy metrics derived from digital aerial photogrammetry and LiDAR ↗

Monitoring forest dynamics is crucial to understanding forest succession drivers and ensuring successful restoration outcomes. Laser scanners onboard robust drone systems are valuable for penetrating forest canopies and providing structural details. With the growing accessibility to low‐cost drones equipped with advanced optical sensors, photogrammetry emerges as a potential and cost‐effective alternative for monitoring forest restoration. Our goal was to explore the potential of monitoring tropical forest restoration sites using low‐cost drones as an alternative to airborne laser scanning. Using linear regression, we compared five canopy‐derived metrics as well as aboveground carbon density (AGCD) models fitted from mean canopy height. Data were collected over 30 plots of 900 m2 sampled across restoration plantations of different ages. Results showed a strong relationship between both laser scans and low‐cost optical sensors for canopy metrics, with r2 = 0.83–0.99, root mean squared error (RMSE) % = 5.41–27.25%, and mean absolute error (MAE) % = 3.81–16.41%. Central tendencies (e.g. mean height) were more reliably estimated, while metrics related to canopy height variation tended to be overestimated by optical sensors. The AGCD models showed little difference, with high r2 values (0.87 and 0.86) and very similar estimated RMSE% (30.02 and 31.33%) and MAE% (25.15 and 25.37%), for both laser scanners and optical sensors, respectively. Our findings demonstrate that digital aerial photogrammetry can produce results comparable to laser scanning in assessing the canopy structure of restored forests, serving as a cost‐efficient alternative for restoration monitoring, particularly in regions with financial or logistical constraints for laser scanner surveys. However, optical data have limitations in capturing reliable terrain information in densely forested areas.

Why it matches plant phenotyping methods低コストドローンのデジタル航空写真測量をLiDARと比較検証し、森林キャノピー構造や地上部炭素密度という植物群落形質を推定する手法が研究の中心であるため。

abstractOur findings demonstrate that digital aerial photogrammetry can produce results comparable to laser scanning in assessing the canopy structure of restored forests

A methodology for the realistic assessment of 3D point clouds of fruit trees in full 3D context ↗

The aim of this paper is to address the lack of standard methodologies for the assessment of 3D point clouds. We present a methodology to realistically assess the accuracy of 3D point clouds, enabling the evaluation in a full 3D context rather than based on isolated points. Additionally, it introduces three significant innovations: a) it bridges the gap related to the unknown error of the reference ground-truth point cloud; b) it provides separate metrics for location error and reconstruction error; and c) it introduces a procedure to compute the location error that eliminates the bias in the selection of point-pair picking between the DGT points and their corresponding pairs in the point cloud being assessed. The geometry and structure of trees are related to the vegetative parameters and productivity in fruit orchards. In consequence, obtaining a precise and accurate geometric characterization of canopies is of interest for implementing site-specific management strategies that optimize input rates and minimize the costs and environmental risks of agricultural operations. Among the different sensing technologies, sensors based on the principle of light detection and ranging (LiDAR) have emerged as the primary choice for accurate geometric characterization of orchards. However, to make informed orchard management decisions based on LiDAR-derived geometric and structural data, it is essential to assess the accuracy of LiDAR-based scanning systems. Unfortunately, there is currently a lack of standard methodologies to evaluate the accuracy of LiDAR-based systems in agricultural environments. This research paper presents a novel methodology to assess the location error and the reconstruction error of 3D point clouds in full 3D context. The methodology involves comparing LiDAR-derived point clouds to an accurate high-resolution 3D digital ground truth (DGT) obtained using digital photogrammetric techniques. One of the main difficulties when using a reference point cloud to assess point cloud errors is the selection of the points to be compared so that they can be considered as corresponding point pairs. When developing the methodology, four procedures of point pair selection and distance calculation were compared. The best performing procedure was selected and proposed as a standard for accuracy assessment of 3D point clouds. The proposed procedure minimizes the error attributed to the selection of the corresponding point pairs between the assessed point cloud and the reference DGT point cloud. Subsequently, the proposed methodology was tested and validated by assessing the accuracy of 46 different point clouds. The conclusions regarding the accuracy, applicability, and practical utility of the proposed methodology are supported by the determination of reconstruction errors and location errors in 46 point clouds obtained with the 3 different MTLS systems operated with different settings. The proposed methodology will be very useful for scanning system manufacturers, researchers, advisors and eventually advanced farmers to quantify the errors committed when characterizing tree canopies. This is crucial to enable accurate management operations in the framework of Precision Agriculture based on canopy variability. Furthermore, the methodology is expected to facilitate the design of new applications requiring high accuracy to be implemented in the near future.

Why it matches plant phenotyping methods果樹キャノピーの3D形状・構造を測定するLiDAR点群について、誤差評価手法を開発し、46点群で検証しており、植物表現型取得の技術的評価が中心である。

abstractThis research paper presents a novel methodology to assess the location error and the reconstruction error of 3D point clouds in full 3D context.

Comparative Analysis of Novel View Synthesis and Photogrammetry for 3D Forest Stand Reconstruction and Extraction of Individual Tree Parameters ↗

The accurate and efficient 3D reconstruction of trees is beneficial for urban forest resource assessment and management. Close-range photogrammetry (CRP) is widely used in the 3D model reconstruction of forest scenes. However, in practical forestry applications, challenges such as low reconstruction efficiency and poor reconstruction quality persist. Recently, novel view synthesis (NVS) technology, such as neural radiance fields (NeRF) and 3D Gaussian splatting (3DGS), has shown great potential in the 3D reconstruction of plants using some limited number of images. However, existing research typically focuses on small plants in orchards or individual trees. It remains uncertain whether this technology can be effectively applied in larger, more complex stands or forest scenes. In this study, we collected sequential images of urban forest plots with varying levels of complexity using imaging devices with different resolutions (cameras on smartphones and UAV). These plots included one with sparse, leafless trees and another with dense foliage and more occlusions. We then performed dense reconstruction of forest stands using NeRF and 3DGS methods. The resulting point cloud models were compared with those obtained through photogrammetric reconstruction and laser scanning methods. The results show that compared to photogrammetric method, NVS methods have a significant advantage in reconstruction efficiency. The photogrammetric method is suitable for relatively simple forest stands, as it is less adaptable to complex ones. This results in tree point cloud models with issues such as excessive canopy noise and wrongfully reconstructed trees with duplicated trunks and canopies. In contrast, NeRF is better adapted to more complex forest stands, yielding tree point clouds of the highest quality that offer more detailed trunk and canopy information. However, it can lead to reconstruction errors in the ground area when the input views are limited. The 3DGS method has a relatively poor capability to generate dense point clouds, resulting in models with low point density, particularly with sparse points in the trunk areas, which affects the accuracy of the diameter at breast height (DBH) estimation. Tree height and crown diameter information can be extracted from the point clouds reconstructed by all three methods, with NeRF achieving the highest accuracy in tree height. However, the accuracy of DBH extracted from photogrammetric point clouds is still higher than that from NeRF point clouds. Meanwhile, compared to ground-level smartphone images, tree parameters extracted from reconstruction results of higher-resolution and varied perspectives of drone images are more accurate. These findings confirm that NVS methods have significant application potential for 3D reconstruction of urban forests.

Why it matches plant phenotyping methodsNVS、写真測量、レーザースキャンを比較して森林の3D再構成と樹高・樹冠径・DBHの抽出精度を評価しており、植物形質の取得手法が研究の中心である。

abstractWe then performed dense reconstruction of forest stands using NeRF and 3DGS methods. The resulting point cloud models were compared with those obtained through photogrammetric reconstruction and laser scanning methods.

A Low-Cost Photogrammetry System for 3D Plant Modeling and Phenotyping ↗

We present an open-source, low-cost photogrammetry system for 3D plant modeling and phenotyping. The system uses a structure-from-motion approach to reconstruct 3D representations of the plants via point clouds. Using wheat as an example, we demonstrate how various phenotypic traits can be computed easily from the point clouds. These include standard measurements such as plant height and radius, as well as features that would be more cumbersome to measure by hand, such as leaf angles and convex hull. We further demonstrate the utility of the system through the investigation of specific metrics that may yield objective classifications of erectophile versus planophile wheat canopy architectures.

Why it matches plant phenotyping methods低コストの3Dフォトグラメトリによる植物形状再構成と、点群からの表現型形質推定を中心に開発・実証しているため。

abstractWe present an open-source, low-cost photogrammetry system for 3D plant modeling and phenotyping.

Plant Height and Soil Compaction in Coffee Crops Based on LiDAR and RGB Sensors Carried by Remotely Piloted Aircraft ↗

Remotely Piloted Aircraft (RPA) as sensor-carrying airborne platforms for indirect measurement of plant physical parameters has been discussed in the scientific community. The utilization of RGB sensors with photogrammetric data processing based on Structure-from-Motion (SfM) and Light Detection and Ranging (LiDAR) sensors for point cloud construction are applicable in this context and can yield high-quality results. In this sense, this study aimed to compare coffee plant height data obtained from RGB/SfM and LiDAR point clouds and to estimate soil compaction through penetration resistance in a coffee plantation located in Minas Gerais, Brazil. A Matrice 300 RTK RPA equipped with a Zenmuse L1 sensor was used, with RGB data processed in PIX4D software (version 4.5.6) and LiDAR data in DJI Terra software (version V4.4.6). Canopy Height Model (CHM) analysis and cross-sectional profile, together with correlation and statistical difference studies between the height data from the two sensors, were conducted to evaluate the RGB sensor’s capability to estimate coffee plant height compared to LiDAR data considered as reference. Based on the height data obtained by the two sensors, soil compaction in the coffee plantation was estimated through soil penetration resistance. The results demonstrated that both sensors provided dense point clouds from which plant height (R2 = 0.72, R = 0.85, and RMSE = 0.44) and soil penetration resistance (R2 = 0.87, R = 0.8346, and RMSE = 0.14 m) were accurately estimated, with no statistically significant differences determined between the analyzed sensor data. It is concluded, therefore, that the use of remote sensing technologies can be employed for accurate estimation of coffee plantation heights and soil compaction, emphasizing a potential pathway for reducing laborious manual field measurements.

Why it matches plant phenotyping methodsRGB/SfMとLiDARによるコーヒー樹高推定を比較・検証することが研究の中心であり、植物形質取得手法の技術評価に該当する。土壌硬度推定も扱うが、樹高計測法の検証が明確である。

abstractthis study aimed to compare coffee plant height data obtained from RGB/SfM and LiDAR point clouds

Studies in the stereometry of large growing trees by terrestrial photogrammetric method ↗

The study has developed a geometrical toolset to calculate biometric parameters of large growing trees using distorted images captured by a digital camera. This toolset focuses on determining a variable scaling factor that accounts for the camera’s inclined position relative to the horizon. An algorithm for the software and an associated online web service has been created. For measurements, a standard ruler, such as one that is 1,000 mm long, is placed on the tree trunk at a height of 1.3 m from the base. The distance from the lens to the standard is measured with an accuracy of ±0.2%. The trunk thickness (DBH) measurement accuracy is not below 1 mm per 1 pixel. This analysis derives from the similarity of triangles in the camera’s field of view, specifically from the lens to the tree trunk and from the lens to the image sensor. The parameters of the digital image are essential, particularly the lens’s focal length varying from 20 mm to 200 mm. Similar but more complex geometric proportions are applied, in case the trunk is vertical. The process involves considering the tilt of the camera matrix to the horizon, and the slope from the lens to the standard reference point. Key factors include the predetermined distance and slope from the lens to the standard on the trunk, along with the parameters of the digital image, particularly the lens’s focal length, typically ranging from 6 to 10 mm. An online web service is offered to perform the relevant measurements and calculations. The software facilitates automatic calculations and generates a data array containing scaling factors corresponding to various height levels from the tree base. Simultaneously, a Visual Basic command array is produced to mark the digital image of the tree at these height levels, complete with scaling factor indicators. This method enables the measurement of trunk thickness at the required heights in pixels, which can then be converted into millimeters. The measurement accuracy is from 6 to 10 millimeters per pixel. The collected data is subsequently organized into a table in Excel. Then, the cross-sectional areas of all trunk segments and their respective volumes were calculated. The total trunk volume is determined by summing the volumes of these segments. The proposed methodology is original, has no prototypes, and may be suitable for practical application.

Why it matches plant phenotyping methods樹木画像から幹径・幹体積を推定する写真測量ツール、アルゴリズム、オンラインサービスを開発しており、植物形質取得法が研究の中心である。

abstractThe study has developed a geometrical toolset to calculate biometric parameters of large growing trees using distorted images captured by a digital camera.

Assessment of grapevine bunch withering: advances in fruit 3D morphology and colour evaluation ↗

The agro-industrial sector is experiencing a new wave of innovation driven by goods-inspecting devices designed to optimise operations, improve product quality, and reduce yield losses. Grape withering is widely used to concentrate berry juice for raisin and sweet wine production. While this process alters wine characteristics, it also introduces costs and risks, as pathogen infections can compromise the quality of the final product. This study presents a reliable three-dimensional analysis for assessing grape colour and bunch morphology to evaluate infection risk and drying performance. Twenty Vitis vinifera bunches were dried under environmental conditions. Colour analysis focused on the distribution of colours in healthy versus rotten bunches. Three-dimensional digital replicas were generated with two methods: i) photogrammetry, and ii) a recently developed artificial intelligence model. The point clouds and meshes output from the two approaches were compared, and morphometric traits were directly measured, including volume, surface area, and both horizontal and vertical sections for each bunch. Key geometrical descriptors of the bunch's horizontal sections and individual berries were found to be relevant for classifying the risk of bunch rot. Additionally, morphometric traits related to bunch compactness were linked to drying speed. A linear model incorporating three-dimensional descriptors was developed to estimate weight loss during withering, achieving an R 2 value of 0.98 and a relative error of 0.07. The artificial intelligence-based technique produced lower-quality models for grape reconstruction, but the selected morphometric traits remained effective. • Grapevine bunches morphology evaluation through three-dimensional reconstruction. • Morphometric traits classified bunch dehydration speed and rot infection risk. • Colour analysis described withering progress and rotting spreading. • A fast artificial intelligence-based 3D acquisition technique was tested.

Why it matches plant phenotyping methodsブドウ房の3D画像再構成と色解析を用いて形態形質・感染リスク・乾燥性能を定量化し、 photogrammetryとAI手法を比較評価しているため、植物フェノタイピング手法が中心である。

abstractThis study presents a reliable three-dimensional analysis for assessing grape colour and bunch morphology to evaluate infection risk and drying performance.

Comparative Analysis of Novel View Synthesis and Photogrammetry for 3D Forest Stand Reconstruction and Extraction of Individual Tree Parameters ↗

Accurate and efficient 3D reconstruction of trees is beneficial for urban forest resource assessment and management. Close-Range Photogrammetry (CRP) is widely used in 3D model reconstruction of forest scenes. However, in practical forestry applications, challenges such as low reconstruction efficiency and poor reconstruction quality persist. Recently, Novel View Synthesis (NVS) technology such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) has shown great potential in the 3D reconstruction of plants using some limited number of images. However, existing research typically focuses on small plants in orchards or individual trees. It remains uncertain whether this technology can be effectively applied in larger, more complex stands or forest scenes. In this study, we collected sequential images of urban forest plots with varying levels of complexity using different imaging devices. We then performed dense reconstruction of forest stand using NeRF and 3DGS methods. The resulting point cloud models were compared with those obtained through photogrammetric reconstruction and laser scanning methods. The results show that compared to photogrammetric method, NVS methods have a significant advantage in reconstruction efficiency. Photogrammetric method is less suited to more complex forest stands, resulting in tree point cloud models with issues such as excessive canopy noise, wrongfully reconstructed trees with duplicated trunks and canopies. In contrast, NeRF is better adapted to more complex forest stands, especially in reconstructing canopy regions. However, it can lead to reconstruction errors in the ground area when the input views are limited. The 3DGS method has a relatively poor capability to generate dense point clouds, resulting in models with low point density, particularly with sparse points in the trunk areas, which affects the accuracy of the diameter at breast height (DBH) estimation. Tree height and crown diameter information can be extracted from the point clouds reconstructed by all three methods, with NeRF achieving the highest accuracy in tree height. However, the accuracy of DBH extracted from photogrammetric point clouds is still higher than that from NeRF point clouds. Meanwhile, compared to ground-level smartphone images, tree parameters extracted from reconstruction results of higher-resolution and varied perspectives of drone images are more accurate. These findings suggest that NVS methods have significant potential for 3D reconstruction of urban forests, providing further technical support for forest resource visualization, inventory and management tasks.

Why it matches plant phenotyping methods森林の3D再構成手法を比較・検証し、樹高、樹冠径、胸高直径という個体レベルの植物形質を点群から抽出して精度評価しているため、方法中心の植物フェノタイピング研究である。

abstractThe resulting point cloud models were compared with those obtained through photogrammetric reconstruction and laser scanning methods.

Evaluating Sensor Fusion and Flight Parameters for Enhanced Plant Height Measurement in Dry Peas ↗

Plant height is an important trait for evaluating plant lodging, drought, and stress. Standard measurement techniques are expensive, laborious, and error-prone. Although UAS-based sensors and digital aerial photogrammetry have been tested on plants with an erect growth habit, further study is needed in the application of these technologies to prostrate crops such as dry peas. This study has compared the performance of LiDAR, RGB, and multispectral sensors across different flight configurations (altitudes, speeds), and image overlaps over dry pea plots to identify the optimal setup for accurate plant height estimation. Data were assessed to determine the effect of sensor fusion on plant height accuracy using LiDAR’s digital terrain model (DTM) as the base layer, and digital surface models (DSMs) generated from RGB and multispectral sensors. All sensors, particularly RGB, tended to underestimate plant height at higher flight altitudes. However, RMSE and MAE values showed no significant difference, indicating that higher flight altitudes can reduce data collection time and cost without sacrificing accuracy. Multispectral and LiDAR sensors were more sensitive to changes in flight speed than RGB sensors; However, RMSE and MAE values did not vary significantly across the tested speeds. Increased image overlap resulted in improved accuracy across all sensors. The Wilcoxon–Mann–Whitney test showed no significant difference between sensor fusion and individual sensors. Although LiDAR provided the highest accuracy of dry peas height estimation, it was not consistent across all canopy structures. Therefore, future research should focus on the integrating machine learning models with LiDAR to improve plant height estimation in dry peas.

Why it matches plant phenotyping methods乾燥エンドウの草丈推定を対象に、LiDAR・RGB・マルチスペクトルセンサーの融合、飛行条件、画像重複率を比較・検証しており、植物表現型取得手法が研究の中心である。

abstractThis study has compared the performance of LiDAR, RGB, and multispectral sensors across different flight configurations (altitudes, speeds), and image overlaps over dry pea plots to identify the optimal setup for accurate plant height estimation.

Enhancing stem volume estimation for savanna species using variable-exponent taper equation and close-range photogrammetry ↗

Stem volume estimation is crucial in forest ecology and management, particularly for timber harvesting strategies and carbon stock assessments. This study aimed to develop a variable-exponent taper equation specifically tailored to savanna tree species using close-range photogrammetry (CRP) data and to evaluate its performance against conventional volume equations for stem volume estimation. A dataset of 30 trees across five dominant savanna species was used to fit the taper model, which was validated using a separate dataset of 322 trees from 14 species. The results demonstrated significant improvements in volume estimation accuracy when using the taper equation. At the tree level, the root mean square error (RMSE) decreased by 47%, from 598 to 319 ​dm 3 , and the mean absolute bias (MAB) by 48%, from 328 to 172 ​dm 3 , compared to volume equations. Similarly, at the plot level, RMSE was reduced by 42% and MAB by 40%. The model performed well for species with regular forms. However, species with irregular tapers exhibited higher errors, reflecting the challenges of modeling stem forms of mixed species. The use of CRP proved valuable, providing high-resolution diameter measurements that improved model parameterization. This study underscores the importance of advanced data collection methods for enhancing taper model accuracy and suggests that further species-specific adjustments are needed to improve performance for species with irregular forms. The findings support the broader application of taper equations for improving stem volume estimates in savanna ecosystems, contributing to better forest management and resource monitoring practices.

Why it matches plant phenotyping methods近距離フォトグラメトリで樹幹径を取得し、樹幹体積推定モデルを開発・独立データで検証しており、植物形態・体積の取得と推定が中心的です。

abstractThis study aimed to develop a variable-exponent taper equation specifically tailored to savanna tree species using close-range photogrammetry (CRP) data and to evaluate its performance against conventional volume equations for stem volume estimation.

Wheat3DGS: In-field 3D Reconstruction, Instance Segmentation and Phenotyping of Wheat Heads with Gaussian Splatting ↗

Automated extraction of plant morphological traits is crucial for supporting crop breeding and agricultural management through high-throughput field phenotyping (HTFP). Solutions based on multi-view RGB images are attractive due to their scalability and affordability, enabling volumetric measurements that 2D approaches cannot directly capture. While advanced methods like Neural Radiance Fields (NeRFs) have shown promise, their application has been limited to counting or extracting traits from only a few plants or organs. Furthermore, accurately measuring complex structures like individual wheat heads-essential for studying crop yields-remains particularly challenging due to occlusions and the dense arrangement of crop canopies in field conditions. The recent development of 3D Gaussian Splatting (3DGS) offers a promising alternative for HTFP due to its high-quality reconstructions and explicit point-based representation. In this paper, we present Wheat3DGS, a novel approach that leverages 3DGS and the Segment Anything Model (SAM) for precise 3D instance segmentation and morphological measurement of hundreds of wheat heads automatically, representing the first application of 3DGS to HTFP. We validate the accuracy of wheat head extraction against high-resolution laser scan data, obtaining per-instance mean absolute percentage errors of 15.1%, 18.3%, and 40.2% for length, width, and volume. We provide additional comparisons to NeRF-based approaches and traditional Muti-View Stereo (MVS), demonstrating superior results. Our approach enables rapid, non-destructive measurements of key yield-related traits at scale, with significant implications for accelerating crop breeding and improving our understanding of wheat development.

Why it matches plant phenotyping methods3D Gaussian SplattingとSAMを用いて小麦穂の3Dセグメンテーションと形態形質抽出手法を開発し、レーザースキャン、NeRF、MVS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。

abstractwe present Wheat3DGS, a novel approach that leverages 3DGS and the Segment Anything Model (SAM) for precise 3D instance segmentation and morphological measurement of hundreds of wheat heads automatically

Comparison of UAV-Based LiDAR and Photogrammetric Point Cloud for Individual Tree Species Classification of Urban Areas ↗

UAV LiDAR and digital aerial photogrammetry (DAP) have shown great performance in forest inventory due to their advantage in three-dimensional information extraction. Many studies have compared their performance in individual tree segmentation and structural parameters extraction (e.g. tree height). However, few studies have compared their performance in tree species classification. Therefore, we have compared the performance of UAV LiDAR and DAP-based point clouds in individual tree species classification with the following steps: (1) Point cloud data processing: Denoising, smoothing, and normalization were conducted on LiDAR and DAP-based point cloud data separately. (2) Feature extraction: Spectral, structural, and texture features were extracted from the pre-processed LiDAR and DAP-based point cloud data. (3) Individual tree segmentation: The marked watershed algorithm was used to segment individual trees on canopy height models (CHM) derived from LiDAR and DAP data, respectively. (4) Pixel-based tree species classification: The random forest classifier (RF) was used to classify urban tree species with features derived from LiDAR and DAP data separately. (5) Individual tree species classification: Based on the segmented individual tree boundaries and pixel-based classification results, the majority filtering method was implemented to obtain the final individual tree species classification results. (6) Fused with hyperspectral data: LiDAR-hyperspectral and DAP-hyperspectral fused data were used to conduct individual tree species classification. (7) Accuracy assessment and comparison: The accuracy of the above results were assessed and compared. The results indicate that LiDAR outperformed DAP in individual tree segmentation (F-score 0.83 vs. 0.79), while DAP achieved higher pixel-level classification accuracy (73.83% vs. 57.32%) due to spectral-textural features. Fusion with hyperspectral data narrowed the gap, with LiDAR reaching 95.98% accuracy in individual tree classification. Our findings suggest that DAP offers a cost-effective alternative for urban forest management, balancing accuracy and operational costs.

Why it matches plant phenotyping methodsUAV LiDAR・写真測量点群を用いた個体樹木の分割、構造特徴抽出、樹種分類を比較・検証しており、植物個体の状態を推定する取得・解析ワークフローが中心である。

abstractTherefore, we have compared the performance of UAV LiDAR and DAP-based point clouds in individual tree species classification with the following steps

Multi-scale remote sensing for sustainable citrus farming: Predicting canopy nitrogen content using UAV-satellite data fusion ↗

• Canopy Nitrogen Content (CNC) was estimated using a combination of UAV and Sentinel-2 data. • Developed models that utilize vegetation indices and tree structural data for CNC estimation. • Created spatial and temporal heat maps to visualize nitrogen content distribution in citrus trees. • Demonstrated a strong correlation between CNC and yield across three growing seasons. • Highlighted the potential for CNC applications to guide the development of site-specific nitrogen management strategies. Accurate monitoring of nitrogen (N) levels, while accounting for spatiotemporal variability is crucial for optimizing fertilization in citrus orchards. Traditional methods, such as frequent leaf and soil sampling followed by laboratory analysis, are costly, labor-intensive, and prone to human error. Remote sensing (RS) technologies, including unmanned aerial vehicles (UAVs) and satellite platforms, offer scalable and precise alternatives for N management. However, integrating these platforms poses challenges due to significant differences in spatial, temporal, and spectral resolution. This study presents a novel approach incorporating multispectral and temporal data from UAVs and Sentinel-2 satellites to estimate canopy N content (CNC) in citrus orchards. This method captures spatiotemporal variability across multiple citrus cultivars, aiming to enhance nitrogen use efficiency (NUE) while reducing environmental impact, ultimately promoting sustainable orchard management practices. The study was conducted in commercial citrus plots in the Hefer Valley, Israel, and spanned two phases. The first phase (May 2019 to April 2022) focused on four plots of the 'Newhall' cultivar, while the second phase expanded to twelve additional plots featuring five different citrus cultivars. The methodology consisted of six key steps: (1) Leaf samples from the study area were collected for laboratory nitrogen (N) analysis. (2) Acquiring and preprocessing bimonthly UAV multispectral images and Sentinel-2 satellite images to ensure data quality and consistency. (3) Segmenting individual trees using UAV imagery and extracting structural features through Structure-from-Motion (SfM) photogrammetry. (4) Processing images and extracting spectral and structural features relevant to N estimation. (5) Developing Random Forest (RF) models to estimate CNC using UAV-derived vegetation indices (VIs) and SfM data and combining these with Sentinel-2 VIs to generate canopy-scale CNC heatmaps. (6) Analyzing the relationship between CNC and yield to understand nitrogen dynamics and their impact on productivity. The integrated RF model, which combined UAV-VIs, Sentinel-2 VIs, and SfM-derived structural data, achieved superior performance (R² = 0.80, RMSE = 0.17 kg/m²) compared to models relying solely on UAV-VIs (R² = 0.68, RMSE = 0.23 kg/m²) or Sentinel-2 VIs (R² = 0.48, RMSE = 0.30 kg/m²). Additionally, CNC expressed as mass per tree demonstrated a strong positive correlation with yield (R² = 0.66), highlighting the relationship between nitrogen dynamics and orchard productivity. These results underscore the robustness of the integrated model and the clear advantage of multi-platform data fusion over single-source approaches. The study provides compelling evidence for the potential of combining UAV and Sentinel-2 data to improve CNC estimation and its correlation with yield in citrus orchards. The findings contribute to advancements in precision agriculture by offering a scalable, data-driven framework to enhance nutrient management and support sustainable orchard practices.

Why it matches plant phenotyping methodsUAV・衛星画像、SfM、特徴抽出、RFモデルを統合し、柑橘樹冠窒素含量という植物形質を推定する方法を開発・評価しており、表現型取得が研究の中心です。

abstractThis study presents a novel approach incorporating multispectral and temporal data from UAVs and Sentinel-2 satellites to estimate canopy N content (CNC) in citrus orchards.

Using Unoccupied Aerial Systems (UAS) and Structure-from-Motion (SfM) to Measure Forest Canopy Cover and Individual Tree Height Metrics in Northern California Forests ↗

Quantifying forest structure to assess changing wildfire risk factors is critical as vulnerable areas require mitigation, management, and resource allocation strategies. Remote sensing offers the opportunity to accurately measure forest attributes without time-intensive field inventory campaigns. Here, we quantified forest canopy cover and individual tree metrics across 44 plots (20 m × 20 m) in oak woodlands and mixed-conifer forests in Northern California using structure-from-motion (SfM) 3D point clouds derived from unoccupied aerial systems (UAS) multispectral imagery. In addition, we compared UAS–SfM estimates with those derived using similar methods applied to Airborne Laser Scanning (ALS) 3D point clouds as well as traditional ground-based measurements. Canopy cover estimates were similar across remote sensing (ALS, UAS-SfM) and ground-based approaches (r2 = 0.79, RMSE = 16.49%). Compared to ground-based approaches, UAS-SfM point clouds allowed for correct detection of 68% of trees and estimated tree heights were significantly correlated (r2 = 0.69, RMSE = 5.1 m). UAS-SfM was not able to estimate canopy base height due to its inability to penetrate dense canopies in these forests. Since canopy cover and individual tree heights were accurately estimated at the plot-scale in this unique bioregion with diverse topography and complex species composition, we recommend UAS-SfM as a viable approach and affordable solution to estimate these critical forest parameters for predictive wildfire modeling.

Why it matches plant phenotyping methodsUAS-SfMによる樹冠被覆率と個体樹高という植物形質の取得手法を開発・比較検証しており、方法が研究の中心である。

abstractwe quantified forest canopy cover and individual tree metrics across 44 plots (20 m × 20 m) in oak woodlands and mixed-conifer forests in Northern California using structure-from-motion (SfM) 3D point clouds derived from unoccupied aerial systems (UAS) multispectral imagery.

Remote Sensing of Forest Gap Dynamics in the Białowieża Forest: Comparison of Multitemporal Airborne Laser Scanning and High-Resolution Aerial Imagery Point Clouds ↗

Remote sensing technologies like airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) have emerged as efficient tools for detecting and analysing canopy gaps (CGs). Comparing these technologies is essential to determine their functionality and applicability in various environments. Thus, this study aimed to assess CG dynamics in the temperate European Białowieża Forest between 2015 and 2022 by comparing ALS data and image-derived point clouds (IPC) from DAP, to evaluate their respective capabilities in describing and analysing forest CG dynamics. Our results demonstrated that ALS-based point clouds provided more detailed and precise spatial information about both the vertical and horizontal structure of forest CGs compared to IPC. ALS detected 27,754 (54%) new CGs between 2015 and 2022, while IPC identified 23,502 (75%) new CGs. Both the average gap area and the total gap area significantly increased over time in both methods. ALS data not only identified a greater number of CGs, particularly smaller ones (below 500 m2), but also produced a more precise representation of CG shape and structure. In conclusion, precise, multi-temporal remote sensing data on the distribution and size of canopy gaps enable effective monitoring of structural changes and disturbances in forest stands, which in turn supports more efficient forest management, e.g., planning of forest regeneration.

Why it matches plant phenotyping methods森林キャノピーギャップの空間・構造特性を対象に、ALSと航空画像由来点群を比較評価しており、植物状態の取得・解析手法が研究の中心である。

titleRemote Sensing of Forest Gap Dynamics in the Białowieża Forest: Comparison of Multitemporal Airborne Laser Scanning and High-Resolution Aerial Imagery Point Clouds

Combined Use of Spectral and Structural Features for Improved Early Detection of Pine Shoot Beetle Attacks in Yunnan Pines ↗

The long-lasting outbreak of the pine shoot beetle (PSB, Tomicus spp.) threatens forest ecological security. Effective monitoring is urgently needed for the Integrated Pest Management (IPM) of this pest. UAV-based hyperspectral remote sensing (HRS) offers opportunities for the early and accurate detection of PSB attacks. However, the insufficient exploration of spectral and structural information from early-attacked crowns and the lack of suitable detection models limit UAV applications. This study developed a UAV-based framework for detecting early-stage PSB attacks by integrating hyperspectral images (HSIs), LiDAR point clouds, and structure from motion (SfM) photogrammetry data. Individual tree segmentation algorithms were utilized to extract both spectral and structural variables of damaged tree crowns. Random forest (RF) was employed to determine the optimal detection model as well as to clarify the contributions of the candidate variables. The results are as follows: (1) Point cloud segmentation using the Canopy Height Model (CHM) yielded the highest crown segmentation accuracy (F-score: 87.80%). (2) Near-infrared reflectance exhibited the greatest decrease for early-attacked crowns, while the structural variable intensity percentile (int_P50-int_P95) showed significant differences (p < 0.05). (3) In the RF model, spectral variables were predominant, with LiDAR structural variables serving as a supplement. The anthocyanin reflectance index and int_kurtosis were identified as the best indicators for early detection. (4) Combining HSI with LiDAR data obtained the best RF model accuracy (classification accuracy: 87.31%; Kappa: 0.8275; SDR estimation accuracy: R2 = 0.8485; RMSEcv = 3.728%). RF integrating HSI and SfM data exhibited similar performance. In conclusion, this study identified optimal spectral and structural variables for UAV monitoring and improved HRS model accuracy and thereby provided technical support for the IPM of PSB outbreaks.

Why it matches plant phenotyping methodsUAV画像・LiDAR・SfMから個体樹冠のスペクトルおよび構造特徴を抽出し、マツノキクイムシ被害状態を検出・推定する方法を開発、検証しており、植物状態の取得手法が中心です。

abstractThis study developed a UAV-based framework for detecting early-stage PSB attacks by integrating hyperspectral images (HSIs), LiDAR point clouds, and structure from motion (SfM) photogrammetry data.

Estimating biomass in grasslands through traditional methods and the use of drones in the State of Chihuahua, Mexico ↗

Objective: To evaluate three biomass estimation methods (Unmanned Aerial Vehicle (UAV or drone), ceptometer, and canopy height), comparing them to the quadrant method in an arborescent tufted grassland in the state of Chihuahua.Methodology: The study was conducted in Teseachi, Namiquipa, in october 2020. We located thirty random points. The first biomass estimation method used was UAV. Once the drone f lights were completed, the quadrant was placed and the coordinates were determined. We carried out nine readings using a ceptometer and obtained an average. Subsequently, we measured the average canopy height. Finally, all forage within the quadrant was cut at ground level and packed for laboratory analysis. The Agisoft Metashape software was used to process the SfM of the aerial images, using nine sampling points, applying the NGBDI vegetation index, and calculating the average pixels of a 33 m moving window. A simple linear regression model was used to analyze the data with the R Project software, version 4.0.3.Results: The simple linear regression model showed an R2 of 0.62 (p0.01), 0.55 (p0.001), and 0.48 (p0.001), for UAV, ceptometer, and canopy height, respectively.Study Limitations: There were no limitations for this report.Conclusions: Data obtained with UAVs can generate predictive biomass maps with acceptable accuracy levels. The ceptometer leaf area index is a reliable method to estimate forage yield. However, using the canopy height method is not advisable to estimate forage yield, since its correlation is weak

Why it matches plant phenotyping methodsUAV画像、セプトメーター、草丈による植物群落のバイオマス推定法を比較・検証し、SfM画像処理と植生指数を用いて予測精度を評価しているため、植物形質取得法が中心的である。

abstractTo evaluate three biomass estimation methods (Unmanned Aerial Vehicle (UAV or drone), ceptometer, and canopy height), comparing them to the quadrant method

Optimizing root phenotyping: Assessing the impact of camera calibration on 3D root reconstruction ↗

Abstract Accurate 3D reconstruction is essential for high-throughput plant phenotyping, particularly for studying complex structures such as root systems. While photogrammetry and Structure from Motion (SfM) techniques have become widely used for 3D root imaging, the camera settings used are often underreported in studies, and the impact of camera calibration on model accuracy remains largely underexplored in plant science. In this study, we systematically evaluate the effects of focus, aperture, exposure time, and gain settings on the quality of 3D root models made with a multi-camera scanning system. We show through a series of experiments that calibration significantly improves model quality, with focus misalignment and shallow depth of field (DoF) being the most important factors affecting reconstruction accuracy. Our results further show that proper calibration has a greater effect on reducing noise than filtering it during post-processing, emphasizing the importance of optimizing image acquisition rather than relying solely on computational corrections. This work improves the repeatability and accuracy of 3D root phenotyping by giving useful calibration guidelines. This leads to better trait quantification for use in crop research and plant breeding.

Why it matches plant phenotyping methods3D根フェノタイピングにおけるカメラ校正と画像取得条件の影響を体系的に検証し、再現性・精度向上の指針を提示する方法研究である。

abstractThis work improves the repeatability and accuracy of 3D root phenotyping by giving useful calibration guidelines.

A 3D reconstruction platform for complex plants using OB-NeRF ↗

Introduction: Applying 3D reconstruction techniques to individual plants has enhanced high-throughput phenotyping and provided accurate data support for developing "digital twins" in the agricultural domain. High costs, slow processing times, intricate workflows, and limited automation often constrain the application of existing 3D reconstruction platforms. Methods: We develop a 3D reconstruction platform for complex plants to overcome these issues. Initially, a video acquisition system is built based on "camera to plant" mode. Then, we extract the keyframes in the videos. After that, Zhang Zhengyou's calibration method and Structure from Motion(SfM)are utilized to estimate the camera parameters. Next, Camera poses estimated from SfM were automatically calibrated using camera imaging trajectories as prior knowledge. Finally, Object-Based NeRF we proposed is utilized for the fine-scale reconstruction of plants. The OB-NeRF algorithm introduced a new ray sampling strategy that improved the efficiency and quality of target plant reconstruction without segmenting the background of images. Furthermore, the precision of the reconstruction was enhanced by optimizing camera poses. An exposure adjustment phase was integrated to improve the algorithm's robustness in uneven lighting conditions. The training process was significantly accelerated through the use of shallow MLP and multi-resolution hash encoding. Lastly, the camera imaging trajectories contributed to the automatic localization of target plants within the scene, enabling the automated extraction of Mesh. Results and discussion: Our pipeline reconstructed high-quality neural radiance fields of the target plant from captured videos in just 250 seconds, enabling the synthesis of novel viewpoint images and the extraction of Mesh. OB-NeRF surpasses NeRF in PSNR evaluation and reduces the reconstruction time from over 10 hours to just 30 Seconds. Compared to Instant-NGP, NeRFacto, and NeuS, OB-NeRF achieves higher reconstruction quality in a shorter reconstruction time. Moreover, Our reconstructed 3D model demonstrated superior texture and geometric fidelity compared to those generated by COLMAP and Kinect-based reconstruction methods. The $R^2$ was 0.9933,0.9881 and 0.9883 for plant height, leaf length, and leaf width, respectively. The MAE was 2.0947, 0.1898, and 0.1199 cm. The 3D reconstruction platform introduced in this study provides a robust foundation for high-throughput phenotyping and the creation of agricultural "digital twins".

Why it matches plant phenotyping methods植物の3D再構成と、そこからの草丈・葉長・葉幅抽出を中心に開発・比較検証した高スループット表現型解析プラットフォームであり、方法が明確に中心的です。

abstractWe develop a 3D reconstruction platform for complex plants to overcome these issues.

Monitoring Canopy Height in the Hainan Tropical Rainforest Using Machine Learning and Multi-Modal Data Fusion ↗