Background: Tropism, an adaptive growth mechanism often completely overlooked in tree phenotyping studies, is a crucial aspect of tree growth that allows them to reconfigure geometrically in relation to their immediate environment. This study introduces an integrated method to quantify tropic behaviour in plant phenotyping studies. Methods: The methodology combines cost-effective three-dimensional (3D) photogrammetric data capture from video, stem delineation techniques and 3D mathematical modelling of posture control for model-assisted identification of tropism traits. The proposed method was tested on a Pinus radiata D.Don. seedling subjected to a gravitational stimulus for 75 days. Stem posture was repeatedly measured using both 3D photogrammetry and fixed photography to create multitemporal 3D datasets and two-dimensional (2D) reference curves. Results: Individual 3D stem curves reconstructed with the proposed methodology introduced an error on spatial coordinates with a normalised RMSD ranging from 1.6 to 4.3% depending on time of capture, when compared with the 2D reference. The error for local tilt angle was higher than the error on spatial coordinates, with RMSD ranging between 5.6–12.2°, as expected for a first-order derivative. The gravitropic coefficient, capturing the sensing of and the reaction to local inclination by the plant, was underestimated by 2% if compared to the reference methodology. No contribution of autotropism (tendency to remain straight) was identified using the new methodology, but that contribution was found to be small using the 2D-approach and likely a key aspect of the gravitropic signature in the studied species. The major challenge with the proposed point cloud-based methodology arose from automated stem delineation. With dedicated algorithm enhancements to address stem occlusion in juvenile conifers and with more regular captures during plant motion, the proposed method could, however, perform identically to the 2D reference methodology. Overall, recovery of tropism traits performed equivalently whether using 2D or 3D data to fit the model of posture control. The minor discrepancies with experimental behaviour originated from fitting a simple kinematic model to complex real-world behaviour rather than data capture and digitising procedures. Conclusions: Overall, the proposed methodology, in its current form, offers a viable alternative to traditional 2D imagery methods at the cost of a small reduction in accuracy and capture time. The advantage of the 3D methodology is that it has the potential to track motion in multiple planes, whilst also measuring plant structure. With refinement, this methodology could be streamlined and adapted for deployment in field and operational environments at scale for phenotyping studies.
Why it matches plant phenotyping methods3Dフォトグラメトリ、茎の自動抽出、点群解析、姿勢モデルを統合し、植物の屈性形質を定量化・検証する方法が研究の中心である。
abstractThis study introduces an integrated method to quantify tropic behaviour in plant phenotyping studies.
Reproduction assets foundThe paper's data availability statement explicitly deposits the raw photogrammetric point clouds and derived stem curves on Figshare and the R stem-extraction pipeline code on GitHub, both with public URLs.Dataset · publicthe
Ministry of Business Innovation & Employment (MBIE)
New Zealand as part of the Tree Interactions Programme
(Catalyst Fund C09X1923).
Supplementary materials and data availability
The raw photogrammetric point clouds and the stem
curves derived from both photogrammetry and 2D
imagery can be found at the following repository:
https://doi.org/10.6084/m9.figshare.32248617. The
R code for the stem extraction pipeline is available
at https://github.com/Robin-hartley/tropism-stem-curves-3d
Hartley et al. New Zealand Journal of Forestry Science (2026) 56:11 Page 14Open asset ↗figshare · 10.6084/m9.figshare.32248617pdf-raw-page:14 lines:97-113Code · publicamme
(Catalyst Fund C09X1923).
Supplementary materials and data availability
The raw photogrammetric point clouds and the stem
curves derived from both photogrammetry and 2D
imagery can be found at the following repository:
https://doi.org/10.6084/m9.figshare.32248617. The
R code for the stem extraction pipeline is available
at https://github.com/Robin-hartley/tropism-stem-curves-3d
Hartley et al. New Zealand Journal of Forestry Science (2026) 56:11 Page 14Open asset ↗github · Robin-hartley/tropism-stem-curves-3dpdf-raw-page:14 lines:97-113Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Introduction: Habitat restoration is necessary for the conservation and management of plant and animal species, especially in rare ecosystems. Drones may be well-suited to monitor changes in plant and animal communities in response to restoration efforts. The objective of the study was to examine whether drone imagery can detect differences in vegetation across multiple contexts. Materials and methods: Using a commercially available drone, I captured and processed aerial imagery with an open-source photogrammetric processing program. Point cloud data were processed to generate a vegetation density index, which was quantified across four cover types and compared between disturbance histories. In addition, using automated radio tracking, I compared vegetation density between used and available locations for Eastern Whip-poor-wills during the day and at night. Results: In August 2024, a drone flight covering a 3.05 km2 area of pine barrens captured 3372 images. Vegetation density differed by cover type (p = 0.001) and was greater in recently disturbed sites (p = 0.002). Scrub oak and recently burned sites had ~30% and ~12% greater vegetation density than deciduous forests and plots > 2 years post-disturbance, respectively. Vegetation density was lower at Eastern Whip-poor-will used locations than at available locations (151.0 vs. 159.7 points/m2, p < 0.001). Conclusions: Analysis of fine-scale differences in vegetation structure was important in discriminating subtle differences in habitat selection for Eastern Whip-poor-wills. This study demonstrated that drones and relatively simple image processing can be practical tools for restoration when quantifying and monitoring vegetation differences in dynamic ecosystems.
Why it matches plant phenotyping methodsドローン画像と点群処理により植生密度・植生構造を定量化する手法を中心に、異なる植生条件での適用性を評価しているため、植物表現型計測の方法適用研究に該当する。
abstractPoint cloud data were processed to generate a vegetation density index, which was quantified across four cover types and compared between disturbance histories.
Reproduction assets foundThe paper's data availability statement points to a public Zenodo deposit containing the study's drone-derived vegetation density data and related measurements.Dataset · publicThe data supporting the findings of this publication has been made available within a publicly accessible
repository at https://doi.org/10.5281/zenodo.20398090.Open asset ↗Zenodo · 10.5281/zenodo.20398090pdf-page:11 lines:1-49Code / dataset availability confirmedOpenAlex · checked 11 Sept 2026
Unmanned aerial vehicle (UAV) photogrammetry offers a cost-effective approach to tree-level detection, however, Structure-from-Motion (SfM) outputs are sensitive to processing choices and site conditions, which can alter canopy representation and reduce individual-tree detection accuracy. Here, we systematically evaluate how SfM reconstruction quality and depth-map filtering influence RGB-only individual-tree detection under controlled acquisition conditions. Objectives were to (i) identify an optimal SfM-derived point-cloud configuration for delineating individual trees, and (ii) implement and test a segmentation workflow (local-maxima treetop detection plus Dalponte2016 in lidR) for detecting and counting trees. We assessed RGB-only SfM for individual-tree detection (ITD) across thirteen 1.21-ha loblolly pine ( Pinus taeda ) plots located in two counties in the state of Alabama in the southeastern United States; eight even-aged plantations and five mixed pine-hardwood stands, while holding image acquisition parameters constant. Using Agisoft Metashape Professional (Agisoft LLC, St. Petersburg, Russia), dense-cloud quality (Lowest, Low, Medium, High, Ultra High) and depth-map filtering (Disabled, Mild, Moderate, Aggressive) were varied in a 5 × 4 full-factorial design; assessment metrics included point-cloud density, canopy-surface completeness, canopy-height-model (CHM) agreement with field heights, and ITD precision/recall/F1. We identified a single high-resolution configuration (Ultra High + Disabled) by screening parameter sets for structural accuracy and suppression of false peaks. Using this configuration, CHMs matched field heights in Washington County, Alabama (R 2 = 0.96; RMSE = 0.44 m; bias = − 0.01 m) and in Cullman County, Alabama (R 2 = 0.44; RMSE = 1.14 m; bias = − 0.09 m); pooled performance was R 2 = 0.98; RMSE = 0.54 m; bias = − 0.01 m. ITD accuracy at the primary 3 m match radius yielded a precision of 0.03; recall = 0.29; F1 = 0.05 in the even-aged plantations (Washington) and a precision of 0.03; recall = 0.12; F1 = 0.05 in mixed pine–hardwood stands (Cullman); pooled F1 = 0.05. The selected parameters and workflow are reproducible and transferable, provide insight into RGB-SfM ITD performance, and indicate when lidar remains preferable for crown delineation.
Why it matches plant phenotyping methodsRGB-SfMによる個体樹の検出・樹高推定と、SfM設定およびセグメンテーションワークフローの系統的評価が研究の中心であり、植物の樹冠構造・樹高という形態形質を抽出する方法を検証している。
abstractwe systematically evaluate how SfM reconstruction quality and depth-map filtering influence RGB-only individual-tree detection
Reproduction assets foundThe paper's Code availability statement deposits the authors' SfM/ITD processing scripts publicly on OSF (DOI 10.17605/OSF.IO/UXBCZ). Phenotype/field datasets are only available on request, so they are not public assets.Code · publicThe workflow and processing scripts used in this study are publicly available through the Open Science Framework
(OSF) repository: Singh and Narine, [32]. Code Repository for Optimizing SfM Parameters for RGB-Only Individual-Tree
Detection in Loblolly Pine and Mixed Pine-Hardwood Stands. https://doi.org/10.17605/OSF.IO/UXBCZ.Open asset ↗10.17605/OSF.IO/UXBCZpdf-page:12 lines:1-70Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 5 Sept 2026
High-throughput phenotyping is essential for resolving genotype-by-environment interactions and accelerating crop breeding. In greenhouse potted-plant systems, narrow aisles, global navigation satellite system (GNSS)-denied operation, variable pot layouts, and plant-level data traceability constrain repeatable automated phenotyping. This study presents PhenoRob-P, a modular autonomous robotic system designed for potted crops in structured facility environments. The system integrates a compact two-wheel differential chassis, a LiDAR–vision fusion framework for row-level navigation, pot-level target identification and local alignment, a six-degree-of-freedom robotic arm with inverse-kinematics-based real-time pose compensation for repeatable multi-view close-range imaging, and a three-tier User–Cloud–Robot platform for task scheduling, remote monitoring, and closed-loop data management. Greenhouse validation showed throughputs of 520 pots/h in continuous scanning mode and 187 pots/h in multi-view fine inspection mode. At travel speeds of 0.2–0.3 m/s, mean terminal positioning errors remained within 30 mm, and approximately 87% of lateral and longitudinal errors fell within ±30 mm. Biological validation demonstrated time-resolved stress phenotyping in wheat, with color indices capturing drought progression and rewatering recovery. For maize, multi-view three-dimensional reconstruction estimated plant height and stem diameter with R 2 values of 0.940 and 0.845, respectively, relative to manual measurements. These results show that PhenoRob-P provides an integrated perception-localization-acquisition-analysis workflow for high-throughput, traceable, and time-resolved phenotyping of potted crops.
Why it matches plant phenotyping methods植物形質の取得を中核とする自律ロボット型ハイスループット表現型解析プラットフォームを開発・検証しており、画像取得、3D再構成、ストレス・形態形質の推定性能も評価している。
abstractThis study presents PhenoRob-P, a modular autonomous robotic system designed for potted crops in structured facility environments.
Reproduction assets foundThe paper's Data availability statement explicitly deposits authors' source code and sample datasets in a public GitHub repository, matching the allowed URL.Code · publicThe source code and sample datasets supporting the findings of this study are openly available at the following GitHub repository: https://github.com/Sunniersy/PhenoRob-P .Open asset ↗https://github.com/Sunniersy/PhenoRob-P · Sunniersy/PhenoRob-Plines:388-431Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Three-dimensional (3D) point-cloud phenotyping enables non-destructive and repeatable characterization of plant architecture, supporting the measurement of traits such as internode length, branching topology, and organ orientation. This article presents TomatoPGT (Tomato Plant Graph Twin) , a 3D tomato dataset designed for research on semantic/instance segmentation, graph-based structural representation, and graph-derived phenotypic trait extraction. The dataset contains 42 scans from three greenhouse-grown tomato plants acquired across early to mid-vegetative development using a rotational multi-view imaging system. Each scan consists of 60-70 overlapping RGB images captured under uniform illumination and reconstructed into a metrically scaled dense colored point cloud using Structure-from-Motion and multi-view stereo. TomatoPGT provides: (i) multi-view RGB images, (ii) dense colored point clouds, (iii) manually curated semantic and instance annotations at organ level, (iv) graph representations encoding plant topology and geometry, and (v) tabulated phenotypic traits computed deterministically from the graphs (internode length, insertion angles, and phyllotactic angles). TomatoPGT supports reproducible development and evaluation of 3D phenotyping pipelines, including learning-based segmentation and graph-based modeling of plant architecture.
Why it matches plant phenotyping methods植物の3D形態表現型抽出を目的としたデータセットで、画像・点群・器官アノテーション・グラフ・形質値を提供し、再現可能なフェノタイピング手法の開発と評価を直接支援している。
abstractThis article presents TomatoPGT (Tomato Plant Graph Twin) , a 3D tomato dataset designed for research on semantic/instance segmentation, graph-based structural representation, and graph-derived phenotypic trait extraction.
Reproduction assets foundThe paper's own TomatoPGT dataset (multi-view RGB images, dense point clouds, semantic/instance annotations, graph representations, and CSV phenotypic traits) is publicly deposited on Mendeley Data, and the authors' Cloud-Seg/Cloud-Graph software tools plus supplementary materials (camera calibrations, example datasetsDataset · publicRepository name 1: Mendeley[2].
Data identification number: DOI: 10.17632/72md54c7n7.1
Direct URL to data: https://data.mendeley.com/datasets/72md54c7n7/1Open asset ↗Mendeley · 10.17632/72md54c7n7.1html-lines:105-178Code · public6. Code and documentation: CloudSeg and CloudGraph software tools, environment specifications, and example usage instructions are hosted on Zenodo[3].Open asset ↗Zenodohtml-lines:264-308Code / dataset availability confirmedCrossref · OpenAlex · checked 14 Sept 2026
Abstract Warmer temperatures, permafrost thaw, and increased wildfire activity are driving rapid ecological change across the Arctic, significantly altering plant productivity and aboveground biomass (AGB). These rapid changes highlight the urgent need to improve monitoring of vegetation dynamics in the Earth’s northern ecosystems, where high spatiotemporal heterogeneity occurs at scales finer than those captured by traditional satellite observations. The growing use of unoccupied aerial systems (UASs) presents an opportunity to overcome this limitation. Yet, the diversity of UAS platforms, sensors, and data collection and processing workflows presents challenges for developing standardized, generalizable approaches. To address this challenge, we compiled 672 AGB plots co-located with 183 UAS-based structure-from-motion (SfM) or light detection and ranging (LiDAR) surveys collected across the Arctic. Here, we: (1) evaluated the generalizability of UAS-derived canopy structure derived from high-resolution SfM and LiDAR for estimating AGB, (2) assessed scaling errors and their sources in two recent satellite-based AGB products derived from Landsat and moderate resolution imaging spectroradiometer, and (3) demonstrated the use of high-resolution AGB maps to quantify biomass variation across tundra plant functional types (PFTs) and to monitor post-fire recovery. Our results show that both SfM and LiDAR accurately captured AGB and its variability across tundra PFTs using a random forest model (overall root mean squared error: 0.332 kg m –2 ), with mapping performance varying slightly by region and data source. Using UAS-derived AGB maps as a benchmark, we identified systematic biases in satellite-derived AGB products, largely attributable to the magnitude of AGB and structural heterogeneity within coarse-resolution pixels. Applying our model to repeat UAS surveys following a tundra fire on Seward Peninsula, we observed rapid AGB recovery in non-shrub patches, with biomass recovering to pre-fire levels within two years. In contrast, shrub patches recovered more slowly, with AGB gains continuing over 2–4 years through both in-patch growth and lateral expansion (via dispersal) into remaining burned areas. Overall, these findings support the generalizability of UAS-based SfM and LiDAR data for estimating tundra AGB and highlight the need for broader collection and synthesis of such data to improve ecological monitoring and model benchmarking in the Arctic.
Why it matches plant phenotyping methodsUASのSfMおよびLiDARから植物群落の地上部 biomass (AGB) を推定する手法の一般化性能を評価し、衛星推定値のベンチマークにも用いており、植物形質取得が研究の中心である。
abstractevaluated the generalizability of UAS-derived canopy structure derived from high-resolution SfM and LiDAR for estimating AGB
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe codes and training data is available on GitHub: https://github.com/Daryl-Open asset ↗pdf-page:20 lines:1-30Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Spatially accurate estimates of forest above-ground biomass (AGB) are indispensable for carbon-stock accounting and sustainable silviculture. Existing mapping approaches face challenges in densely vegetated Coastal Plain forests because of seasonal optical variability, radar–optical saturation, and limited wall-to-wall structural information. We aimed to (i) develop and evaluate a multisensor, AI-enabled fusion framework for landscape-scale AGB mapping, (ii) quantify the added value of seasonal optical data and photogrammetric canopy-height profiles, and (iii) interpret model drivers using explainable artificial intelligence (AI) to relate predictors to forest structure and composition. We mapped AGB across ~ 10,500 km 2 in southeastern North Carolina using wall-to-wall predictors from optical, radar, and photogrammetric sources. Forest Inventory and Analysis plot data (n = 305) were used to train and evaluate an ensemble of gradient-boosted tree models (CatBoost, LightGBM, XGBoost) and a neural network (RealMLP) via cross-validation. Model behavior was interpreted using feature importance and partial dependence analysis. Expanding Sentinel-2 temporal coverage from summer-only to four-season composites improved normalized RMSE by 8.7%. Incorporating canopy-height profiles from NAIP produced the largest accuracy gain, lowering nRMSE by 15.9–18.0% relative to the multisensor baseline, which underscores the critical value of structural information for AGB prediction. Three key predictors illustrated complementary ecological dimensions: the 10th percentile canopy height captured canopy openness, L-band polarimetric alpha indicated volume-scattering regime, and spring red-edge reflectance captured vegetation biochemistry. These findings show that fusing structure, polarimetry, and spectral phenology yields robust AGB maps and improves generalizability across heterogeneous landscapes. This transferable, broadly accessible framework integrating structural, polarimetric, and spectral phenology data enables landscape-scale AGB monitoring and supports targeted conservation planning, restoration tracking, and adaptive management for carbon sequestration. The incorporation of high-resolution wall-to-wall structural data is particularly valuable for improving the accuracy and usability of forest AGB maps, thereby informing more responsive decision-making.
Why it matches plant phenotyping methods森林の地上部バイオマスという植物群落形質を対象に、光学・レーダー・写真測量データを融合した推定フレームワークを開発・評価しており、形質推定手法が研究の中心である。
abstractdevelop and evaluate a multisensor, AI-enabled fusion framework for landscape-scale AGB mapping
Reproduction assets foundThe authors explicitly state that the code reproducing all figures and analyses is archived in a GitHub repository and permanently preserved via Zenodo (doi 10.5281/zenodo.18688899). The GEDI-derived CHM25 product (Zenodo 11176727) is a cited prior-work dataset from Wang et al. (2025), not this paper's own asset, and FCode · publicGEDI data products are distributed by NASA’s Land Processes
Distributed Active Archive Center and are accessible through
Google Earth Engine. The code used to reproduce all figures
and analyses has been archived in a GitHub repository (https://
github.com/ChaoEcohydroRS/NC_SoutheastBiomass) and
permanently preserved via Zenodo (https://doi.org/10.5281/zenodo.18688899, submitted on 20 February 2026).
Declarations
Conflict of interest The authors declare no competing inter-
ests.
Disclaimer The findings and conclusions in this publication
are those of the author(s) and should not be construed to rep-
resent any official USDA or U.S. Government determination or
policy.
Open Access This articleOpen asset ↗Zenodo · 10.5281/zenodo.18688899pdf-raw-page:23 lines:1-89Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
ABSTRACT Lodging is a major contributor to decreased yield in tef, a staple cereal crop in Ethiopia. Semidwarf varieties have been developed with a goal to increase yield through reduced lodging, but studying lodging susceptibility currently requires a labor‐intensive, imprecise, manual scoring method. Here we present workflows for analyzing tef stand height from UAS sensors across time to both predict lodging later in the season with early height and to measure the severity of lodging after a storm event. We compare 3D point clouds generated by photogrammetry from RGB images with those generated from LiDAR to estimate height, demonstrating that they produce similar results, despite differences in cost. Stand height and lodging can both be accurately measured with low‐cost UAS, reducing the need for manual measurements and increasing precision and temporal resolution in plant breeding programs.
Why it matches plant phenotyping methodsUAS画像・LiDARによるテフの草高と倒伏程度の推定ワークフローを開発・比較し、育種での測定精度向上を示す中心的な表現型計測研究。
abstractHere we present workflows for analyzing tef stand height from UAS sensors across time to both predict lodging later in the season with early height and to measure the severity of lodging after a storm event.
Reproduction assets foundThe paper's Data Availability Statement and Methods sections point to a public GitHub repository containing the authors' analysis code and associated data (including PheNode sensor data), plus the PlantCV-Geospatial package used for the RGB/LiDAR height and lodging analysis.Code · publicthe USDA NIFA AFRI (Grant Number
2022-
67021-
36467 to N.F.), and by the Bellwether Foundation.
Conflicts of Interest
Getu Beyene has patent “Lodging resistance in Eragrostis tef” pending
to Donald Danforth Plant Science Center.
Data Availability Statement
Code and data associated with this manuscript are available on GitHub
(https://github.com/danforthcenter/teff-manuscript).References
Abebe, Y., A. Bogale, K. Michael Hambidge, B. J. Stoecker, and R. S.
Gibson. 2007. “Phytate, Zinc, Iron and Calcium Content of Selected Raw
and Prepared Foods Consumed in Rural Sidama, Southern Ethiopia,
and Implications for Bioavailability.” Journal of Food Composition and
Analysis 20, no. 3: 161–168.
AssOpen asset ↗danforthcenter/teff-manuscriptpdf-raw-page:8 lines:1-98Code · publicyzing images of plants (Gehan
et al. 2017; Schuhl et al. 2026) that provides a framework for
measuring and storing observations extracted per object within
each image. All code associated with these analyses is available
on GitHub (https://github.com/danforthcenter/teff-manuscript),
as well as the PlantCV-
Geospatial package (https://github.com/danforthcenter/plantcv-geospatial). As observed in the ortho-
mosaic (Figure 1A), tef plots were planted under power lines in
the field, which could not be flown under due to UAS safety re-
strictions. Pixels belonging to powerlines needed to be removed
to measure plot heights. During import, PlantCV-
Geospatial
was used with a height percentile tOpen asset ↗danforthcenter/plantcv-geospatialpdf-raw-page:4 lines:1-107Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
BACKGROUND: Leaf inclination angle (LIA) is a key trait affecting crop canopy structure and photosynthetic efficiency, but its accurate measurement is challenging due to complex leaf geometry, especially in narrow, curved rice leaves. As the flag leaf serves as the primary photosynthetic organ in rice, the precise spatial parsing of its architecture is crucial for optimizing canopy light interception and yield potential. With the rapid development of high-throughput phenotyping technologies, an increasing number of studies have focused on the fine-grained characterization of 3D crop architecture. However, accurate methodologies for extracting the flag leaf inclination angle (FLIA) in rice, as well as systematic investigations into its spatiotemporal variation patterns, remain largely unexplored. RESULTS: In this study, we systematically evaluated multiple plane-fitting strategies based on SfM-MVS point clouds, finding that voxel-based piecewise analysis outperformed traditional global approaches. To further improve accuracy, skeleton extraction methods were innovatively extended to LIA estimation. A proposed multi-method ensemble, based on the median of eight skeleton extraction combinations, yielded high robustness (R2 = 0.923, RMSE = 2.072°) against photographic ground truth. By applying the proposed framework to both field- and pot-grown rice, we observed no significant FLIA differences between varieties or nitrogen treatments under field-grown conditions, likely due to phenotypic plasticity regulated by population effects. However, pot-grown plants, experiencing reduced interplant competition, exhibited significant varietal differences in FLIA. Across growth environments, varieties, and nitrogen treatments, FLIA at maturity was significantly lower than at anthesis and grain filling stages due to leaf senescence. CONCLUSIONS: This study establishes a robust and accurate measurement framework for LIA based on 3D point clouds, improving estimation performance through piecewise analysis, voxelization, and ensemble strategies. The proposed approach is demonstrated to be an effective tool for the precise quantification of rice leaf phenotypes.
Why it matches plant phenotyping methodsSfM-MVS点群からイネ葉の傾斜角を抽出する手法を開発・検証し、圃場および鉢植えで適用しているため、植物フェノタイピング手法が研究の中心である。
abstractA proposed multi-method ensemble, based on the median of eight skeleton extraction combinations, yielded high robustness (R2 = 0.923, RMSE = 2.072°) against photographic ground truth.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe python program, complete dataset, including the original two-dimensional images and corresponding piecewise measurement trajectories, is publicly available at https://github.com/Interstingsun/LIA (accessed on 6 February, 2026).Open asset ↗Interstingsun/LIAlines:77-83Code / dataset availability confirmedOpenAlex · checked 5 Sept 2026
Unmanned aerial vehicles (UAVs) have become indispensable tools in precision agriculture and plant phenotyping, enabling the rapid, non-destructive assessment of crop traits across space and time. Equipped with RGB, multispectral, thermal, and other sensors, UAVs provide detailed information on canopy structure, physiology, and stress responses that can guide management decisions and accelerate breeding programs. Despite these advances, the downstream processing of UAV imagery remains technically demanding. Converting orthomosaics into standardized, biologically meaningful data often requires a combination of photogrammetry, geospatial analysis, and custom scripting, which can limit reproducibility and accessibility across research groups. We present drone2report, an open-source python-based software that processes orthomosaics from UAV flights to generate vegetation indices, summary statistics, derived subimages, and text (html) reports, supporting both research and applied crop breeding needs. Alongside the basic structure and functioning of drone2report, we also present five case studies that illustrate practical applications common in UAV-/drone-phenotyping of plants: (i) thresholding to remove background noise and highlight regions of interest; (ii) monitoring plant phenotypes over time; (iii) extracting information on plant height to detect events like lodging or the falling over of spikes; (iv) integrating multiple sensors (cameras) to construct and optimize new synthetic indices; (v) integrate a trained deep learning network to implement a classification task. These examples demonstrate the tool’s ability to automate analysis, integrate heterogeneous data and models, and support reproducible computation of agronomically relevant traits. drone2report streamlines orthorectified UAV-image processing for precision agriculture by linking orthomosaics to standardized, plot-level outputs. Its modular, configuration-driven design allows transparent workflows, easy customization, and integration of multiple sensors within a unified analytical framework. By facilitating reproducible, multi-modal image analysis, drone2report lowers technical barriers to UAV-based phenotyping and opens the way to robust, data-driven crop monitoring and breeding applications.
Why it matches plant phenotyping methods植物表現型取得のためのUAV画像処理ソフトウェアを開発し、植物高・倒伏などの形質抽出、マルチセンサー統合、再現可能な解析ワークフローを中心的に提示している。
abstractWe present drone2report, an open-source python-based software that processes orthomosaics from UAV flights to generate vegetation indices, summary statistics, derived subimages, and text (html) reports
Reproduction assets foundThe paper explicitly states that the code and data to reproduce its five case studies (thresholding, temporal vegetation indices, height analysis, multi-sensor index optimization, deep learning classification) are publicly available in the authors' GitHub repository, and the DRONE2REPORT software itself is released as Code · publicThe code and data to reproduce these case studies
can be found at https://github.com/ne1s0n/paper-drone2report (accessed on 13 April
2026).Open asset ↗ne1s0n/paper-drone2reportpdf-page:6 lines:1-59Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
ABSTRACT 3D reconstruction has matured into a robust technology. However, small, flexible objects such as conifer seedlings remain challenging due to their fine‐scale structures, and susceptibility to movement. This study investigates and evaluates methods for reconstructing spruce ( Picea abies ) and pine ( Pinus sylvestris ) seedlings, with the aim of establishing a workflow capable of capturing geometry and texture for applications in machine learning and virtual testing environments. Two acquisition approaches were tested: photogrammetry using a RGB camera and a 3D scanner, both mounted on a robotic arm. While the scanner produced incomplete results, the photogrammetry approach successfully generated point clouds (pcl) with color information. Three different photogrammetry software were tested before relying on Agisoft Metashape and Meshroom for image processing and dense pcl generation, followed by pcl filtering in CloudCompare and meshing in Blender. Six seedlings were reconstructed to textured meshes and quantitatively evaluated using the metrics precision, recall, F1‐score, mask intersection‐over‐union (IoU), and boundary IoU. Results showed an average mask IoU of 75.7% and F1‐score of 86.1%. Pine seedlings yielded higher recall and F1‐scores, whereas spruce reconstructions demonstrated higher precision. The proposed semi‐automated workflow demonstrates the feasibility of reconstructing small and slender structured flexible objects, specifically conifer seedlings.
Why it matches plant phenotyping methods針葉樹苗の形状・テクスチャを取得する3D画像再構成ワークフローを開発・比較・定量評価しており、植物フェノタイピング手法が中心である。
abstractThis study investigates and evaluates methods for reconstructing spruce ( Picea abies ) and pine ( Pinus sylvestris ) seedlings, with the aim of establishing a workflow capable of capturing geometry and texture for applications in machine learning and virtual testing environments.
Reproduction assets foundThe paper's Data Availability Statement states that the raw seedling image data and finalized textured meshes (the paper's phenotyping/3D reconstruction inputs and outputs) are freely available on Zenodo under DOI 10.5281/zenodo.19823955, which appears in the allowed URL list.Dataset · publicand without adjusting the scanning parameters, while also re-
Data Availability Statement
taining texture and color. In contrast to prior approaches that
require manual intervention or do not preserve visual informa- Raw image data and finalized textured meshes are freely available at
Zenodo.org with https://doi.org/10.5281/zenodo.19823955.
tion, the proposed workflow enables a semi-automated recon-
struction process suitable for dataset generation. As shown, the
methodology is effective for the digital reconstruction of small References
and slender structured flexible objects and holds potential for
Abbood, S. A., H. A. Ajjah, A. H. H. Alboabidallah, M. U. MohaOpen asset ↗Zenodopdf-layout-page:14 lines:50-74Code / dataset availability confirmedOpenAlex · Crossref · checked 13 Sept 2026
Abstract Background: Next-generation models of fire behavior and smoke production rely on gridded, 3D inputs of wildland fuel complexes. We used a hierarchically scaled sampling design to characterize canopy and surface fuels that are common to prescribed burning programs in the southeastern and western US. Sampling included airborne laser scanning, terrestrial laser scanning, close-range photogrammetry, and destructive field sampling. The objective of this study was to use a combination of airborne laser scanning (ALS), terrestrial laser scanning (TLS), structure-from-motion photogrammetry (SfM), and field observations to create co-located 3D datasets of live and dead understory fuels for use in wildland fuel mapping and prescribed burn decision support Results: Using our integrated, co-located methods, we produced hierarchically-scaled datasets detailing the structure and composition of canopy and surface fuels across 9 southeastern pine sites, 5 western pine sites, and 4 western grassland sites. These are now publicly available at within the Wildland Fire Science Initiative data repository (https://doi.org/10.60594/W4859C). In this paper, we detail methods and the repository structure. Conclusions: The study was designed to evaluate and advance methods for 3D fuel characterization and to provide consistently scaled and labelled datasets for model training and evaluation. More specifically, machine learning models can be used to parse 3D point clouds collected from ALS, TLS, and structure-from-motion photogrammetry into fuel objects and metrics. Calibration with field plots will allow our hierarchically-scaled datasets to be used as the foundation for synthetic fuelbed mapping, starting with fine-scale objects such as individual shrubs or downed wood and scaling to vegetation patches and operational burn units.
Why it matches plant phenotyping methodsALS、TLS、SfMと現地観測を統合して植物群落の3D構造・燃料特性を取得し、手法の評価・改良と公開データセット構築を主目的としているため、植物形質計測法が中心である。
abstractThe objective of this study was to use a combination of airborne laser scanning (ALS), terrestrial laser scanning (TLS), structure-from-motion photogrammetry (SfM), and field observations to create co-located 3D datasets of live and dead understory fuels for use in wildland fuel mapping and prescribed burn decision support
Reproduction assets foundThe paper's hierarchically scaled ALS/TLS/SfM point clouds, field fuel measurements, and analysis scripts are explicitly stated to be open source and archived in the Wildland Fire Science Initiative data repository (DOI 10.60594/W4859C), a paper-specific public asset directly reproducing this study's phenotyping/fuel-3Dataset · publicThe datasets and analysis scripts for this study are open source and are being archived with the Wildland Fire Science Initiative data repository (doi.org/10.60594/W4859C), including project metadata, methods documentation and data libraries (Prichard and Rowell 2025).Open asset ↗Wildland Fire Science Initiative data repository · 10.60594/W4859Clines:384-403Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
3D crop phenotyping technology provides critical support for screening morphology-related plant genes and identification of germplasm resource. Organ segmentation or recognition is the first key step in 3D crop phenotyping, where inductive deep learning currently dominates as the mainstream methodology. However, the high requirement for data annotation in inductive learning paradigm has transformed the manual data labeling into a labor-intensive task, thereby in turn restricting the progress of inductive learning. This problem has inspired us to leverage Graph Neural Networks (GNNs) as the transductive learning tool to directly segment organs on sparsely annotated crop point clouds. We propose a Dual-branch Graph Convolutional Network (DBGCN) that only requires sparse labels to perform organ instance inference directly on plant point clouds that have featureless point features. Different from existing graph-based networks, DBGCN not only carries out the static-feature-space graph convolutions that are good at mining and aggregating on local information on the point cloud, but also incorporates dynamic graph convolutions that captures the potential changes of the graph manifold in deep feature space. Extensive experiments prove that the fusion of two types of graph feature convolutions brings a high node (point) classification accuracy, outperforming mainstream GNNs and even several popular inductive deep architectures. On the PlantNet sub-dataset, DBGCN achieves an mAcc (mean accuracy of node classification) of 93.00% under 1.95% manual annotation ratio. On the Soybean-MVS sub-dataset, DBGCN achieves an mAcc of 91.05% under 4.88% manual annotation ratio. Furthermore, our DBGCN not only works well on crop 3D data but can also serve other applications such as the segmentation of point cloud data for large-scale street view. Our dataset and code can be found at https://github.com/chinazhouzhaoyi/DBGCN/tree/master/.
Why it matches plant phenotyping methods3D植物点群から器官を分割・推論する深層学習手法を開発し、植物フェノタイピングデータ上で精度検証しているため、表現型取得・抽出法が中心である。
abstractWe propose a Dual-branch Graph Convolutional Network (DBGCN) that only requires sparse labels to perform organ instance inference directly on plant point clouds
Reproduction assets foundThe authors explicitly state that their dataset (plant point clouds) and DBGCN code are publicly available on GitHub.Code · publicOur data and code are available at: https://github.com/chinazhouzhaoyi/DBGCN/tree/master/.Open asset ↗chinazhouzhaoyi/DBGCNhtml-lines:414-455Dataset · publicOur dataset and code can be found at https://github.com/chinazhouzhaoyi/DBGCN/tree/master/Open asset ↗chinazhouzhaoyi/DBGCNhtml-lines:88-94Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Service crops are grown to provide ecosystem services in viticulture, but their adoption remains limited due to their competition with grapevine for soil resources. To identify trade-offs between services, the effect of service crops management strategies on grapevine performances still need further research. This dataset presents data from two experiments conducted to study the effect of service crops management on soil resources and grapevine performances. The inter-row vegetation was sampled in two Mediterranean vineyards using quadrats for biomass estimation. In addition, an unmanned aerial vehicle (UAV) was regularly flown over the vineyards for a period spanning more than four years in total over the two vineyards. The dataset presented here includes both raw data acquired during fieldwork and processed data derived from this raw inputs. The raw data consists of image series captured by two UAVs during each flight campaign, including RGB and multispectral imagery. Images were acquired between 2021-06-10 and 2022-07-29 for the first vineyard, and between 2023-06-08 and 2025-03-12 for the second vineyard. Based on these raw data, the processed data comprises spatial vectors, raster layers, and dense point clouds generated from UAV images using a Structure from Motion (SfM) photogrammetry workflow, at a 5 cm spatial resolution. The raster layers and dense point clouds provide specific information on vineyard characteristics for each UAV flight date, including elevation, vegetation indices, visible and near-infrared reflectance, and canopy height. In addition, the processed data include measurements of vegetation dry biomass, as well as separate measurements of dry biomass and leaf area measured for selected service crops species. This dataset can be reused for the calibration and/or evaluation of classification algorithms aimed at discriminating vines from the inter-row vegetation, or as part of a larger dataset to explore relationships between remotely-sensed vegetation indices and field-measured vegetation biomass or surface.
Why it matches plant phenotyping methodsUAV画像とSfM処理により、植生指数・樹冠高・バイオマス等の植物形質を取得した再利用可能なデータセットで、分類アルゴリズムの校正・評価用途も明示されており、植物フェノタイピング手法・データ基盤が中心です。
abstractThe dataset presented here includes both raw data acquired during fieldwork and processed data derived from this raw inputs.
Reproduction assets foundThe paper is a Data in Brief article describing a public dataset on Research Data Gouv (doi: 10.57745/MXM55R) containing UAV RGB/multispectral imagery, SfM-derived rasters and point clouds, and field-measured vegetation biomass/leaf-area data from two Mediterranean vineyards — directly the paper's phenotyping inputs. ADataset · publicollected in vineyards located in southern France near Montpellier (43°32.5243′N, 3°50.8240′E). Data are stored on Research Data Gouv, a remote storage solution curated by the French Department of Research.
Data accessibility
Repository name: Research Data Gouv
Data identification number: doi: 10.57745/MXM55R
Direct URL to data: https://doi.org/10.57745/MXM55R
Related research article
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Value of the Data
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The fine scale imaging of vineyards (i.e., 5 cm resolution) allows for classification of the vegetation in the vineyard inter-rows, and subsequent exploration of its respective dynamics.
•Open asset ↗Research Data Gouv · 10.57745/MXM55Rlines:1-47Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
To address the inefficiency and high cost of manual counting of tobacco leaves, this study proposes a UAV-based method for automatic leaf counting in field-grown tobacco using 3D point clouds and an improved PointNext network. Although UAV imagery has been applied to crop phenotyping, most existing UAV-based leaf-counting methods still rely on 2D images or hand-crafted features and rarely exploit 3D point clouds with dedicated leaf-level segmentation, which limits accuracy and robustness under leaf overlap, variable viewing angles, and complex field backgrounds. In this work, oblique UAV photogrammetry is used to reconstruct individual plants into 3D point clouds, and a segmentation network, SRW-PointNext, is developed by integrating an SCSA attention mechanism and a Residual-SegHead to enhance feature extraction and segmentation performance, while a re-weighted loss alleviates class imbalance. Leaf point clouds are then clustered using MeanShift to obtain leaf counts. Experiments on field-grown tobacco demonstrate that the proposed method achieves a point-cloud segmentation precision of 92.09%, a MIoU of 76.13%. Compared with the original PointNext baseline, SRW-PointNext increased MIoU and overall precision by 3.34% and 2.42% respectively. The final accuracy rate of leaf counting was 92.61%, effectively achieving accurate and stable leaf counting under actual field conditions, and providing technical support for digital management, yield estimation and seedling breeding in tobacco production.
Why it matches plant phenotyping methodsUAV三次元画像と改良セグメンテーション手法により圃場タバコの葉数を推定する方法を開発・検証しており、表現型取得が研究の中心である。
abstractthis study proposes a UAV-based method for automatic leaf counting in field-grown tobacco using 3D point clouds and an improved PointNext
Reproduction assets foundThe paper reports a UAV-based tobacco leaf counting method with an annotated 1000-plant point cloud dataset and SRW-PointNext code, both explicitly declared publicly available at author-provided Zenodo and GitHub URLs matching the allowed list.Dataset · publicData supporting the reported results can be found at: https://zenodo.org/records/15130271 .Open asset ↗zenodo · 15130271lines:531-564Code · publicThe code used in this study is available at: https://github.com/Nan20377/SRW-Pointnext.git .Open asset ↗github · Nan20377/SRW-Pointnextlines:531-564Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Post-disturbance recovery is a central element of forest resilience against intensifying disturbance regimes. Although recovery signals are strong across Central European forests, the relative roles of different factors contributing to recovery remain incompletely understood. As climate change increasingly challenges recovery, elucidating these processes is essential to adapt forest management to changing climate and disturbance regimes. We extended and applied a biologically grounded model of forest growth to remote sensing data to quantify how management shapes two key drivers of canopy recovery—disturbance legacies and post-disturbance height growth—across Bavaria, Germany. We combined 23,036 ha of quality-filtered photogrammetric canopy height model data with a Landsat-based disturbance map, a forest ownership map and environmental covariates in a Bayesian modelling framework. Post-disturbance growth rates were governed primarily by forest type and site conditions, whereas management strongly influenced disturbance legacies, i.e. the remaining post-disturbance vegetation height structure on site. Legacies varied widely across management types: Federal and set-aside forests retained the highest level of disturbance legacies, while private forests had the lowest legacy levels. Despite marginally lower growth rates, set-aside areas had recovery trajectories that were comparable to managed forests. The median recovery time to 5 m mean canopy height was 14.3 years over all forest and management types. Set-aside areas exhibited the greatest variation in recovery trajectories. We here show that (i) management affects disturbance legacies more strongly than post-disturbance tree growth, (ii) set-aside areas do not differ in recovery speed from managed areas, and (iii) legacies are diversifying forest recovery trajectories, with potential implications for future forest resilience. Our results underline that the post-disturbance reorganization window is a crucial period for management to influence long-term forest development. The framework presented here provides a scalable approach to monitor structural recovery and guide adaptive forest policy and management under increasing disturbance. • Forest management in Central Europe affects post-disturbance recovery more via legacies than tree growth rates. • Set-aside forests recover their canopy height equally fast as managed forests in Central Europe. • Homogenizing and removing disturbance legacies can reduce forest canopy variation across forest stand development. • We combined a biological growth model with remote sensing data to assess forest canopy recovery.
Why it matches plant phenotyping methodsリモートセンシングによる林冠高構造の定量と生物学的成長モデルを組み合わせ、森林の構造回復をスケーラブルにモニタリングする枠組みが研究の中心である。
abstractWe extended and applied a biologically grounded model of forest growth to remote sensing data to quantify how management shapes two key drivers of canopy recovery—disturbance legacies and post-disturbance height growth—across Bavaria, Germany.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the analysis data and code on Zenodo with a public DOI, which is a paper-specific, publicly actionable asset for reproducing the forest recovery analysis.Code · publicthank three anonymous reviewers for providing helpful
suggestions on an earlier version of the work.
Appendix A. Supporting information
Supplementary data associated with this article can be found in the
online version at doi:10.1016/j.foreco.2026.123616.
Data availability
Data and code of the analysis are available at Zenodo: https://doi.org/10.5281/zenodo.17804070.References
Anderson-Teixeira, Kristina J., Miller, Adam D., Mohan, Jacqueline E., Hudiburg, Tara
W., Duval, Benjamin D., DeLucia, Evan H., 2013. Altered Dynamics of Forest
Recovery under a Changing Climate. Glob. Change Biol. 19 (7), 2001–2021. https://
doi.org/10.1111/gcb.12194.
Arano, Kathryn G., Munn, Ian A., 2006. Evaluating Open asset ↗Zenodo · 10.5281/zenodo.17804070pdf-raw-page:10 lines:1-55Code / dataset availability confirmedEurope PMC · OpenAlex · checked 5 Sept 2026
Estimating canopy structure - leaf inclination distribution (LIDFa), leaf area index (LAI), and fractional vegetation cover (FCover) - is vital for breeding, yet the added value of multi-angular UAV sensing over nadir-only baselines remains insufficiently quantified. This study developed a UAV-based multi-angular inversion framework that derived high-resolution bidirectional reflectance factors (BRF) from oblique photogrammetry and fitted a kernel-driven BRDF model to characterize reflectance anisotropy. Using transfer learning across cultivars and dates, we compared the retrieval performance of multi-angle versus nadir-only baselines for LIDFa, LAI, and FCover. BRDF model simulations agreed well with airborne BRF (optimal R 2 > 0.80, RRMSE R 2 = 0.59 vs. 0.38 for the best MA and NAD models, respectively) and LIDFa ( R 2 = 0.46 vs. 0.37). For FCover, both configurations achieved high accuracy ( R 2 ≥ 0.73), with MA models providing marginal gains ( R 2 = 0.75). Methodologically, CNN-based transfer learning proved most effective for LAI and FCover, while a Random Forest model using raw multi-angle spectra yielded the best results for LIDFa. Optimal viewing configurations were trait-dependent, generally favoring forward scattering directions with zenith angles between 15° and 45°. These results indicate that kernel-driven BRDF modeling effectively captures spectral anisotropy in dense wheat canopies, and that multi-angular observations provide a distinct advantage for retrieving structural parameters with complex scattering behaviors, such as LAI and LIDFa.
Why it matches plant phenotyping methods小麦育種材料のキャノピー構造形質を対象に、UAVマルチアングルセンシング、BRDFモデル、CNN/RFによる推定フレームワークを開発・比較しており、形質取得手法が研究の中心である。
abstractThis study developed a UAV-based multi-angular inversion framework that derived high-resolution bidirectional reflectance factors (BRF) from oblique photogrammetry and fitted a kernel-driven BRDF model to characterize reflectance anisotropy.
Reproduction assets foundThe paper's data availability statement explicitly deposits the complete source code for BRDF modeling and the transfer learning pipeline, plus a subset of preprocessed field data, in a public GitHub repository matching an allowed URL. Additional data are available only on request.Code · publicThe complete source code for BRDF modeling and the transfer learning pipeline, along with a subset of the preprocessed field data used in this study, are openly available in the GitHub repository at https://github.com/ZWM-RS/UAV-multi-angle-inversion-of-canopy-structure-parameters-in-wheat-breeding-materials.git . Any additional data supporting the findings of this study are available from the corresponding author upon reasonable request.Open asset ↗ZWM-RS/UAV-multi-angle-inversion-of-canopy-structure-parameters-in-wheat-breeding-materialslines:451-474Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 5 Sept 2026
Background Rice plant architecture underpins yield and grain quality, yet two obstacles impede accurate field characterization in dense paddies. First, single-plant reconstruction is constrained by severe inter-plant occlusion, cluttered backgrounds, and limited viewpoints. These factors obscure culms, leaves, basal tillers, and the true physical scale of the plant. Active ranging devices are cumbersome in outdoor plots and can lose accuracy, whereas conventional passive photogrammetry performs poorly under such conditions. Second, delineating panicles within a 3D rice model is intrinsically difficult. Panicles are slender, highly branched, and visually similar to surrounding foliage, often interwoven and partially hidden. These factors result in fragmented boundaries and missing details. Direct point-cloud segmentation struggles with such discontinuous geometry and requires costly 3D annotation, whereas generic image segmentation models trained on natural scenes transfer poorly to paddy imagery. These challenges motivate a field-ready workflow that both reconstructs whole plants at high resolution in dense plantings and reliably segments panicles to enable trait extraction. Results A low-cost, in-field, multi-view pipeline for whole-plant three-dimensional reconstruction, termed One Stop 3D Target Reconstruction And segmentation (OSTRA), operates on color images with a reference-board setup. The pipeline builds detailed three-dimensional models of individual rice plants and automatically segments key organs (in this case, panicles), despite dense surrounding vegetation. When applied to 231 diverse rice landraces grown in a crowded field setting, the method produced high-fidelity plant models with clearly delineated panicle structures. From these reconstructions, three architectural traits were derived: plant height, leaf area, and panicle length. Genome-wide association analysis of the measured traits identified strong genotype-phenotype associations tagging known candidate genes. Natural variants at D2 and RFL/APO2 were associated with plant height variation, variants at FLW7 were linked to differences in leaf area, and allelic variation at AAI1 corresponded to panicle length variation. These loci are established regulators of plant growth and morphology, indicating that this three-dimensional phenotyping pipeline attains accuracy sufficient to rediscover meaningful genetic signals. Conclusions This study provides a practical tool for precise rice phenotyping even under dense field planting conditions, overcoming occlusion and structural complexity. By enabling non-destructive, field-based measurement of complete plant architecture and linking these phenotypes to specific genes, the pipeline bridges field phenomics and genomics. The integrated reconstruction and analysis framework advances the study of rice architecture and offers a general route to connect complex traits with their genetic determinants.
Why it matches plant phenotyping methods密植圃場でのイネ全体3D再構築、器官分割、形質抽出を中核とする画像ベース表現型解析手法の開発・実証であり、明確に収載対象。
abstractA low-cost, in-field, multi-view pipeline for whole-plant three-dimensional reconstruction, termed One Stop 3D Target Reconstruction And segmentation (OSTRA), operates on color images with a reference-board setup.
Reproduction assets foundThe paper explicitly states that the 3D rice plant models (231 landraces) are deposited on Zenodo and the OSTRA source code is publicly available on GitHub. Both are paper-specific, public, and actionable.Code · publicThe source code of OSTRA is available on GitHub at [http://github.com/ganlab/ostra] (http:/github.com/ganlab/ostra).Open asset ↗github · ganlab/ostralines:217-246Code / dataset availability confirmedEurope PMC · bioRxiv · OpenAlex · checked 15 Sept 2026
Lodging is a major contributor to decreased yield in tef, a staple cereal crop in Ethiopia. Semidwarf varieties have been developed with a goal to increase yield through reduced lodging, but studying lodging susceptibility currently requires a labor-intensive, imprecise, manual scoring method. Here we present workflows for analyzing tef stand height from UAS sensors across time to both predict lodging later in the season with early height and to measure the severity of lodging after a storm event. We compare 3D point clouds generated by photogrammetry from RGB images with those generated from LiDAR to estimate height, demonstrating that they produce similar results, despite differences in cost. Stand height and lodging can both be accurately measured with low-cost UAS, reducing the need for manual measurements and increasing precision and temporal resolution in plant breeding programs. Significance Statement Extreme weather or heavy grain can cause plant stems to bend, a process called lodging. Lodging significantly reduces crop yields globally, particularly in grain crops such as tef ( Eragrostis tef ). Semidwarf crops have previously been reported to be lodging-resistant, increasing crop yields. Here, we used uncrewed aerial systems (UAS) to measure plant growth, height, and lodging in gene edited semidwarf tef lines, and compared the results to ground-truth data. Using a UAS equipped with a red-green-blue (RGB) camera or LiDAR sensor, we measured plant height and lodging, and found that early-season height measurements could predict future lodging potential. The tools used were contributed to the open-source software PlantCV-Geospatial for community use. This work contributes to a broader understanding of genetic resistance to lodging, providing valuable insights for tef crop improvement and reduces the need for labor-intensive manual measurements.
Why it matches plant phenotyping methodsUASのRGB画像・LiDARから3D点群を生成し、植物の草高と倒伏を定量化・検証するワークフローが研究の中心であるため、植物フェノタイピング手法として含める。
abstractHere we present workflows for analyzing tef stand height from UAS sensors across time to both predict lodging later in the season with early height and to measure the severity of lodging after a storm event.
Reproduction assets foundThe paper states that code and data associated with the manuscript (UAS-based tef height/lodging phenotyping analyses) are publicly available in the authors' GitHub repository danforthcenter/teff-manuscript. The PlantCV-Geospatial package and D2S platform are general-purpose tools/platforms rather than paper-specific,.Code · publicInstitute Block Grant to K.M.M. and
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N.F., the National Science Foundation (grant numbers 2120153 and 2346101 to N.F.),
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the USDA NIFA AFRI (grant number 2022-67021-36467 to N.F.), and by the Bellwether
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Foundation.
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Data Availability
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Code and data associated with this manuscript are available on GitHub
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(https://github.com/danforthcenter/teff-manuscript).477
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CC-BY 4.0 International license
available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprint
this version posted January 7, 2026.
;
https://doi.org/10.64898/2026.01.0Open asset ↗danforthcenter/teff-manuscriptpdf-raw-page:13 lines:1-76Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Nature-based climate solutions, such as agroforestry, offer potential for carbon sequestration while providing co-benefits. However, the lack of scalable and low-cost measurement, reporting, and verification (MRV) systems limits smallholder participation in carbon markets. This study presents the DiameterAlgorithm, a non-contact method for tree diameter estimation using semantic segmentation and two-dimensional photogrammetry. The fine-tuned model achieved a mean intersection over union (mIoU) of 0.937. The algorithm was tested on image datasets from managed trees settings in Kenya (n = 142) and Pennsylvania, USA (n = 40), with regression analysis showing high accuracy (R² = 0.97, RMSE = 2.20–2.23 cm). Bias analysis showed slight overestimation for small to medium trees (5–35 cm DBH) and underestimation for larger trees (>36 cm DBH), with an overall mean bias of +0.68 cm. Coupled with allometric equations, the DiameterAlgorithm enables scalable, site-level biomass estimation for carbon markets.
Why it matches plant phenotyping methods樹木直径という植物形態形質を画像から推定する手法を開発し、複数地域のデータで精度・バイアスを検証しており、フェノタイピング手法が研究の中心である。
abstractThis study presents the DiameterAlgorithm, a non-contact method for tree diameter estimation using semantic segmentation and two-dimensional photogrammetry.
Reproduction assets foundThe paper publicly releases its tree image dataset (calibration/evaluation images from Kenya and Pennsylvania) on ScholarSphere and the containerized diameter estimation tool on Docker Hub, both explicitly stated in the data availability statement.Dataset · publicThe image dataset that was used to calibrate and evaluate the algorithm can be found on the ScholarSphere repository
of the Pennsylvania State University (https://scholarsphere.psu.edu/resources/08a985a4-d878-4fa9-b2f2-60601005Open asset ↗ScholarSphere · 08a985a4-d878-4fa9-b2f2-60601005pdf-page:13 lines:1-61Code / dataset availability confirmedEurope PMC · checked 13 Sept 2026
Field / plotPhotogrammetry / SfM / MVSLiDAR / point cloudMultispectral / hyperspectralLeaf2D/3D reconstructionArchitecture / morphology / geometry
Conifer shoots possess highly complex geometrical structures at a very fine spatial resolution. Accurately characterizing the full architecture of a conifer shoot, which influences how radiation is scattered, has proven challenging. Previous radiative transfer models for coniferous stands have represented these structures in a relatively simplified or coarse manner. This paper presents a dataset that can be used for up-scaling of needle to shoot optical properties and studying the influence of detailed three-dimensional (3D) structure of shoot to light scattering within tree crown. The dataset includes 3D structural information as well optical properties of needles and twigs for 27 shoots of two conifer species present in both locations (3 shoots per species and position in the crown) - Scots pine ( Pinus sylvestris L.) and Norway spruce ( Picea abies L. Karst. ). The samples were collected on 22nd April 2024 in Rájec, the Czech Republic and 17th September 2024 in Järvselja, Estonia. Subsequently blue light 3D photogrammetry scanning technique was used to obtain their high-resolution 3D point cloud representations. Reflectance and transmittance measurements of needles were obtained using a spectroradiometer and an integrating sphere. For each of these samples, the dataset comprises a photo of the sampled shoot, obtained 3D surface reconstruction, and optical properties of conifer needles and twigs (hemispherical-conical reflectance and transmittance factors) in the spectral range of 400-2000 nm. A detailed 3D representation of needle shoots, when combined with radiative transfer modeling, may offer a means to study and compensate for inaccuracies in the measurement of needle optical properties and to enhance the assessment of shoot scattering characteristics.
Why it matches plant phenotyping methods針葉樹シュートの3D構造をフォトグラメトリで取得し、光学特性とともに再利用可能なデータセットとして提供しているため、植物形態・構造の計測手法が中心です。
abstractThis paper presents a dataset that can be used for up-scaling of needle to shoot optical properties and studying the influence of detailed three-dimensional (3D) structure of shoot to light scattering within tree crown.
Reproduction assets foundThe paper is a Data in Brief article describing a public Mendeley Data repository containing the paper's own phenotyping measurements: 3D surface geometry models (.obj) of Scots pine and Norway spruce shoots, sample photos (.jpg), and needle/twig optical property spectra (HCRF/HCTF, .csv, 400-2000 nm). The repository, Dataset · publicRepository name: Mendeley
Data identification number: 10.17632/h39f9t7fjg.1
Direct URL to data: https://data.mendeley.com/datasets/h39f9t7fjg/2Open asset ↗Mendeley · 10.17632/h39f9t7fjg.1lines:47-74Code / dataset availability confirmedOpenAlex · checked 13 Sept 2026
Abstract. Historical aerial images, captured by film cameras in the previous century, are valuable resources for quantifying Earth's surface and landscape changes over time. In the post-war period, these images were often acquired to create topographic maps, resulting in the acquisition of large-scale aerial photographs with stereo coverage. Photogrammetric techniques applied to these stereo images enable the extraction of 3D information to reconstruct digital surface models (DSMs) and orthoimages. Here, we present a highly automated photogrammetric approach for generating countrywide DSMs of Switzerland, at a 1 m resolution, from approximately 32 000 scanned aerial stereo images acquired between 1979 and 2006, with known exterior and interior orientation. We derived four countrywide DSMs for the epochs 1979–1985, 1985–1991, 1991–1998, and 1998–2006. From the DSMs, we generated corresponding countrywide vegetation height models (VHMs). We assessed the quality of the historical DSMs at the country scale and within six representative study sites, evaluating the vertical accuracy and the completeness of image matching across different land cover types. Mean completeness ranged from 64 % for “glacial and perpetual snow” to 98 % for “sealed surfaces”, with a value of 93 % for the “closed forest” class. Across Switzerland, the median elevation accuracy of the historical DSMs compared with a reference digital terrain model (DTM) on sealed surface points ranged from 0.08 to 0.16 m, with a normalized median absolute deviation (NMAD) of around 0.8 m and a maximum root mean square error (RMSE) of 1.20 m. Similar accuracies are obtained when comparing historical DSMs with measured geodetic points. The VHMs generated in this study enabled the detection of major changes in forest areas due to windstorm damage, forest dynamics, and growth. This work demonstrates the feasibility of generating accurate, very-high-resolution DSM time series (spanning three decades) and VHMs from historical aerial images of the entire surface of Switzerland in a highly automated manner. The VHMs are already being used to estimate countrywide biomass changes. The countrywide DSMs and VHMs for the four epochs, along with auxiliary data, are available online at https://doi.org/10.16904/envidat.528 (Marty et al., 2024) and can be used to quantify long-term elevation changes and related processes across different surfaces.
Why it matches plant phenotyping methods歴史的航空画像から植生高モデルを生成する自動写真測量法を開発・精度評価し、森林の高さ変化という植物キャノピー形質を抽出しているため、測定法が中心的である。
abstractFrom the DSMs, we generated corresponding countrywide vegetation height models (VHMs).
Reproduction assets foundThe paper's countrywide DSMs, VHMs, and auxiliary rasters (matching mask, vegetation mask, metadata shapefile) for four epochs are deposited publicly on EnviDat with an explicit DOI. These vegetation height models are the paper's plant/canopy phenotyping measurements. No author analysis code or trained models are namedDataset · publicDatasets can be accessed from EnviDat ( https://doi.org/10.16904/envidat.528 , Marty et al., 2024). The following files are available for the four epochs: countrywide digital surface model (DSM), hillshaded DSM, and vegetation height models (VHMs).Open asset ↗Envidat · 10.16904/envidat.528lines:249-256Code / dataset availability confirmedOpenAlex · Europe PMC · checked 14 Sept 2026
Accurate 3D phenotyping of agricultural produce remains challenging due to the trade-off between reconstruction quality and acquisition throughput in existing sensing technologies. While RGB-D cameras enable high-throughput scanning in operational settings like harvesting conveyors, they produce incomplete, low-quality 3D models. Conversely, close-range Structure-from-Motion (SfM) produces high-quality reconstructions but is not suitable for high-throughput field application. This study bridges this gap through 3DPotatoTwin , a paired dataset containing 339 tuber samples across three cultivars collected in Hokkaido, Japan. Our dataset uniquely combines: (1) conveyor-acquired RGB-D point clouds, (2) ground measurement, (3) SfM reconstructions under indoor controlled environment, and (4) aligned model pairs with transformation matrices. The multi-sensory alignment employs an semi-supervised pin-guided pipeline incorporating single-pin extraction and referencing, cross-strip matching, and binary-color-enhanced ICP, achieving 0.59 ± 0.11 mm registration accuracy. Beyond serving as a benchmark for 3D phenotyping algorithms, the dataset enables training of 3D completion networks to reconstruct high-quality 3D models from partial RGB-D point clouds. Meanwhile, the proposed semi-automated annotation pipeline has the potential to accelerate 3D dataset generation for similar studies. The presented methodology demonstrates broader applicability for multi-sensor data fusion across crop phenotyping applications. The dataset and pipeline source code are publicly available at HuggingFace and GitHub, respectively.
Why it matches plant phenotyping methodsジャガイモ塊茎の3D表現型計測を対象に、RGB-D・SfM・地上計測を統合したデータセット、位置合わせパイプライン、ベンチマークを開発しており、表現型取得手法が中心である。
abstractAccurate 3D phenotyping of agricultural produce remains challenging due to the trade-off between reconstruction quality and acquisition throughput in existing sensing technologies.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicAll the batch processing scripts mentioned in this section were provided in the 3dscan folder at Github (https://github.com/UTokyo-FieldPhenomics-Lab/PotatoScan/).Open asset ↗UTokyo-FieldPhenomics-Lab/PotatoScanhtml-lines:119-131Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
This research aims to develop a novel technique to acquire a large amount of high-density, high-precision 3D point cloud data for plant phenotyping using photogrammetry technology. The complexity of plant structures, characterized by overlapping thin parts such as leaves and stems, makes it difficult to reconstruct accurate 3D point clouds. One challenge in this regard is occlusion, where points in the 3D point cloud cannot be obtained due to overlapping parts, preventing accurate point capture. Another is the generation of erroneous points in non-existent locations due to image-matching errors along object outlines. To overcome these challenges, we propose a 3D point cloud reconstruction method named closed-loop coarse-to-fine method with multi-masked matching (CLCFM3). This method repeatedly executes a process that generates point clouds locally to suppress occlusion (multi-matching) and a process that removes noise points using a mask image (masked matching). Furthermore, we propose the closed-loop coarse-to-fine method (CLCFM) to improve the accuracy of structure from motion, which is essential for implementing the proposed point cloud reconstruction method. CLCFM solves loop closure by performing coarse-to-fine camera position estimation. By facilitating the acquisition of high-density, high-precision 3D data on a large number of plant bodies, as is necessary for research activities, this approach is expected to enable comparative analysis of visible phenotypes in the growth process of a wide range of plant species based on 3D information.
Why it matches plant phenotyping methods植物フェノタイピングのためのフォトグラメトリ画像から高精度3D点群を再構成する手法を開発しており、表現型取得法が研究の中心である。
abstractThis research aims to develop a novel technique to acquire a large amount of high-density, high-precision 3D point cloud data for plant phenotyping using photogrammetry technology.
Reproduction assets foundThe authors explicitly deposit the MMM/CLCFM analysis code and scripts in a public GitHub repository, which also provides download links to the supporting image and 3D point cloud data used in this paper's soybean phenotyping reconstructions. The supplementary material contains only result figures, not datasets. Gene/NCode · publicThe computer codes and scripts of MMM and CLCFM are deposited in a GitHub repository at https://github.com/tanasoft/MMM-CLCFM (accessed on 3 September 2025). Download links to image and 3D point cloud data that support the findings of this study are also provided at this GitHub repository.Open asset ↗https://github.com/tanasoft/MMM-CLCFMlines:116-305Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
In this study, we explore the application of videogrammetry for 3D reconstruction in complex forest environments, aiming to enhance forest inventory measurement methods. Traditional techniques are often labor-intensive and lack scalability in dense or challenging terrain. We assess the efficacy of videogrammetry for generating 3D point clouds in complex forest environments, focusing on an Insta 360 Pro 2 setup with six fish-eye cameras. Harnessing this lightweight and user-friendly technology, we aim to elevate the process of data collection while delivering realistic visual representations of forest areas. Our approach enables the estimation of key forest characteristics, such as tree distribution and Diameter at Breast Height (DBH). The average errors for tree position and DBH measurements range from 5.2 cm to 18.8 cm and from 0.9 cm to 1.9 cm, respectively. The reconstructed 3D tree information is structurally similar to data obtained with Terrestrial Laser Scanning (TLS), with normally distributed Multiscale Model-to-Model Cloud Comparison (M3C2) errors with a mean of 0 cm and a standard deviation of 15 cm to 22 cm. Our method reduces the need for manual data collection, thus supporting effective forest management and planning.
Why it matches plant phenotyping methods森林内の樹木形態（樹木位置・胸高直径）を videogrammetry で推定する手法を開発し、TLS と比較検証しており、植物フェノタイピング手法が中心である。
abstractWe assess the efficacy of videogrammetry for generating 3D point clouds in complex forest environments
Reproduction assets foundThe authors publicly deposited the videogrammetric point clouds generated by their pipeline (with walkthrough demos and TLS comparison videos) on Zenodo, directly reproducing this paper's 3D reconstruction measurements.Dataset · publicd have appeared
to influence the work reported in this paper.
Appendix A. Supplementary data
Supplementary material related to this article can be found online
at https://doi.org/10.1016/j.ecoinf.2025.103398.Data availability
The generated videogrammetric point clouds using the proposed
pipeline are available for download here: https://doi.org/10.5281/zenodo.16258209. The folder also contains walkthrough demos of the
point clouds, as well as video comparisons with TLS-derived point
clouds.
References
AgiSoft, 2018. AgiSoft metashape professional (version 1.4.5) (software),. Available
Online: http://www.agisoft.com.Alsadik, B., Gerke, M., Vosselman, G., 2015. Efficient use of video for 3D moOpen asset ↗zenodo · 10.5281/zenodo.16258209pdf-raw-page:12 lines:1-70Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
The architecture of rice tillers plays a pivotal role in yield potential, yet conventional phenotyping methods have struggled to capture these intricate three-dimensional (3D) structures with high fidelity. In this study, a 3D model reconstruction method was developed specifically for rice tillers to overcome the challenges posed by their slender, feature-poor morphology in multi-view stereo-based 3D reconstruction. By applying strategically designed colorful reference markers, high-resolution 3D tiller models of 231 rice landraces were reconstructed. Accurate phenotyping was achieved by introducing ScaleCalculator, a software tool that integrated depth images from a depth camera to calibrate the physical sizes of the 3D models. The high efficiency of the 3D model-based phenotyping pipeline was demonstrated by extracting the following seven key agronomic traits: flag leaf length, panicle length, first internode length below the panicle, stem length, flag leaf angle, second leaf angle from the panicle, and third leaf angle. Genome-wide association studies (GWAS) performed with these 3D traits identified numerous candidate genes, nine of which had been previously confirmed in the literature. This work provides a 3D phenomics solution tailored for slender organs and offers novel insights into the genetic regulation of complex morphological traits in rice.
Why it matches plant phenotyping methodsイネ分げつの3D再構成とScaleCalculatorによるスケール校正を開発し、7つの形態形質を抽出するフェノタイピング手法が研究の中心であるため。
abstracta 3D model reconstruction method was developed specifically for rice tillers
Reproduction assets foundThe paper's 3D tiller models for 231 rice landraces are publicly deposited on Zenodo, and the authors' ScaleCalculator phenotyping source code is publicly available on GitHub, both explicitly stated in the Data Availability Statement. SNP genotype data are unpublished and excluded.Code · publicvelopment Co. LTD, and
Jiangsu Collaborative Innovation Center for Modern Crop Production.
Data Availability Statement: The 3D tiller models created in this study are available for research pur-
poses at https://zenodo.org/records/16080993 (accessed on 18 July 2025).The source code of ScaleCal-
culator is available on GitHub at https://github.com/ganlab/OSTRA/tree/master/ScaleCalculator
(accessed on 18 July 2025).
Acknowledgments: We thank Jianmin Wan for their valuable suggestions and Jiaqi Deng for their
technical help.
Conflicts of Interest: The authors declare that there are no conflicts of interest regarding the publica-
tion of this article.
References
1. Food and Agriculture OrganizatOpen asset ↗github · ganlab/OSTRApdf-raw-page:16 lines:1-50Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Abstract Background Remote sensing techniques for assessing fire severity using two-dimensional imagery, such as satellite data, are limited to a single severity value per pixel, typically at a 30-m resolution. This often leads to an underestimation of understory fire severity, as live tree crowns can obscure the extent of the burned area beneath. By leveraging the three-dimensional capabilities of drone imagery, a more comprehensive assessment of fire severity across different canopy height strata can be achieved. Methods We show how drone digital aerial photogrammetry (dDAP), also known as structure from motion, can be used to generate three-dimensional multispectral photogrammetric point clouds for quantifying fire effects at various canopy height strata as well as classify ground cover below normally occluding overstory trees. Conducted during prescribed fires at Fort Jackson, South Carolina, RGB and multispectral imagery were collected via drone both pre- and post-fire at five plots, with two additional unburned plots flown to serve as controls. Multispectral photogrammetric point clouds were generated and NDVI values were calculated for each point. Point clouds were segmented into 2-m height stratum layers, to compare NDVI values for different canopy height strata pre- and post-fire. Orthoimages of the understory, overstory, and traditional nadir views were generated. Conclusions Findings showed that prescribed fire had a substantial effect on NDVI values up to 6 m in height, with only minor effects observed above 6 m. Ground cover under the canopy, typically occluded from overhead imagery, was classified with 87% accuracy. This study demonstrated the ability to digitally remove occluding tall vegetation using dDAP and to derive a more precise assessment of fire effects on ground and understory vegetation compared to two-dimensional satellite imagery.
Why it matches plant phenotyping methodsドローンの3次元マルチスペクトル点群を用いて、植物の樹冠層別の火災影響・NDVI・地被状態を抽出する手法が研究の中心であり、単なる生物学的測定ではない。
abstractcan be used to generate three-dimensional multispectral photogrammetric point clouds for quantifying fire effects at various canopy height strata as well as classify ground cover below normally occluding overstory trees
Reproduction assets foundThe paper's Data availability statement points to a public deposit of the drone orthophotos and videos (the sensor imagery inputs used to build the multispectral point clouds) on the Wildland Fire Science Initiative data portal under DOI 10.60594/W48G6B. No author analysis code, trained models, or derived phenotype/traDataset · publicther funded by the Precision Forestry Cooperative at Univer-
sity of Washington.
Strategic Environmental Research and Development Program,RC-2640,David
R. Weise,University of Washington Precision Forestry Cooperative
Data availability
Drone orthophotos and videos are available on the Wildland Fire Science
Initiative data portal https://portal.wfsi-data.org/view/doi:https://doi.org/10.60594/W48G6B (Weise et al. 2025).Open asset ↗10.60594/W48G6Bpdf-raw-page:15 lines:92-98Code / dataset availability confirmedOpenAlex · Europe PMC · checked 13 Sept 2026
Community-based forest restoration has the potential to sequester large amounts of atmospheric carbon, avoid forest degradation, and support sustainable development. However, if partnered with international funders, such projects often require robust and transparent aboveground carbon measurements to secure payments, and current monitoring approaches are not necessarily appropriate due to costs, scale, and complexity. The use of consumer-grade drones in combination with open source structure-from-motion photogrammetry may provide a solution. In this study, we tested the suitability of a simplified drone-based method for measuring aboveground carbon density in heavily degraded tropical forests at a 2 ha restoration site in Sabah, Malaysia, comparing our results against established field-based methods. We used structure-from-motion photogrammetry to generate canopy height models from drone imagery, and applied multiple pre-published plot-aggregate allometric equations to examine the importance of utilising regionally calibrated allometric equations. Our results suggest that this simplified method can produce aboveground carbon density measurements of a similar magnitude to field-based methods, quickly and only with a single input metric. However, there are greater levels of uncertainty in carbon density measurements due to errors associated with canopy height measurements from drones. Our findings also highlight the importance of selecting regionally calibrated allometric equations for this approach. At scales between 1 and 100 ha, drone-based methods provide an appealing option for data acquisition and carbon measurement, balancing trade-offs between accuracy, simplicity, and cost effectiveness and coinciding well with the needs of community-scale aboveground carbon measurement. Of importance, we also discuss considerations relating to the accessibility of this method for community use, beyond purchasing a drone, that must not be overlooked. Nevertheless, the method presented here lays the foundations for a simple workflow for measuring aboveground carbon density at a community scale that can be refined in future studies.
Why it matches plant phenotyping methodsドローン画像とSfMから森林キャノピー高モデルを生成し、地上部炭素密度を推定する測定ワークフローを開発・検証しており、植物群落の形態・状態の取得が研究の中心である。
abstractWe used structure-from-motion photogrammetry to generate canopy height models from drone imagery
Reproduction assets foundThe authors state that all drone images and field data underlying this study's aboveground carbon density measurements are publicly available in the CEDA Archive with a catalogue record and DOI. This is a paper-specific, public, directly actionable dataset. Other URLs (OpenDroneMap, LAStools, QGIS, PyCrown) are genericDataset · publicAll drone images and field data are publicly available from the CEDA Archive, a NERC repository for earth observation data. The dataset can be accessed via the following catalogue record link: https://catalogue.ceda.ac.uk/uuid/98692ec457ee431cacc4027820e46411/ (DOI: https://doi.org/10.5285/98692ec457ee431cacc4027820e46411 ).Open asset ↗CEDA Archive · 10.5285/98692ec457ee431cacc4027820e46411lines:151-177Code / dataset availability confirmedCrossref · Europe PMC · OpenAlex · checked 6 Sept 2026
With the rapid advancements in computer vision and deep learning, multi-view stereo (MVS) based on conventional RGB cameras has emerged as a promising and cost-effective tool for botanical research. However, existing methods often struggle to capture the intricate textures and fine edges of plants, resulting in suboptimal 3D reconstruction accuracy. To overcome this challenge, we proposed Edge_MVSFormer on the basis of TransMVSNet, which particularly focuses on enhancing the accuracy of plant leaf edge reconstruction. This model integrates an edge detection algorithm to augment edge information as input to the network and introduces an edge-aware loss function to focus the network’s attention on a more accurate reconstruction of edge regions, where depth estimation errors are obviously more significant. Edge_MVSFormer was pre-trained on two public MVS datasets and fine-tuned with our private data of 10 model plants collected for this study. Experimental results on 10 test model plants demonstrated that for depth images, the proposed algorithm reduces the edge error and overall reconstruction error by 2.20 ± 0.36 mm and 0.46 ± 0.07 mm, respectively. For point clouds, the edge and overall reconstruction errors were reduced by 0.13 ± 0.02 mm and 0.05 ± 0.02 mm, respectively. This study underscores the critical role of edge information in the precise reconstruction of plant MVS data.
Why it matches plant phenotyping methods植物の葉のエッジと3D形状を高精度に再構築するMVS手法を開発・評価しており、植物表現型取得の方法が中心である。
titleEdge_MVSFormer: Edge-Aware Multi-View Stereo Plant Reconstruction Based on Transformer Networks
Reproduction assets foundThe paper's Data Availability Statement explicitly states that the dataset (private multi-view plant images with ground truth point clouds/depth maps) and the code used in this study are publicly available on Zenodo, with the URL matching an allowed URL.Code · publicThe dataset and code used in this study are publicly available at the webpage https://zenodo.org/records/15086606 with a DOI: 10.5281/zenodo.15086606, accessed on 19 March 2025.Open asset ↗zenodo · 10.5281/zenodo.15086606lines:95-266Code / dataset availability confirmedEurope PMC · OpenAlex · checked 6 Sept 2026
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によるダイズ群落の3D構造再構成と、収量推定・倒伏判別のための専用深層学習モデル開発が研究の中心であり、再利用可能な表現型取得・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.
Reproduction assets foundThe article's Data availability statement explicitly says the code and data used in the study (soybean UAV point cloud phenotyping, SoyNet/SoyNet-Res models, yield/lodging analysis) are publicly downloadable from the authors' GitLab repository.Code · publicData availability
The code and data mentioned in the article can be downloaded from https://gitlab.com/zlyzly28/plant-phenomics .Open asset ↗gitlab.com/zlyzly28/plant-phenomicslines:588-659Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
Published12 Mar 2025Investigaciones Geográficas Boletín del Instituto de GeografíaCited by 1 · OpenAlex ↗
This study employs photogrammetry to evaluate and monitor the recovery of the cypress forest on Guadalupe Island, Mexico, an ecosystem significantly impacted by fires and overgrazing. Two drone surveys were conducted over the forest area during the summers of 2016 and 2019 using natural color (RGB) and near-infrared (NIR) cameras. This work presents the first complete 3D reconstruction of the cypress forest on the island. The image processing products include the canopy height model (CHM), digital surface model (DSM), and digital terrain model (DTM), which were utilized to calculate the number, density, height and crown projected areas of trees. The CHM showed a high correlation with the forest's structure (R = 0.92), based on field measurements of tree heights. Our study accounted for approximately 67,340 trees taller than two meters in 2019. Over 90% of the cypress population consisted of young trees between 2 and 3 meters tall, which have recovered significantly following a fire in 2008 that burned 70% of its extent. A horizontal expansion of 134 hectares was observed from 2016 to 2019 in the regeneration process.
Why it matches plant phenotyping methodsドローン画像のフォトグラメトリによる3D再構成を用いて樹木の高さ・密度・樹冠面積を推定し、現地測定との相関で検証しているため、植物形質の取得手法が中心です。
abstractThis study employs photogrammetry to evaluate and monitor the recovery of the cypress forest on Guadalupe Island, Mexico
Reproduction assets foundThe paper's photogrammetric phenotyping products (2016/2019 point clouds, orthomosaics, DSMs, CHMs) are publicly downloadable via a DOI data repository, and supplemental crown/treetop features are in CICESE's institutional repository. Both URLs appear in allowed_urls.Dataset · publicees. This phenomenon can be seen
in the three years observation window (2016-2019)
using photogrammetry.
AVAILABILITY OF DATA
AND MATERIALS
Point clouds from the 2016 and 2019 photogram-
metric reconstructions, as well as orthomosaics,
digital surface models (DSMs), and canopy height
models (CHMs), are available for download in
https://doi.org/10.5069/G9668BDD and https://
doi.org/10.5069/G92J693D. Supplemental infor-
mation such as Features related to crown and tree-
tops are accessible through CICESE’s institutional
repository (https://repositoriobiblioteca.cicese.mx/jspui/handle/123456789/44)
REFERENCES
Aljos-Farjon. (2017). A handbook of the world’s conifers
(second ed., vol. 1).Open asset ↗10.5069/G9668BDD · 10.5069/G9668BDDpdf-raw-page:15 lines:1-89Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 6 Sept 2026
Differences in canopy architecture play a role in determining both the light and water use efficiency. Canopy architecture is determined by several component traits, including leaf length, width, number, angle, and phyllotaxy. Phyllotaxy may be among the most difficult of the leaf canopy traits to measure accurately across large numbers of individual plants. As a result, in simulations of the leaf canopies of grain crops such as maize and sorghum, this trait is frequently approximated as alternating 180° angles between sequential leaves. We explore the feasibility of extracting direct measurements of the phyllotaxy of sequential leaves from 3D reconstructions of individual sorghum plants generated from 2D calibrated images and test the assumption of consistently alternating phyllotaxy across a diverse set of sorghum genotypes. Using a voxel-carving-based approach, we generate 3D reconstructions from multiple calibrated 2D images of 366 sorghum plants representing 236 sorghum genotypes from the sorghum association panel. The correlation between automated and manual measurements of phyllotaxy is only modestly lower than the correlation between manual measurements of phyllotaxy generated by two different individuals. Automated phyllotaxy measurements exhibited a repeatability of R 2 = 0.41 across imaging timepoints separated by a period of two days. A resampling based genome wide association study (GWAS) identified several putative genetic associations with lower-canopy phyllotaxy in sorghum. This study demonstrates the potential of 3D reconstruction to enable both quantitative genetic investigation and breeding for phyllotaxy in sorghum and other grain crops with similar plant architectures.
Why it matches plant phenotyping methods3D再構成とボクセル・カービングにより、ソルガムの葉序を自動抽出・定量し、手動測定との比較と再現性評価まで行っており、植物表現型取得法が研究の中心である。
abstractWe explore the feasibility of extracting direct measurements of the phyllotaxy of sequential leaves from 3D reconstructions of individual sorghum plants generated from 2D calibrated images
Reproduction assets foundThe paper's data availability statement explicitly provides public access to the reconstruction/skeletonization code (GitHub SorghumVoxelCarving), the raw 2D sorghum images used for voxel-carving 3D reconstruction (Zenodo DOI 10.5281/zenodo.4426620), and the phenotypic data, GWAS result files, and analysis/figure code,Code · publicThe code for reconstruction and skeletonization is available at GitHub: https://github.com/cropsinsilico/SorghumVoxelCarving .Open asset ↗cropsinsilico/SorghumVoxelCarvinglines:93-131Dataset · publicThe raw images analyzed in this study are available at Zenodo: Mathieu Gaillard, Chenyong Miao, James C. Schnable, & Bedrich Benes. (2021). Voxel Carving Based 3D Reconstruction of Sorghum [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4426620 .Open asset ↗Zenodo · 10.5281/zenodo.4426620lines:93-131Code · publicThe phenotypic data, GWAS result files and code for main figures and analysis are available at Github: https://github.com/jdavis-132/phyllotaxy.git .Open asset ↗jdavis-132/phyllotaxylines:93-131Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
We show non-invasive 3D plant disease imaging using automated monocular vision-based structure from motion. We optimize the number of key points in an image pair by using a small angular step size and detection in the extra green channel. Furthermore, we upsample the images to increase the number of key points. With the same setup, we obtain functional fluorescence information that we map onto the 3D structural plant image, in this way obtaining a combined functional and 3D structural plant image using a single setup.
Why it matches plant phenotyping methods植物の3D構造と蛍光機能情報を取得・統合する画像計測手法の開発が中心であり、植物病害の非侵襲的フェノタイピングに該当します。
abstractWe show non-invasive 3D plant disease imaging using automated monocular vision-based structure from motion.
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the code and datasets for reproducing the SfM 3D plant imaging results in the 4TU repository, with a DOI matching an allowed URL.Code · publicThe code and data sets for reproducing the results are available in 4TU repository at https://doi.org/10.4121/e6db8707-10ee-4553-9a98-753f1b4c526a .Open asset ↗4TU repository · 10.4121/e6db8707-10ee-4553-9a98-753f1b4c526alines:52-127Code / dataset availability confirmedEurope PMC · OpenAlex · checked 14 Sept 2026
In quantitative genomic analysis of wheat plant height (PH), the average height of a few representative plants is typically used to represent the PH of the entire plot, which overlooks the variation in height among other plants. Extracting different height quantiles from canopy point clouds can address this limitation. For this purpose, low-cost UAV cross-circling oblique (CCO) imaging, combined with structure-from-motion (SfM) and multi-view stereopsis (MVS), was employed to generate precise canopy point clouds for 262 F5 recombinant inbred lines (Zhongmai 578 × Jimai 22) across seven environments. Multi-level 3D-PH measurements were extracted from six height quantiles, revealing a strong correlation (mean r = 0.95) between 3D-PH and field-measured PH (FM-PH) across environments. The 90 % and 92 % height quantiles showed the closest agreement with FM-PH compared to other quantiles. Eleven stable quantitative trait loci (QTLs) associated with multi-level 3D-PH were identified using a 50K single nucleotide polymorphism array. Among these, QPhzj.caas-3A.2 (detected by 3D-PH) and QPhzj.caas-7A.1 (detected by both FM-PH and 3D-PH) represented potential novel loci. KASP markers for these QTLs were developed and validated. Furthermore, within the intervals of QPhzj.caas-5A and QPhzj.caas-3B (both were detected by 3D-PH), two candidate genes associated with PH regulation were identified: TaGL3-5A and Rht5 , respectively. Corresponding KASP markers for these genes were also developed and validated. This study highlighted the advantages of 3D model and multi-level 3D-PH in elucidating the genetic basis of crop height, and provided a precise and objective basis for advancing wheat breeding programs.
Why it matches plant phenotyping methodsUAV画像からSfM/MVSで3Dキャノピーモデルを構築し、複数の高さ分位点として植物高を抽出・検証することが研究の中心であるため、画像ベースの植物フェノタイピング手法として適格。
abstractExtracting different height quantiles from canopy point clouds can address this limitation.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe plant height data for various environments and the detailed information of the genetic map can be downloaded from https://github.com/ILIKEWIND123/Plant-Phenomics .Open asset ↗ILIKEWIND123/Plant-Phenomicslines:364-399Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Abstract. Historical aerial images, captured by film cameras in the previous century, are valuable resources for quantifying Earth’s surface and landscape changes over time. In the post-war period, these images were often acquired to create topographic maps, resulting in the acquisition of large-scale aerial photographs with stereo coverage. Photogrammetric techniques applied to these stereo images enable the extraction of 3D information to reconstruct digital surface models (DSMs) and orthoimages. Here, we present a highly automated photogrammetric approach for generating countrywide DSMs of Switzerland, at a 1 m resolution, from approximately 40,000 scanned aerial stereo images acquired between 1979 and 2006, with known exterior and interior orientation. We derived four countrywide DSMs for the epochs 1979–1985, 1985–1991, 1991–1998, and 1998–2006. From the DSMs, we generated corresponding countrywide vegetation height models (VHMs). We assessed the quality of the historical DSMs at the country scale and within six representative study sites, evaluating the vertical accuracy and the completeness of image-matching across different land cover types. Mean completeness ranged from 64 % for ‘glacial and perpetual snow’ to 98 % for ‘sealed surfaces’, with a value of 93 % for the ‘closed forest’ class. Across Switzerland, the median elevation accuracy of the historical DSMs compared with a reference digital terrain model (DTM) on sealed surface points ranged from 0.28 to 0.53 m, with a normalised median absolute deviation (NMAD) of around 1 m and a maximum root mean square error (RMSE) of 3.90 m. The same analysis between geodetic points and historical DSMs showed higher accuracies, with median values of ≤ 0.05 m and an NMAD < 1 m. The VHMs generated in this study enabled the detection of major changes in forest areas due to windstorm damage, forest dynamics, and growth. This work demonstrates the feasibility of generating accurate, very high-resolution DSM time series (spanning three decades) and VHMs from historical aerial images of the entire surface of Switzerland in a highly automated manner. The VHMs are already being used to estimate countrywide biomass changes. The countrywide DSMs and VHMs for the four epochs, along with auxiliary data, are available online at https://doi.org/10.16904/envidat.528 (Marty et al., 2024) and can be used to quantify long-term elevation changes and related processes across different surfaces.
Why it matches plant phenotyping methods歴史航空画像から植生高モデルを自動生成するフォトグラメトリ手法を開発・精度評価し、森林の高さ変化という植物状態を測定するデータセットも提供しているため、植物フェノタイピング手法が中心である。
abstractFrom the DSMs, we generated corresponding countrywide vegetation height models (VHMs).
Reproduction assets foundThe paper's own countrywide DSM and vegetation height model (VHM) rasters, plus masks and metadata, are publicly deposited on EnviDat with an explicit DOI, directly reproducing the paper's vegetation height measurements.Dataset · public22
5 Data availability
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Datasets can be accessed from EnviDat (https://doi.org/10.16904/envidat.528, Marty et al., 2024). The following files are
435
available for the four epochs: countrywide digital surface model (DSM), hillshaded DSM, and vegetation height models
436
(VHMs). A metadata shapefile is provided with information about the acquisition year of the photographs used here; the
437
geometry corresponds to the 1:25,00Open asset ↗EnviDat · 10.16904/envidat.528pdf-raw-page:22 lines:1-63Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
Tree height and vertical forest structure are important attributes in forestry, but their traditional measurement or assessment in the field is expensive, time-consuming, and often inaccurate. One of the main advantages of using remote sensing data to estimate vertical forest structure is the ability to obtain accurate data for larger areas in a more time- and cost-efficient manner. Temporal changes are also important for estimating and analysing tree heights, and in many countries, national airborne laser scanning (ALS) surveys have been conducted either only once or at specific, longer intervals, whereas aerial surveys are more often arranged in cycles with shorter intervals. In this study, we reviewed all freely available national airborne remote sensing data describing three-dimensional forest structures in Slovenia and compared them with traditional field measurements in an area dominated by uneven-aged forests. The comparison of ALS and digital aerial photogrammetry (DAP) data revealed that freely available national ALS data provide better estimates of dominant forest heights, vertical structural diversity, and their changes compared to cyclic DAP data, but they are still useful due to their temporally dense data. Up-to-date data are very important for forest management and the study of forest resilience and resistance to disturbance. Based on field measurements (2013 and 2023) and all remote sensing data, dominant and maximum heights are statistically significantly higher in uneven-aged forests than in mature, even-aged forests. Canopy height diversity (CHD) information, derived from lidar ALS and DAP data, has also proven to be suitable for distinguishing between even-aged and uneven-aged forests. The CHDALS 2023 was 1.64, and the CHDCAS 2022 was 1.38 in uneven-aged stands, which were statistically significantly higher than in even-aged forest stands.
Why it matches plant phenotyping methodsALSと航空写真測量による樹高・森林垂直構造・樹冠高多様性の推定を現地測定と比較検証しており、植物（森林）の形態形質測定が研究の中心です。
abstractThe comparison of ALS and digital aerial photogrammetry (DAP) data revealed that freely available national ALS data provide better estimates of dominant forest heights, vertical structural diversity, and their changes compared to cyclic DAP data
Reproduction assets foundThe paper's own field measurements (2013, 2023) and derived analysis data are not publicly deposited; the Data Availability Statement says raw data are available only upon reasonable request from the corresponding author. The freely available national ALS/DAP source data are public via the Slovenian national remote-snsDataset · publicmote Sens. Environ. 2018, 208, 1–14. [CrossRef]
14. Goodbody, T.R.H.; Coops, N.C.; White, J.C. Digital Aerial Photogrammetry for Updating Area-Based Forest Inventories: A Review
of Opportunities, Challenges, and Future Directions. Curr. For. Rep. 2019, 5, 55–75. [CrossRef]
15. GURS. Daljinsko zaznavanje. 2024. Available online: https://www.e-prostor.gov.si/podrocja/drzavni-topografski-sistem/daljinsko-zaznavanje/ (accessed on 13 November 2024).
16. Haala, N. The landscape image matching algorithms. In Proceedings of the 54th Photogrammetric Week, Stuttgart, Germany,
9–13 September 2013; pp. 271–284.
17. Triglav Čekada, M.; Bric, V. Končan je projekt Laserskega skeniranja Slovenije. Geod. VOpen asset ↗GURSpdf-raw-page:14 lines:1-48Code / dataset availability confirmedCrossref · OpenAlex · checked 15 Sept 2026
Cover crops (CC) immobilize mineral soil N in their biomass, preventing N losses during crop rotation intervals. As the CC biomass is incorporated into the soil and decomposes, N is released for the following main crop. The efficiency of CC N uptake and release depends on CC quantity and quality, which can be enhanced in mixtures. Traditional N uptake measurements are labour-intensive and limited in capturing spatial variability. We calibrated relationships between traditional measurements and multispectral data from an Unmanned Aerial Vehicle (UAV) to quantify CC traits with minimal disturbance and high spatial resolution in both monocultures and mixtures. This innovative approach combined vegetation indices, textural features, and a photogrammetry-derived canopy surface model to predict CC traits. Linear models were trained for biomass, N uptake, and C:N predictions, while a K-Nearest-Neighbour model was trained for N concentration. When evaluated on the test set, the calibrated remote sensing models accurately predicted CC aboveground biomass (R 2 : 0.71, RMSE: 287.1 kg/ha, NRMSE: 11.74 %), N concentration (R 2 : 0.80, RMSE: 1.77 gN /kg, NRMSE: 6.96 %), N uptake (R 2 : 0.56, RMSE: 9.38 kgN /ha, NRMSE: 15.08 %), and C:N ratio (R 2 : 0.62, RMSE: 1.86, NRMSE: 10.98 %). The field experiment included monocultures, bi-, and tri-species mixtures of common vetch ( Vicia sativa ), black oat ( Avena strigosa ), and fodder radish ( Raphanus sativus ). N uptake was similar between treatments, yet the CC species differed in strategies, producing high biomass with low N concentration or vice versa. This study provides a basis for spatially predicting key CC traits using UAV optical data.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像、テクスチャ特徴、フォトグラメトリ由来モデルを用いて、植物のバイオマス、窒素濃度、窒素吸収量、C:N比を推定する手法を開発・検証しており、表現型取得が研究の中心である。
abstractWe calibrated relationships between traditional measurements and multispectral data from an Unmanned Aerial Vehicle (UAV) to quantify CC traits with minimal disturbance and high spatial resolution in both monocultures and mixtures.
Reproduction assets foundThe paper's Data availability statement explicitly states the authors' R code for image processing, model training, and figure production is publicly available on the authors' WUR GitLab repository (uav4covercroptraits). No phenotype dataset or image deposit is stated separately.Code · publictal for the
UAV data acquisition.
Supplementary materials
Supplementary material associated with this article can be found, in
the online version, at doi:10.1016/j.atech.2024.100608.
Data availability
The R code generated during this study to process the images, train
the models and produce the figures, is publicly available at https://git.wur.nl/dall002/uav4covercroptraits.References
[1] C. Aita, S.J. Giacomini, Crop residue decomposition and nitrogen release in singleOpen asset ↗git.wur.nl/dall002/uav4covercroptraitspdf-raw-page:10 lines:1-89Code / dataset availability confirmedOpenAlex · Crossref · checked 8 Sept 2026
Computer vision techniques offer promising tools for disease detection in orchards and can enable effective phenotyping for the selection of resistant cultivars in breeding programmes and research. In this study, a digital phenotyping system for disease detection and monitoring was developed using drones, object detection and photogrammetry, focusing on European pear rust (Gymnosporangium sabinae) as a model pathogen. High-resolution RGB images from ten low-altitude drone flights were collected in 2021, 2022 and 2023. A total of 16,251 annotations of leaves with pear rust symptoms were created on 584 images using the Computer Vision Annotation Tool (CVAT). The YOLO algorithm was used for the automatic detection of symptoms. A novel photogrammetric approach using Agisoft’s Metashape Professional software ensured the accurate localisation of symptoms. The geographic information system software QGIS calculated the infestation intensity per tree based on the canopy areas. This drone-based phenotyping system shows promising results and could considerably simplify the tasks involved in fruit breeding research.
Why it matches plant phenotyping methodsドローン画像、物体検出、写真測量を統合し、ナシ樹のさび病症状を検出・局在化して樹体ごとの感染強度を推定するデジタル表現型解析システムの開発が中心である。
abstracta digital phenotyping system for disease detection and monitoring was developed using drones, object detection and photogrammetry
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the annotated UAV image dataset on Mendeley Data and the trained model, detection workflow, and Metashape loading script on figshare, both with public URLs matching allowed entries.Dataset · publicsource repository Mendeley Data (https://data.mendeley.com/datasets/44kjgc4gkc/1, accessed on 8Open asset ↗Mendeley Data · 44kjgc4gkc/1pdf-page:15 lines:1-67Code · publicThe model, the detection workflow with instructions and the script for
loading the detections into Agisoft’s Metashape are available in the open-source figshare repository
(https://doi.org/10.6084/m9.figshare.27225312.v2, accessed on 28 October 2024).Open asset ↗figshare · 10.6084/m9.figshare.27225312.v2pdf-page:15 lines:1-67Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Studies on the phenotypic traits and their associations in Chinese cabbage lack precise and objective digital evaluation metrics. Traditional assessment methods often rely on subjective evaluations and experience, compromising accuracy and reliability. This study develops an innovative, comprehensive trait evaluation method based on 3D point cloud technology, with the aim of enhancing the precision, reliability, and standardization of the comprehensive phenotypic traits of Chinese cabbage. By using multi-view image sequences and structure-from-motion algorithms, 3D point clouds of 50 plants from each of the 17 Chinese cabbage varieties were reconstructed. Color-based region growing and 3D convex hull techniques were employed to measure 30 agronomic traits. Comparisons between 3D point cloud-based measurements of the plant spread, plant height, leaf area, and leaf ball volume and traditional methods yielded R2 values greater than 0.97, with root mean square errors of 1.27 cm, 1.16 cm, 839.77 cm3, and 59.15 cm2, respectively. Based on the plant spread and plant height, a linear regression prediction of Chinese cabbage weights was conducted, yielding an R2 value of 0.76. Integrated optimization algorithms were used to test the parameters, reducing the measurement time from 55 min when using traditional methods to 3.2 min. Furthermore, in-depth analyses including variation, correlation, principal component analysis, and clustering analyses were conducted. Variation analysis revealed significant trait variability, with correlation analysis indicating 21 pairs of traits with highly significant positive correlations and 2 pairs with highly significant negative correlations. The top six principal components accounted for 90% of the total variance. Using the elbow method, k-means clustering determined that the optimal number of clusters was four, thus classifying the 17 cabbage varieties into four distinct groups. This study provides new theoretical and methodological insights for exploring phenotypic trait associations in Chinese cabbage and facilitates the breeding and identification of high-quality varieties. Compared with traditional methods, this system provides significant advantages in terms of accuracy, speed, and comprehensiveness, with its low cost and ease of use making it an ideal replacement for manual methods, being particularly suited for large-scale monitoring and high-throughput phenotyping.
Why it matches plant phenotyping methods中国白菜の表現型を3D点群から抽出する測定法を開発し、従来法との精度比較・検証および高速化を行っており、植物表現型測定が研究の中心である。
abstractThis study develops an innovative, comprehensive trait evaluation method based on 3D point cloud technology
Reproduction assets foundThe paper's phenotyping analysis code is explicitly deposited on a public GitHub repository with an authors' URL. The phenotype/trait measurement data themselves are only available upon request, so they do not qualify as a public asset.Code · publicapproach significantly streamlines the process, saving time and
enhancing efficiency by automating tasks which previously required extensive manual ef-
fort, thereby ensuring a more systematic and reliable method of phenotypic information
detection. The code used in this study can be accessed at the following GitHub repository:
https://github.com/chongchong123123/code (accessed on 18 October 2024).
2.4. Accuracy Analysis of Agronomic Parameter Measurements
In the course of agronomic trait measurement research, we utilized point cloud tech-
nology to measure key agronomic traits, including the plant height, plant spread, various
leaf dimensions (leaf length and leaf width), the width and thicOpen asset ↗chongchong123123/codepdf-raw-page:8 lines:1-62Code / dataset availability confirmedOpenAlex · checked 13 Sept 2026
Three raster-based (RB) and one point cloud-based (PCB) algorithms were tested to segment individual Aleppo pine trees and extract their tree height (H) and crown diameter (CD) using two types of point clouds generated from two different techniques: (1) Low-Density (≈1.5 points/m2) Airborne Laser Scanning (LD-ALS) and (2) photogrammetry based on high-resolution unmanned aerial vehicle (UAV) images. Through intensive experiments, it was concluded that the tested RB algorithms performed best in the case of UAV point clouds (F1-score > 80.57%, H Pearson’s r > 0.97, and CD Pearson´s r > 0.73), while the PCB algorithm yielded the best results when working with LD-ALS point clouds (F1-score = 89.51%, H Pearson´s r = 0.94, and CD Pearson´s r = 0.57). The best set of algorithm parameters was applied to all plots, i.e., it was not optimized for each plot, in order to develop an automatic pipeline for mapping large areas of Mediterranean forests. In this case, tree detection and height estimation showed good results for both UAV and LD-ALS (F1-score > 85% and >76%, and H Pearson´s r > 0.96 and >0.93, respectively). However, very poor results were found when estimating crown diameter (CD Pearson´s r around 0.20 for both approaches).
Why it matches plant phenotyping methods個体樹のセグメンテーション手法を比較・検証し、樹高と樹冠径という植物形質を点群から推定する自動パイプラインを評価しており、フェノタイピング手法が中心です。
titleBenchmarking of Individual Tree Segmentation Methods in Mediterranean Forest Based on Point Clouds from Unmanned Aerial Vehicle Imagery and Low-Density Airborne Laser Scanning
Reproduction assets foundThe paper's Data Availability Statement states that the data presented in the study (the UAV/LD-ALS point clouds, reference tree measurements, and segmentation outputs underlying the phenotyping analysis) are openly available on Zenodo under DOI 10.5281/zenodo.10518411. This is a paper-specific, publicly actionablephenDataset · publicData Availability Statement: The data presented in this study are openly available in zenodo at
10.5281/zenodo.10518411.Open asset ↗zenodo · 10.5281/zenodo.10518411pdf-page:25 lines:1-56Code / dataset availability confirmedCrossref · checked 13 Sept 2026
Unmanaged forest ecosystems play a critical role in addressing the ongoing climate and biodiversity crises. As there is no commercial interest in monitoring the health and development of such inaccessible habitats, low-cost assessment approaches are needed. We used a method combining RGB imagery acquired using an Unmanned Aerial Vehicle (UAV), Sentinel-2 data, and field surveys to determine the carbon stock of an unmanaged forest in the UNESCO World Heritage Site wilderness area Dürrenstein-Lassingtal in Austria. The entry-level consumer drone (DJI Mavic Mini) and freely available Sentinel-2 multispectral datasets were used for the evaluation. We merged the Sentinel-2 derived vegetation index NDVI with aerial photogrammetry data and used an orthomosaic and a Digital Surface Model (DSM) to map the extent of woodland in the study area. The Random Forest (RF) machine learning (ML) algorithm was used to classify land cover. Based on the acquired field data, the average carbon stock per hectare of forest was determined to be 371.423 ± 51.106 t of CO2 and applied to the ML-generated class Forest. An overall accuracy of 80.8% with a Cohen’s kappa value of 0.74 was achieved for the land cover classification, while the carbon stock of the living above-ground biomass (AGB) was estimated with an accuracy within 5.9% of field measurements. The proposed approach demonstrated that the combination of low-cost remote sensing data and field work can predict above-ground biomass with high accuracy. The results and the estimation error distribution highlight the importance of accurate field data.
Why it matches plant phenotyping methodsUAV・衛星リモートセンシングと機械学習により森林の地上部バイオマス（炭素蓄積量）を推定し、現地測定と精度検証しているため、植物群落レベルの形質推定手法が中心です。
abstractWe used a method combining RGB imagery acquired using an Unmanned Aerial Vehicle (UAV), Sentinel-2 data, and field surveys to determine the carbon stock of an unmanaged forest
Reproduction assets foundThe paper's Data Availability Statement points to an openly available Zenodo deposit containing the original study data (field carbon stock measurements, UAV-derived datasets, and Sentinel-2 based analysis inputs). No separate author analysis code or trained model repository is mentioned.Dataset · publicData Availability Statement: The original data presented in the study are openly available here:
https://doi.org/10.5281/zenodo.11657557, accessed on 5 June 2024.Open asset ↗zenodo · 10.5281/zenodo.11657557pdf-page:17 lines:1-58Code / dataset availability confirmedOpenAlex · checked 13 Sept 2026
Uncrewed aerial system (UAS) structure from motion (SfM) monitoring strategies for individual trees has rapidly expanded in the early 21st century. It has become common for studies to report accuracies for individual tree heights and DBH, along with stand density metrics. This study evaluates individual tree detection and stand basal area accuracy and precision in five ponderosa pine sites against the range of SfM parameters in the Agisoft Metashape, Pix4DMapper, and OpenDroneMap algorithms. The study is designed to frame UAS-SfM individual tree monitoring accuracy in the context of data processing and storage demands as a function of SfM algorithm parameter levels. Results show that when SfM algorithms are properly tuned, differences between software types are negligible, with Metashape providing a median F-score improvement over OpenDroneMap of 0.02 and PIX4DMapper of 0.06. However, tree extraction performance varied greatly across algorithm parameters, with the greatest extraction rates typically coming from parameters causing increased density in dense point clouds and minimal point cloud filtering. Transferring UAS-SfM forest monitoring into management will require tradeoffs between accuracy and efficiency. Our analysis shows that a one-step reduction in dense point cloud quality saves 77–86% in point cloud processing time without decreasing tree extraction (F-score) or basal area precision using Metashape and PIX4DMapper but the same parameter change for OpenDroneMap caused a ~5% loss in precision. Providing reproducible processing strategies is a vital step in successfully transferring these technologies into usage as management tools.
Why it matches plant phenotyping methodsUAS-SfMの処理パラメータと複数ソフトウェアを比較し、個体樹の抽出精度、樹高・DBH、林分断面積を評価する技術検証が中心である。
abstractThis study evaluates individual tree detection and stand basal area accuracy and precision in five ponderosa pine sites against the range of SfM parameters in the Agisoft Metashape, Pix4DMapper, and OpenDroneMap algorithms.
Reproduction assets foundThe paper's Data Availability Statement explicitly publishes the project's data and analysis source code to a public GitHub repository, which qualifies as a paper-specific public code asset. No separate phenotype dataset deposit is stated beyond this repository.Code · publicData Availability Statement: Data and analysis source code for this project has been published to the
public domain at: https://github.com/georgewoolsey/uas_sfm_tree_detection.Open asset ↗georgewoolsey/uas_sfm_tree_detectionpdf-page:19 lines:1-56Code / dataset availability confirmedarXiv · OpenAlex · checked 13 Sept 2026
Creation of new annotated public datasets is crucial in helping advances in 3D computer vision and machine learning meet their full potential for automatic interpretation of 3D plant models. Despite the proliferation of deep neural network architectures for segmentation and phenotyping of 3D plant models in the last decade, the amount of data, and diversity in terms of species and data acquisition modalities are far from sufficient for evaluation of such tools for their generalization ability. To contribute to closing this gap, we introduce PLANesT-3D; a new annotated dataset of 3D color point clouds of plants. PLANesT-3D is composed of 34 point cloud models representing 34 real plants from three different plant species: \textit{Capsicum annuum}, \textit{Rosa kordana}, and \textit{Ribes rubrum}. Both semantic labels in terms of "leaf" and "stem", and organ instance labels were manually annotated for the full point clouds. PLANesT-3D introduces diversity to existing datasets by adding point clouds of two new species and providing 3D data acquired with the low-cost SfM/MVS technique as opposed to laser scanning or expensive setups. Point clouds reconstructed with SfM/MVS modality exhibit challenges such as missing data, variable density, and illumination variations. As an additional contribution, SP-LSCnet, a novel semantic segmentation method that is a combination of unsupervised superpoint extraction and a 3D point-based deep learning approach is introduced and evaluated on the new dataset. The advantages of SP-LSCnet over other deep learning methods are its modular structure and increased interpretability. Two existing deep neural network architectures, PointNet++ and RoseSegNet, were also tested on the point clouds of PLANesT-3D for semantic segmentation.
Why it matches plant phenotyping methods3D植物点群の注釈付きデータセットを構築し、植物器官のセマンティック・インスタンス分割手法を開発・評価しており、植物フェノタイピング手法が中心である。
abstractwe introduce PLANesT-3D; a new annotated dataset of 3D color point clouds of plants.
Reproduction assets foundThe paper introduces PLANesT-3D, an annotated 3D plant point cloud dataset, and SP-LSCnet segmentation code, both explicitly stated as publicly available at the authors' Aperta record and GitHub repository.Dataset · publicThe PLANesT-3D dataset is publicly available at https://aperta.ulakbim.gov.tr/record/286354 and https://github.com/visionlab-ogu/PLANesT-3D/tree/main/dataOpen asset ↗aperta.ulakbim.gov.tr · 286354lines:83-145Dataset · publicThe 2D color images for all the 34 plants together with their estimated camera poses and parameters are also open to the public to provide input data for recent 3D reconstruction techniques 3 3
3
The data is available at https://github.com/visionlab-ogu/PLANesT-3D/tree/main/data .Open asset ↗github.com/visionlab-ogu/PLANesT-3Dlines:494-505Code · publicThe code for SP-LSCnet is available at https://github.com/visionlab-ogu/PLANesT-3DOpen asset ↗github.com/visionlab-ogu/PLANesT-3Dlines:146-154Code / dataset availability confirmedEurope PMC · OpenAlex · checked 14 Sept 2026
In recent years, illegal felling of and damage to the incense tree Aquilaria sinensis (Lour.) Spreng. have been reported in Hong Kong. Their native populations are under increasingly severe threat. Therefore, the development of a standard and efficient method to classify and document wounds on vulnerable trees is urgently needed for conservation purposes. In this study, photogrammetry was used to document wounds in A. sinensis through 3D modeling. A total of 752 wound records from 484 individual A. sinensis trees from Hong Kong were included to establish a new wound classification system. Our major findings include a novel standardized procedure for photogrammetric documentation and a wound classification system. The results of this study will facilitate A. sinensis conservation, by enhancing wound documentation and information transfer to law enforcement and education.
Why it matches plant phenotyping methods樹木の損傷状態を対象に、写真測量による3D記録と分類手順を開発しており、植物状態の取得・抽出が研究の中心である。
abstractphotogrammetry was used to document wounds in A. sinensis through 3D modeling.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicAll the 3D models can be accessed on the “Virtual Carpological Herbarium” in the Shiu‐Ying Hu Herbarium ( https://syhuherbarium.sls.cuhk.edu.hk/collections/3d‐digitized‐tag/wound/ ; Username: syhuherbarium; Password: @CUHK).Open asset ↗lines:403-434Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Published23 May 2024Forestry An International Journal of Forest ResearchCited by 45 · OpenAlex ↗
Abstract Accurate and efficient forest inventories are essential for effective forest management and conservation. The advent of ground-based remote sensing has revolutionized the data acquisition process, enabling detailed and precise 3D measurements of forested areas. Several algorithms and methods have been developed in the last years to automatically derive tree metrics from such terrestrial/ground-based point clouds. However, few attempts have been made to make these automatic tree metrics algorithms accessible to wider audiences by producing software solutions that implement these methods. To fill this major gap, we have developed 3DFin, a novel free software program designed for user-friendly, automatic forest inventories using ground-based point clouds. 3DFin empowers users to automatically compute key forest inventory parameters, including tree Total Height, Diameter at Breast Height (DBH), and tree location. To enhance its user-friendliness, the program is open-access, cross-platform, and available as a plugin in CloudCompare and QGIS as well as a standalone in Windows. 3DFin capabilities have been tested with Terrestrial Laser Scanning, Mobile Laser Scanning, and terrestrial photogrammetric point clouds from public repositories across different forest conditions, achieving nearly full completeness and correctness in tree mapping and highly accurate DBH estimations (root mean squared error <2 cm, bias <1 cm) in most scenarios. In these tests, 3DFin demonstrated remarkable efficiency, with processing times ranging from 2 to 7 min per plot. The software is freely available at: https://github.com/3DFin/3DFin.
Why it matches plant phenotyping methods森林個体の樹高・胸高直径などの植物形質を点群から自動抽出するソフトウェアの開発と技術検証が中心であり、植物フェノタイピング手法に該当する。
abstractwe have developed 3DFin, a novel free software program designed for user-friendly, automatic forest inventories using ground-based point clouds.
Reproduction assets foundThe paper's DBH/tree-metric analysis was run on the public SilviLaser 2021 Benchmark Dataset (TU Wien Research Data, DOI 10.48436/afdjq-ce434), and the authors' analysis software 3DFin is publicly available (GitHub releases, CloudCompare plugin, PyPI). Both are paper-specific, public, and actionable.Dataset · publicoptimized presets that facilitate the
effective application of 3DFin in various forest inventory scenarios. Data availability
Another direction for the research linked to 3DFin is the devel-
The data underlying this article are available in TU Wien Research
opment of a complementary software tool focused on the seman-
Data, at https://doi.org/10.48436/afdjq-ce434.
tic segmentation of point clouds into different vegetation struc-
tures. This tool will build upon the capabilities of 3DFin, employing
advanced deep learning techniques to distinguish between var- References
ious types of vegetation elements within a forested scene. The
Bentley JL. Multidimensional binary search trees used foOpen asset ↗10.48436/afdjq-ce434pdf-layout-page:17 lines:1-66Code · publichines as a plugin
in CloudCompare via the CloudCompare PythonRuntime (Montaigu,
2024). The latest alpha-version of CloudCompare (version 2.13.1,
March 2024) including the 3DFin plugin can be downloaded from
the official site https://www.danielgm.net/cc/release/. 3DFin is
also downloadable on Windows as a standalone program from
https://github.com/3DFin/3DFin/releases. Additionally, 3DFin
and its dependencies may be installed and launched on any
OS (Windows, Linux and macOS) as a Python package, available
in PyPI. A script entry point is also installed by pip in Python
installation’s bin | script directory. This enables launching 3DFin’s
GUI from the command line, which avoids the need to wrOpen asset ↗pdf-raw-page:7 lines:1-72Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Published23 May 2024International Journal of Applied Earth Observation and GeoinformationCited by 9 · OpenAlex ↗
In smallholder areas, the abandonment of orchards is a recent phenomenon with socioeconomic and environmental consequences. Biomass estimation and monitoring of these areas is essential to analyze their influence on the CO2 balance and to quantify carbon pools. In the current context of energy supply uncertainties and considering the demanding use of alternative energy sources, the quantification of fruit tree biomass in abandoned areas is a question of great interest. In this study, the above biomass of abandoned orange trees was estimated using tree parameters calculated from 3D points derived from images captured by a UAV and applying the Structure from Motion (SfM) technique. From these data, a canopy height model was calculated and used to apply a developed crown contour detection algorithm. Using this information and 3D points, the tree parameters crown area, crown diameter, crown length, maximum tree height, minimum tree height, mean and standard deviation of crown point heights were calculated for a set of 36 felled and weighted orange trees. Stepwise regression was used to estimate the above biomass values. All previously reported variables were included. The crown area parameter produced the most accurate model with R2, RMSE and RMSE % values of 0.85, 10.165 kg and 19.56 %, respectively. These results demonstrate the potential of UAV-SfM-derived 3D point clouds to estimate the above-ground biomass of abandoned fruit trees, relevant information for environmental analysis and biofuel energy production.
Why it matches plant phenotyping methodsUAV-SfMによる3D画像から樹冠形状・樹高などの植物形質を抽出し、樹冠検出アルゴリズムと回帰モデルで個体バイオマスを推定する方法が中心である。
abstractthe above biomass of abandoned orange trees was estimated using tree parameters calculated from 3D points derived from images captured by a UAV and applying the Structure from Motion (SfM) technique
Reproduction assets foundThe article's 'Research data' section states that the point cloud, crown delineation source code, reference data, and tree parameters are publicly available in a Mendeley Data repository (doi:10.17632/j3k2mctbnv.1). This is a paper-specific, public, actionable asset. Note: the Mendeley DOI itself is not in the allowed-Dataset · publicThe dataset containing point cloud, source code for crown delinea
tion, reference data, and parameters of abandoned orange groves, is
available for download from Mendeley data repository doi: 10
.17632/j3k2mctbnv.1.Open asset ↗Mendeley data repositorypdf-raw-page:9 lines:1-76Code / dataset availability confirmedEurope PMC · checked 13 Sept 2026
Field / plotPhotogrammetry / SfM / MVSLiDAR / point cloudStem / branch2D/3D reconstructionArchitecture / morphology / geometry
Conifer shoots exhibit intricate geometries at an exceptionally detailed spatial scale. Describing the complete structure of a conifer shoot, which contributes to a radiation scattering pattern, has been difficult, and the previous respective components of radiative transfer models for conifer stands were rather coarse. This paper presents a dataset aimed at models and applications requiring detailed 3D representations of needle shoots. The data collection was conducted in the Järvselja RAdiation transfer Model Intercomparison (RAMI) pine stand in Estonia. The dataset includes 3-dimensional surface information on 10 shoots of two conifer species present in the stand (5 shoots per species) - Scots pine ( Pinus sylvestris L.) and Norway spruce ( Picea abies L. Karst. ). The samples were collected on 26th July 2022, and subsequently blue light 3D photogrammetry scanning technique was used to obtain their high-resolution 3D point cloud representations. For each of these samples, the dataset comprises of a photo of the sampled shoot and its obtained 3-dimensional surface reconstruction. Scanned shoots may replace previous, artificially generated models and contribute to the more realistic representation of 3D forest representations and, consequently, more accurate estimates of related parameters and processes by radiative transfer models.
Why it matches plant phenotyping methods針葉樹シュートの3次元形状を高解像度スキャンで取得した再利用可能なデータセットであり、植物形態の計測・表現が中心。
abstractThis paper presents a dataset aimed at models and applications requiring detailed 3D representations of needle shoots.
Reproduction assets foundThe paper describes a public Mendeley Data repository containing the paper's own 3D surface geometry (.stl) models and photos (.jpg) of 10 scanned conifer shoots, directly reproducing the paper's phenotyping measurements.Dataset · publicRepository name: Mendeley
Data identification number: 10.17632/rs3f6trdvw.1
Direct URL to data: https://data.mendeley.com/datasets/rs3f6trdvw/1Open asset ↗Mendeley · 10.17632/rs3f6trdvw.1lines:1-53Code / dataset availability confirmedEurope PMC · OpenAlex · checked 15 Sept 2026
Leaves, crucial for plant physiology, exhibit various morphological traits that meet diverse functional needs. Traditional leaf morphology quantification, largely 2-dimensional (2D), has not fully captured the 3-dimensional (3D) aspects of leaf function. Despite improvements in 3D data acquisition, accurately depicting leaf morphologies, particularly at the edges, is difficult. This study proposes a method for 3D leaf edge reconstruction, combining 2D image segmentation with curve-based 3D reconstruction. Utilizing deep-learning-based instance segmentation for 2D edge detection, structure from motion for estimation of camera positions and orientations, leaf correspondence identification for matching leaves among images, and curve-based 3D reconstruction for estimating 3D curve fragments, the method assembles 3D curve fragments into a leaf edge model through B-spline curve fitting. The method's performances were evaluated on both virtual and actual leaves, and the results indicated that small leaves and high camera noise pose greater challenges to reconstruction. We developed guidelines for setting a reliability threshold for curve fragments, considering factors occlusion, leaf size, the number of images, and camera error; the number of images had a lesser impact on this threshold compared to others. The method was effective for lobed leaves and leaves with fewer than 4 holes. However, challenges still existed when dealing with morphologies exhibiting highly local variations, such as serrations. This nondestructive approach to 3D leaf edge reconstruction marks an advancement in the quantitative analysis of plant morphology. It is a promising way to capture whole-plant architecture by combining 2D and 3D phenotyping approaches adapted to the target anatomical structures.
Why it matches plant phenotyping methods植物の葉縁形態を3D再構築して定量化する手法を開発し、仮想葉と実葉で性能評価・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 proposes a method for 3D leaf edge reconstruction, combining 2D image segmentation with curve-based 3D reconstruction.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the datasets and analysis code for this 3D leaf edge reconstruction study in a public GitHub repository (MorphometricsGroup/Murata-2024), and the virtual-leaf simulation inputs (Sketchfab 3D leaf models) are publicly available. Generic libraries (Detectron2, 3Dataset · publicto S4
Data Availability Statement
The datasets used and/or analyzed during the current study are available in the repositories on Zenodo (10.5281/zenodo.10836254, 10.5281/zenodo.10836258, 10.5281/zenodo.10836260, 10.5281/zenodo.10065546, 10.5281/zenodo.10828962, 10.5281/zenodo.10121073, and 10.5281/zenodo.10829007) and GitHub ( https://github.com/MorphometricsGroup/Murata-2024 ).Open asset ↗MorphometricsGroup/Murata-2024lines:311-320Code / dataset availability confirmedOpenAlex · checked 13 Sept 2026
Forests are a vital source of food, fuel, and medicine and play a crucial role in climate change mitigation. Strategic and policy decisions on forest management and conservation require accurate and up-to-date information on available forest resources. Forest inventory data such as tree parameters, heights, and crown diameters must be collected and analysed to monitor forests effectively. Traditional manual techniques are slow and labour-intensive, requiring additional personnel, while existing non-contact methods are costly, computationally intensive, or less accurate. Kenya plans to increase its forest cover to 30% by 2032 and establish a national forest monitoring system. Building capacity in forest monitoring through innovative field data collection technologies is encouraged to match the pace of increase in forest cover. This study explored the applicability of low-cost, non-contact tree inventory based on stereoscopic photogrammetry in a recently reforested stand in Kieni Forest, Kenya. A custom-built stereo camera was used to capture images of 251 trees in the study area from which the tree heights and crown diameters were successfully extracted quickly and with high accuracy. The results imply that stereoscopic photogrammetry is an accurate and reliable method that can support the national forest monitoring system and REDD+ implementation.
Why it matches plant phenotyping methodsステレオ写真測量を用いて樹高と樹冠径を非接触で抽出し、精度と信頼性を評価しているため、植物形質取得手法が研究の中心です。
abstractThis study explored the applicability of low-cost, non-contact tree inventory based on stereoscopic photogrammetry
Reproduction assets foundThe paper's Data Availability Statement explicitly states that the supporting data and software code for the stereoscopic photogrammetry tree inventory (tree height, crown diameter, DBH extraction from stereo images of 251 trees in Kieni Forest) are publicly available on the authors' GitHub repository DeKUT-DSAIL/TreeVCode · publicData Availability Statement: The data that support the findings of this study, as well as the software
code, are publicly available on GitHub: https://github.com/DeKUT-DSAIL/TreeVision (accessed on
11 August 2023).Open asset ↗DeKUT-DSAIL/TreeVisionpdf-page:11 lines:1-61Code / dataset availability confirmedOpenAlex · Europe PMC · checked 7 Sept 2026
Abstract It is of great significance to study the plant morphological structure for improving crop yield and achieving efficient use of resources. Three dimensional (3D) information can more accurately describe the morphological and structural characteristics of crop plants. Automatic acquisition of 3D information is one of the key steps in plant morphological structure research. Taking wheat as the research object, we propose a point cloud data-driven 3D reconstruction method that achieves 3D structure reconstruction and plant morphology parameterization at the phytomer scale. Specifically, we use the MVS-Pheno platform to reconstruct the point cloud of wheat plants and segment organs through the deep learning algorithm. On this basis, we automatically reconstructed the 3D structure of leaves and tillers and extracted the morphological parameters of wheat. The results show that the semantic segmentation accuracy of organs is 95.2%, and the instance segmentation accuracy AP50 is 0.665. The R2 values for extracted leaf length, leaf width, leaf attachment height, stem leaf angle, tiller length, and spike length were 0.97, 0.80, 1.00, 0.95, 0.99, and 0.95, respectively. This method can significantly improve the accuracy and efficiency of 3D morphological analysis of wheat plants, providing strong technical support for research in fields such as agricultural production optimization and genetic breeding.
Why it matches plant phenotyping methods小麦の3D形態情報をMVS-Phenoと点群・深層学習で取得し、器官分割、形態パラメータ抽出、精度評価を行う手法研究であり、フェノタイピング手法が中心です。
abstractwe propose a point cloud data-driven 3D reconstruction method that achieves 3D structure reconstruction and plant morphology parameterization at the phytomer scale.
Reproduction assets foundThe paper's Data Availability statement explicitly states that the data and code used in the article are publicly available on GitHub at the authors' repository, which matches an allowed URL. This qualifies as a paper-specific public asset covering the wheat 3D reconstruction/phenotyping analysis.Code · publicThe data and code used in this article are available on GitHub, at https://github.com/lwlwr99/reconstruct-the-3D-morphological-structure-of-wheatOpen asset ↗lwlwr99/reconstruct-the-3D-morphological-structure-of-wheatlines:280-436Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Prescribed burning and pyric herbivory play pivotal roles in mitigating wildfire risks, underscoring the imperative of consistent biomass monitoring for assessing fuel load reductions. Drone-derived surface models promise uninterrupted biomass surveillance but require complex photogrammetric processing. In a Mediterranean mountain shrubland burning experiment, we refined a Structure from Motion (SfM) and Multi-View Stereopsis (MVS) workflow to diminish biases in 3D modeling and RGB drone imagery-based surface reconstructions. Given the multitude of SfM-MVS processing alternatives, stringent quality oversight becomes paramount. We executed the following steps: (i) calculated Root Mean Square Error (RMSE) between Global Navigation Satellite System (GNSS) checkpoints to assess SfM sparse cloud optimization during georeferencing; (ii) evaluated elevation accuracy by comparing the Mean Absolute Error (MAE) of six surface and thirty terrain clouds against GNSS readings and known box dimensions; and (iii) complemented a dense cloud quality assessment with density metrics. Balancing overall accuracy and density, we selected surface and terrain cloud versions for high-resolution (2 cm pixel size) and accurate (DSM, MAE = 57 mm; DTM, MAE = 48 mm) Digital Elevation Model (DEM) generation. These DEMs, along with exceptional height and volume models (height, MAE = 12 mm; volume, MAE = 909.20 cm3) segmented by reference box true surface area, substantially contribute to burn impact assessment and vegetation monitoring in fire management systems.
Why it matches plant phenotyping methodsドローン画像のSfM-MVS処理を改良・精度検証し、植生の高さ・体積・バイオマス監視に用いる手法が研究の中心である。
abstractwe refined a Structure from Motion (SfM) and Multi-View Stereopsis (MVS) workflow to diminish biases in 3D modeling and RGB drone imagery-based surface reconstructions.
Reproduction assets foundThe paper's SfM sparse-cloud optimization analysis was implemented as a Python module in the authors' public MetashapeTools repository (co-author Marvin Ludwig), explicitly linked in the text. The bl_gimbal repository is only a gimbal hardware controller, not phenotyping analysis, and the Data Availability Statement isCode · publicencing process of the sparse cloud [50]. This approach focuses on minimizing the error of
georeferencing check points within the sparse cloud by identifying the optimal filter pa-
rameters. Consequently, only tie points with low reprojection errors are used. This appli-
cation is available as a Python module for MetashapeTools (https://github.com/en-vima/MetashapeTools/, accessed on 30 August 2023).
An orthomosaic is a detailed and geometrically accurate image of an area, composed
of multiple photos that have been orthorectified. Within this framework, once the
Figure 4. (a) Illustrates the optimized workflow for the Metashape Structure from Motion (SfM)
(Ludwig et al, 2020 [50]). (b) RepresOpen asset ↗en-vima/MetashapeToolspdf-raw-page:7 lines:1-31Code / dataset availability confirmedEurope PMC · OpenAlex · checked 13 Sept 2026
Premise We recognized the need for a customized imaging protocol for plant specimens at the time of collection for the purpose of three-dimensional (3D) modeling, as well as the lack of a broadly applicable photogrammetry protocol that encompasses the heterogeneity of plant specimen geometries and the challenges introduced by processes such as wilting. Methods and results We developed an equipment list and set of detailed protocols describing how to capture images of plant specimens in the field prior to their deformation (e.g., with pressing) and how to produce a 3D model from the image sets in Agisoft Metashape Professional. Conclusions The equipment list and protocols represent a foundation on which additional improvements can be made for specimen geometries outside of the range of the six types considered, and an easy entry into photogrammetry for those who have not previously used it.
Why it matches plant phenotyping methods植物標本の3D形状を取得するための野外撮像およびフォトグラメトリ手順を開発した研究であり、画像取得・形状抽出手法が中心です。
abstractWe developed an equipment list and set of detailed protocols describing how to capture images of plant specimens in the field prior to their deformation (e.g., with pressing) and how to produce a 3D model from the image sets in Agisoft Metashape Professional.
Reproduction assets foundThe paper's field-captured plant specimen image sets and resulting 3D meshes are publicly deposited on MorphoSource (project 000494239), and the final 3D models for the five viable subject types are also publicly viewable on Sketchfab. These directly reproduce the paper's photogrammetry-based plant digitization outputsDataset · publicThe meshes and image sets relevant to this project are available at Morphosource ( https://www.morphosource.org/projects/000494239?locale=en ). The data were uploaded and managed by Alex Adkinson.Open asset ↗MorphoSource · 000494239lines:228-292Code / dataset availability confirmedEurope PMC · OpenAlex · checked 13 Sept 2026
Tree height is a crucial structural parameter in forest inventory as it provides a basis for evaluating stock volume and growth status. In recent years, close-range photogrammetry based on smartphone has attracted attention from researchers due to its low cost and non-destructive characteristics. However, such methods have specific requirements for camera angle and distance during shooting, and pre-shooting operations such as camera calibration and placement of calibration boards are necessary, which could be inconvenient to operate in complex natural environments. We propose a tree height measurement method based on three-dimensional (3D) reconstruction. Firstly, an absolute depth map was obtained by combining ARCore and MidasNet. Secondly, Attention-UNet was improved by adding depth maps as network input to obtain tree mask. Thirdly, the color image and depth map were fused to obtain the 3D point cloud of the scene. Then, the tree point cloud was extracted using the tree mask. Finally, the tree height was measured by extracting the axis-aligned bounding box of the tree point cloud. We built the method into an Android app, demonstrating its efficiency and automation. Our approach achieves an average relative error of 3.20% within a shooting distance range of 2-17 m, meeting the accuracy requirements of forest survey.
Why it matches plant phenotyping methodsスマートフォン画像・深度情報と3D再構成を用いて樹高という植物構造形質を自動測定する手法を開発し、誤差評価とAndroidアプリ化まで行っているため、植物フェノタイピング手法が中心である。
abstractWe propose a tree height measurement method based on three-dimensional (3D) reconstruction.
Reproduction assets foundThe paper's Data Availability Statement explicitly provides two paper-specific public assets: the source code of the TreeHeight prototype app on GitHub and the authors' annotated tree image dataset (300 annotated images augmented to 1000 pairs of color images, relative depth maps, and tree masks) on Google Drive. Both,Code · publicThe source code of the prototype app is publicly available on GitHub at https://github.com/LisaShen0509/Tree_Height_Measurement (accessed on 27 July 2023).Open asset ↗LisaShen0509/Tree_Height_Measurementlines:432-615Dataset · publicTree image dataset is available at https://drive.google.com/file/d/1kG6LWMOAiA2KvGF_suZ5cG_4C-udUV0m/view?usp=sharing (accessed on 27 July 2023).Open asset ↗lines:432-615Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Premise: Many plant communities across the world are undergoing changes due to climate change, human disturbance, and other threats. These community-level changes are often tracked with the use of permanent vegetative plots, but this approach is not always feasible. As an alternative, we propose using photogrammetry, specifically photograph-based digital surface models (DSMs) developed using structure-from-motion, to establish virtual permanent plots in plant communities where the use of permanent structures may not be possible. Methods: plots distributed across alpine communities in the northeastern United States. We then compared field estimates of percent coverage with coverage estimated using DSMs. Results: Digital surface models can provide effective, minimally invasive, and permanent records of plant species presence and percent coverage, while also allowing managers to mark survey locations virtually for long-term monitoring. We found that percent coverage estimated from DSMs did not differ from field estimates for most species and substrates. Discussion: In order to continue surveying efforts in areas where permanent structures or other surveying methods are not feasible, photogrammetry and structure-from-motion methods can provide a low-cost approach that allows agencies to accurately survey and record sensitive plant communities through time.
Why it matches plant phenotyping methods植物群落の種存在と被覆率を、写真測量・SfMによるDSMから推定する手法を開発し、現地推定値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す。
abstractwe propose using photogrammetry, specifically photograph-based digital surface models (DSMs) developed using structure-from-motion, to establish virtual permanent plots in plant communities
Reproduction assets foundThe authors publicly deposited the 3D image models (DSMs/virtual permanent plots) created in this study on FigShare, as stated in the Data Availability Statement. Other URLs (Agisoft, QGIS, R Metrics, GLORIA) are generic tools or cited prior work, not paper-specific assets.Dataset · publiccreate virtual permanent plots in rare and threatened plant communities. Applications in Plant Sciences
11(5): e11534. 10.1002/aps3.11534
This article is part of the special issue “Advances in Plant Imaging across Scales.”
DATA AVAILABILITY STATEMENT
3D image models created during this project can be found online on FigShare ( https://figshare.com/s/03a500bf7717afe3a9a6 ).
REFERENCES
Agisoft Helpdesk Portal
. 2022. 3D Model Reconstruction. Website: https://agisoft.freshdesk.com/support/solutions/articles/31000152092 [accessed 12 June 2023].
Barros, A.
, Aschero V., Mazzolari A., Cavieres L. A., and Pickering C. M.. 2020. Going off trails: How dispersed visitor use affects alpine vegetation. Open asset ↗FigSharelines:99-133Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
Field / plotPhotogrammetry / SfM / MVSLiDAR / point cloudStem / branchMorphology / geometry measurement2D/3D reconstructionVisualization / data managementPlant / canopy height
Introduction Rubber trees are an important cash crop in Hainan Province; thus, monitoring sample plots of these trees provides important data for determining growth conditions. However, existing monitoring technology and rubber forest sample plot analysis methods are relatively simple and present widespread issues, such as limited monitoring equipment, transportation difficulties, and relatively poor three-dimensional visualization effects in complex environments. These limitations have complicated the development of rubber forest sample plot monitoring. Method This study developed a terrestrial photogrammetry system combined with 3D point-cloud reconstruction technology based on the structure from motion with multi-view stereo method and sample plot survey data. Deviation analyses and accuracy evaluations of sample plot information were performed in the study area for trees to explore the practical significance of this method for monitoring rubber forest sample plots. Furthermore, the relationship between the height of the first branch, diameter at breast height (DBH), and rubber tree volume was explored, and a rubber tree standard volume model was established. Results The Bias, relative Bias, RMSE, and RRMSE of the height of the first branch measured by this method were −0.018 m, −0.371%, 0.562 m, and 11.573%, respectively. The Bias, relative Bias, RMSE, and RRMSE of DBH were −0.484 cm, −1.943%, −2.454 cm, and 9.859%, respectively, which proved that the method had high monitoring accuracy and met the monitoring requirements of rubber forest sample plots. The fitting results of rubber tree standard volume model had an R2 value of 0.541, and the estimated values of each parameter were 1.745, 0.115, and 0.714. The standard volume model accurately estimated the volume of rubber trees and forests using the first branch height and DBH. Discussion This study proposed an innovative planning scheme for a terrestrial photogrammetry system for 3D visual monitoring of rubber tree forests, thus providing a novel solution to issues observed in current sample plot monitoring practices. In the future, the application of terrestrial photogrammetry systems to monitor other types of forests will be explored.
Why it matches plant phenotyping methods地上 photogrammetry と3D点群再構成を用いて樹高関連形質、DBH、樹木体積を取得・検証する方法を開発し、精度評価も行っており、植物形質計測が中心である。
abstractThis study developed a terrestrial photogrammetry system combined with 3D point-cloud reconstruction technology based on the structure from motion with multi-view stereo method and sample plot survey data.
Reproduction assets foundThe paper's data availability statement deposits the study's dataset (3D visual sustainable management of rubber forest based on terrestrial photogrammetry system) on Figshare with a public DOI, making the paper-specific phenotyping data (DBH, first branch height, point-cloud measurements) publicly available.Dataset · publics in the future. Such monitoring is important for the sustainable development of tropical agriculture and forestry in Hainan Province.
Statements
Data availability statement
The datasets [3D Visual Sustainable Management of Rubber Forest Based on Terrestrial Photogrammetry System] for this study can be found in the [FIGSHARE] [ https://doi.org/10.6084/m9.figshare.22133126 ].
Author contributions
ZQ and SL contributed to the conception and design of the study and wrote the first draft of the manuscript. SL, LL, YX, CW, NL, RL, and DY organized the database and performed the statistical analysis. LL, YX, CW, NL, RL, and DY wrote the sections of the manuscript. All authors contributed to the maOpen asset ↗FIGSHARE · 10.6084/m9.figshare.22133126lines:623-661Code / dataset availability confirmedarXiv · OpenAlex · checked 15 Sept 2026
Automatic tree density estimation and counting using single aerial and satellite images is a challenging task in photogrammetry and remote sensing, yet has an important role in forest management. In this paper, we propose the first semisupervised transformer-based framework for tree counting which reduces the expensive tree annotations for remote sensing images. Our method, termed as TreeFormer, first develops a pyramid tree representation module based on transformer blocks to extract multi-scale features during the encoding stage. Contextual attention-based feature fusion and tree density regressor modules are further designed to utilize the robust features from the encoder to estimate tree density maps in the decoder. Moreover, we propose a pyramid learning strategy that includes local tree density consistency and local tree count ranking losses to utilize unlabeled images into the training process. Finally, the tree counter token is introduced to regulate the network by computing the global tree counts for both labeled and unlabeled images. Our model was evaluated on two benchmark tree counting datasets, Jiangsu, and Yosemite, as well as a new dataset, KCL-London, created by ourselves. Our TreeFormer outperforms the state of the art semi-supervised methods under the same setting and exceeds the fully-supervised methods using the same number of labeled images. The codes and datasets are available at https://github.com/HAAClassic/TreeFormer.
Why it matches plant phenotyping methods樹木の個体数・密度という植物状態を航空・衛星画像から推定する画像解析手法を開発し、複数データセットで評価しているため、植物フェノタイピング手法が中心である。
abstractAutomatic tree density estimation and counting using single aerial and satellite images is a challenging task
Reproduction assets foundThe paper's authors publicly release their analysis code and the KCL-London tree counting dataset via GitHub, and the paper's annotation workflow directly uses the public London Datastore local-authority-maintained trees dataset for tree locations.Code · publichmark tree counting datasets, Jiangsu, and Yosemite, as well as a new dataset, KCL-London, created by ourselves. Our TreeFormer outperforms the state of the art semi-supervised methods under the same setting and exceeds the fully-supervised methods using the same number of labeled images. The codes and datasets are available at https://github.com/HAAClassic/TreeFormer .
Index Terms:
Tree counting, semi-supervised model, transformer, pyramid learning strategy, remote sensing.
I Introduction
Trees are the pulse of the earth and are vital organisms in maintaining the ecological functioning and health of the planet [ 1 ] . Tree counting using high-resolution images is useful in various fields suOpen asset ↗HAAClassic/TreeFormerlines:1-71Dataset · publicmages are gathered and stitched together from Google Maps at 0.2 m ground sampling distance (GSD). The gathered images are divided into images with 1024 × 1024 pixels.
To aid the identification of tree locations and numbers of selected images, we employed the accessible tree locations of London in London Datastore website 1 1
1
https://data.london.gov.uk/dataset/local-authority-maintained-trees .
Although these data show the locations and species information for over 880,000 of London’s trees, the data mainly contains information on trees in the main streets and does not cover trees that are dense between houses or parks. We manually annotated the latter.
To this end, Global Mapper as geograOpen asset ↗lines:124-145Code / dataset availability confirmedEurope PMC · checked 13 Sept 2026
There is a growing body of literature that recognises the importance of UAVs in precision agriculture tasks. Currently, flowering thinning tasks in orchard management rely on the decisions derived from time-consuming manual flower cluster counting in the field by an agrotechnician. Yet it is hard to guarantee the counting accuracy due to numerous human factors. The present dataset contains UAV images during the full blooming period of an apple orchard for three consecutive years, 2018, 2019, and 2020. It is directly linked to a research article entitled "Feasibility assessment of tree-level flower intensity quantification from UAV RGB imagery: A triennial study in an apple orchard". The data collection site was an apple orchard located at Randwijk, Overbetuwe, The Netherlands (51.938, 5.7068 in WGS84 UTM 31U). Moreover, the flower cluster number and floridity ground truth are also provided in one row from the orchard. The UAV flights were conducted with different flying altitudes, camera resolutions, and lighting conditions. This dataset aims to support researchers focussing on remote sensing, machine vision, deep learning, and image classification, and the stakeholders interested in precision horticulture and orchard management. It can be used for flowering intensity estimation and prediction, and spatial and temporal flowering variability mapping by using digital photogrammetry and 3D reconstruction.
Why it matches plant phenotyping methodsリンゴ樹の開花強度という植物形質をUAV RGB画像から推定するための3年間の画像・地上真値データセットであり、再利用可能なフェノタイピング基盤として中心的です。
abstractThe present dataset contains UAV images during the full blooming period of an apple orchard for three consecutive years, 2018, 2019, and 2020.
Reproduction assets foundThis Data in Brief article describes the authors' own public Zenodo deposit containing the paper-specific UAV RGB images, flower cluster/floridity ground truth, and GCP files for the apple orchard flowering monitoring study, with direct download URL provided.Dataset · publicRepository name: Zenodo
Data identification number: https://doi.org/10.5281/zenodo.6802308
Direct URL to data: https://zenodo.org/record/6802308#.YvvMFuxBz0pOpen asset ↗Zenodo · 10.5281/zenodo.6802308lines:1-51Code / dataset availability confirmedOpenAlex · Crossref · checked 15 Sept 2026
The study of plant phenotypes based on 3D models has become an important research direction for automatic plant phenotype acquisition. Building a labeled three-dimensional dataset of the whole growth period can help the development of 3D crop plant models in point cloud segmentation. Therefore, the demand for 3D whole plant growth period model datasets with organ-level markers is growing rapidly. In this study, five different soybean varieties were selected, and three-dimensional reconstruction was carried out for the whole growth period (13 stages) of soybean using multiple-view stereo technology (MVS). Leaves, main stems, and stems of the obtained three-dimensional model were manually labeled. Finally, two-point cloud semantic segmentation models, RandLA-Net and BAAF-Net, were used for training. In this paper, 102 soybean stereoscopic plant models were obtained. A dataset with original point clouds was constructed and the subsequent analysis confirmed that the number of plant point clouds was consistent with corresponding real plant development. At the same time, a 3D dataset named Soybean-MVS with labels for the whole soybean growth period was constructed. The test result of mAccs at 88.52% and 87.45% verified the availability of this dataset. In order to further promote the study of point cloud segmentation and phenotype acquisition of soybean plants, this paper proposed an annotated three-dimensional model dataset for the whole growth period of soybean for 3D plant organ segmentation. The release of the dataset can provide an important basis for proposing an updated, highly accurate, and efficient 3D crop model segmentation algorithm. In the future, this dataset will provide important and usable basic data support for the development of three-dimensional point cloud segmentation and phenotype automatic acquisition technology of soybeans.
Why it matches plant phenotyping methods大豆全生育期の3D再構成、器官ラベル付き点群データセットの構築と検証が中心で、植物表現型自動取得を支援する再利用可能な基盤である。
abstractThe study of plant phenotypes based on 3D models has become an important research direction for automatic plant phenotype acquisition.
Reproduction assets foundThe paper's own 3D soybean phenotyping assets are publicly available: the original reconstructed 3D models and the annotated Soybean-MVS point cloud dataset are on Kaggle, and the authors' analysis code (BAAF-Net and RandLA-Net segmentation implementations used to train/test the dataset) is on GitHub with explicit dataDataset · publicNatural Science Foundation of Heilongjiang Province of
China (LH2021C021).
Institutional Review Board Statement: Not applicable.
Data Availability Statement: Original models are available in a publicly accessible repository: The
original contributions presented in the study are publicly available. These data can be found here:
https://www.kaggle.com/datasets/soberguo/soybean-original-model (accessed on 1 January 2023).
The soybean-MVS dataset is available in a publicly accessible repository: Publicly available datasets
were analyzed in this study. These data can be found here: https://www.kaggle.com/datasets/soberguo/soybeanmvs (accessed on 1 January 2023).
Conflicts of Interest: The authorsOpen asset ↗soberguo/soybean-original-modelpdf-raw-page:15 lines:1-47Dataset · publicesented in the study are publicly available. These data can be found here:
https://www.kaggle.com/datasets/soberguo/soybean-original-model (accessed on 1 January 2023).
The soybean-MVS dataset is available in a publicly accessible repository: Publicly available datasets
were analyzed in this study. These data can be found here: https://www.kaggle.com/datasets/soberguo/soybeanmvs (accessed on 1 January 2023).
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
Table A1. Image collection quantity of soybean plants of different varieties in different stages.
V1 V2 V3 V4 V5 R1 R2 R3 R4 R5 R6 R7 R8
2018 2019 2018 2019 2018 2019 2018 2019 2018 2019 2018 2019 2018 2019 20Open asset ↗soberguo/soybeanmvspdf-raw-page:15 lines:1-47Code / dataset availability confirmedCrossref · OpenAlex · checked 14 Sept 2026
Abstract Understanding three‐dimensional (3D) root traits is essential to improve water uptake, increase nitrogen capture, and raise carbon sequestration from the atmosphere. However, quantifying 3D root traits by reconstructing 3D root models for deeper field‐grown roots remains a challenge due to the unknown tradeoff between 3D root‐model quality and 3D root‐trait accuracy. Therefore, we performed two computational experiments. We first compared the 3D model quality generated by five state‐of‐the‐art open‐source 3D model reconstruction pipelines on 12 contrasting genotypes of field‐grown maize roots. These pipelines included COLMAP, COLMAP+PMVS (Patch‐based Multi‐View Stereo), VisualSFM, Meshroom, and OpenMVG+MVE (Multi‐View Environment). The COLMAP pipeline achieved the best performance regarding 3D model quality versus computational time and image number needed. In the second test, we compared the accuracy of 3D root‐trait measurement generated by the Digital Imaging of Root Traits 3D pipeline (DIRT/3D) using COLMAP‐based 3D reconstruction with our current DIRT/3D pipeline that uses a VisualSFM‐based 3D reconstruction on the same dataset of 12 genotypes, with 5–10 replicates per genotype. The results revealed that (1) the average number of images needed to build a denser 3D model was reduced from 3000 to 3600 (DIRT/3D [VisualSFM‐based 3D reconstruction]) to around 360 for computational test 1, and around 600 for computational test 2 (DIRT/3D [COLMAP‐based 3D reconstruction]); (2) denser 3D models helped improve the accuracy of the 3D root‐trait measurement; (3) reducing the number of images can help resolve data storage problems. The updated DIRT/3D (COLMAP‐based 3D reconstruction) pipeline enables quicker image collection without compromising the accuracy of 3D root‐trait measurements.
Why it matches plant phenotyping methods3D画像再構成パイプラインを比較・検証し、更新版DIRT/3Dによる根形質推定の精度と効率を評価しており、植物フェノタイピング手法が中心である。
titleComparison of open‐source three‐dimensional reconstruction pipelines for maize‐root phenotyping
Reproduction assets foundThe paper publicly releases its analysis scripts on GitHub, demo workflows for reconstruction and trait computation, Docker/Singularity containers for DIRT/3D reconstruction and trait extraction, and manuscript data on CyVerse Data Commons via a permanent DOI.Code · publice computation of the software-
supported GPUs. The GPU model with the DELL workstation
was a GeForce RTX 2070 SUPER, NVIDIA Corporation
TU104, nvcc: NVIDIA (R) Cuda compiler driver. All the
pipelines were tested under the command-line interface to
generate related 3D root models in point cloud format. The
scripts are on GitHub (https://github.com/Computational-Plant-Science/3D_review_scripts/tree/master, folder Compu-
tational_test_1).
25782703,
2023,
1,
Downloaded
from
https://acsess.onlinelibrary.wiley.com/doi/10.1002/ppj2.20068,
Wiley
Online
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[28/06/2023].
See
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Terms
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(https://onlinelibrary.wiley.com/terms-and-conditions)
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of
useOpen asset ↗Computational-Plant-Science/3D_review_scriptspdf-raw-page:3 lines:1-114Code · publicm the University of Georgia to the University
of Arizona.
C O N F L I C T O F I N T E R E S T S TAT E M E N T
The authors declare no conflicts of interest.
DATA AVA I L A B I L I T Y S TAT E M E N T
GitHub link for all the scripts for running the test:
https://github.com/Computational-Plant-Science/3D_review_scripts/tree/master
https://github.com/Computational-Plant-Science/3D_model_reconstruction_demo
https://github.com/Computational-Plant-Science/3D_model_traits_demo
Permamnent DOI link to access manuscript data on
CyVerse Data Commons: https://www.doi.org/10.25739/sg2m-ky55/O RC I D
SuxingLiu https://orcid.org/0000-0001-7639-4470
WesleyPaul Bonelli https://orcid.org/0000-0002-2665-5078
PeOpen asset ↗Computational-Plant-Science/3D_model_reconstruction_demopdf-raw-page:12 lines:1-88Code · publicF I N T E R E S T S TAT E M E N T
The authors declare no conflicts of interest.
DATA AVA I L A B I L I T Y S TAT E M E N T
GitHub link for all the scripts for running the test:
https://github.com/Computational-Plant-Science/3D_review_scripts/tree/master
https://github.com/Computational-Plant-Science/3D_model_reconstruction_demo
https://github.com/Computational-Plant-Science/3D_model_traits_demo
Permamnent DOI link to access manuscript data on
CyVerse Data Commons: https://www.doi.org/10.25739/sg2m-ky55/O RC I D
SuxingLiu https://orcid.org/0000-0001-7639-4470
WesleyPaul Bonelli https://orcid.org/0000-0002-2665-5078
Peter Pietrzyk https://orcid.org/0000-0002-6794-8133
Alexander Bucksch https:/Open asset ↗Computational-Plant-Science/3D_model_traits_demopdf-raw-page:12 lines:1-88Code / dataset availability confirmedOpenAlex · checked 13 Sept 2026
Remote-sensing data has become essential for site-specific farming methods. It is also a powerful tool for monitoring the agroecosystem services offered by integrating cover crops (CC) into crop rotations. This study presents a method to determine the canopy height (CH), defined as the average height of the crop stand surface, including tops and gaps, of heterogeneous and multi-species CC using commercial unmanned aerial vehicles (UAVs). Images captured with red–green–blue cameras mounted on UAVs in two missions varying in ground sample distances were used as input for generating three-dimensional point clouds using the structure-from-motion approach. These point clouds were then compared to manual ground measurements. The results showed that the agreement between the methods was closest when CC presented dense and smooth canopies. However, stands with rough canopies or gaps showed substantial differences between the UAV method and ground measurements. We conclude that the UAV method is substantially more precise and accurate in determining CH than measurements taken with a ruler since the UAV introduces additional dimensions with greatly increased resolution. CH can be a reliable indicator of biomass yield, but no differences between the investigated methods were found, probably due to allometric variations of different CC species. We propose the presented UAV method as a promising tool to include site-specific information on CC in crop production strategies.
Why it matches plant phenotyping methodsUAV画像とSfM点群を用いて被覆作物の群落高を推定する手法を提示し、地上測定と比較検証しており、植物形質取得法が研究の中心である。
abstractThis study presents a method to determine the canopy height (CH), defined as the average height of the crop stand surface, including tops and gaps, of heterogeneous and multi-species CC using commercial unmanned aerial vehicles (UAVs).
Reproduction assets foundThe paper's canopy height and biomass measurements (UAV-derived CH, ruler measurements, DMY) are openly available as a Zenodo dataset, explicitly stated in the Data Availability Statement. The Metashape scripts GitHub link and CRAN raster package are generic third-party tools, not authors' analysis code.Dataset · publicData Availability Statement: The data presented in this study are openly available in the Zenodo
archive at the following DOI: https://doi.org/10.5281/zenodo.7713341 (Kümmerer, 2023).Open asset ↗Zenodo · 10.5281/zenodo.7713341pdf-page:15 lines:1-51Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 15 Sept 2026
Current methods of root sampling typically only obtain small or incomplete sections of root systems and do not capture their true complexity. To facilitate the visualization and analysis of full-sized plant root systems in 3-dimensions, we developed customized mesocosm growth containers. While highly scalable, the design presented here uses an internal volume of 45 ft 3 (1.27 m 3 ), suitable for large crop and bioenergy grass root systems to grow largely unconstrained. Furthermore, they allow for the excavation and preservation of 3-dimensional root system architecture (RSA), and facilitate the collection of time-resolved subterranean environmental data. Sensor arrays monitoring matric potential, temperature and CO 2 levels are buried in a grid formation at various depths to assess environmental fluxes at regular intervals. Methods of 3D data visualization of fluxes were developed to allow for comparison with root system architectural traits. Following harvest, the recovered root system can be digitally reconstructed in 3D through photogrammetry, which is an inexpensive method requiring only an appropriate studio space and a digital camera. We developed a pipeline to extract features from the 3D point clouds, or from derived skeletons that include point cloud voxel number as a proxy for biomass, total root system length, volume, depth, convex hull volume and solidity as a function of depth. Ground-truthing these features with biomass measurements from manually dissected root systems showed a high correlation. We evaluated switchgrass, maize, and sorghum root systems to highlight the capability for species wide comparisons. We focused on two switchgrass ecotypes, upland (VS16) and lowland (WBC3), in identical environments to demonstrate widely different root system architectures that may be indicative of core differences in their rhizoeconomic foraging strategies. Finally, we imposed a strong physiological water stress and manipulated the growth medium to demonstrate whole root system plasticity in response to environmental stimuli. Hence, these new "3D Root Mesocosms" and accompanying computational analysis provides a new paradigm for study of mature crop systems and the environmental fluxes that shape them.
Why it matches plant phenotyping methods3Dルートメソコスム、フォトグラメトリ、点群解析による根系形態形質の取得・検証が研究の中心であり、植物フェノタイピング手法に該当する。
abstractTo facilitate the visualization and analysis of full-sized plant root systems in 3-dimensions, we developed customized mesocosm growth containers.
Reproduction assets foundThe paper's supplementary videos on figshare are photogrammetry-generated 3D point clouds of the paper's own root system phenotyping measurements (sorghum, maize, and switchgrass root systems, including stress-conditioned and sensor-flux coaligned visualizations), publicly downloadable. The OpenCV link is a generic, unDataset · publice, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2022.1041404/full#supplementary-material . Videos can be found for viewing and download at https://doi.org/10.6084/m9.figshare.21335898.v1 .
Supplementary Figure 1
Interpolation of 3-dimensional environmental sensor data.
Click here for additional data file.
Supplementary Figure 2
Time course of shoot morphological responses of switchgrass in different growth media.
Click here for additional data file.
Supplementary Figure 3
Manual post-process cleaning of Open asset ↗figshare · 10.6084/m9.figshare.21335898.v1lines:327-356Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Abstract. The SiDroForest (Siberian drone-mapped forest inventory) data collection is an attempt to remedy the scarcity of forest structure data in the circumboreal region by providing adjusted and labeled tree-level and vegetation plot-level data for machine learning and upscaling purposes. We present datasets of vegetation composition and tree and plot level forest structure for two important vegetation transition zones in Siberia, Russia; the summergreen–evergreen transition zone in Central Yakutia and the tundra–taiga transition zone in Chukotka (NE Siberia). The SiDroForest data collection consists of four datasets that contain different complementary data types that together support in-depth analyses from different perspectives of Siberian Forest plot data for multi-purpose applications. i. Dataset 1 provides unmanned aerial vehicle (UAV)-borne data products covering the vegetation plots surveyed during fieldwork (Kruse et al., 2021, https://doi.org/10.1594/PANGAEA.933263). The dataset includes structure-from-motion (SfM) point clouds and red–green–blue (RGB) and red–green–near-infrared (RGN) orthomosaics. From the orthomosaics, point-cloud products were created such as the digital elevation model (DEM), canopy height model (CHM), digital surface model (DSM) and the digital terrain model (DTM). The point-cloud products provide information on the three-dimensional (3D) structure of the forest at each plot.ii. Dataset 2 contains spatial data in the form of point and polygon shapefiles of 872 individually labeled trees and shrubs that were recorded during fieldwork at the same vegetation plots (van Geffen et al., 2021c, https://doi.org/10.1594/PANGAEA.932821). The dataset contains information on tree height, crown diameter, and species type. These tree and shrub individually labeled point and polygon shapefiles were generated on top of the RGB UVA orthoimages. The individual tree information collected during the expedition such as tree height, crown diameter, and vitality are provided in table format. This dataset can be used to link individual information on trees to the location of the specific tree in the SfM point clouds, providing for example, opportunity to validate the extracted tree height from the first dataset. The dataset provides unique insights into the current state of individual trees and shrubs and allows for monitoring the effects of climate change on these individuals in the future.iii. Dataset 3 contains a synthesis of 10 000 generated images and masks that have the tree crowns of two species of larch (Larix gmelinii and Larix cajanderi) automatically extracted from the RGB UAV images in the common objects in context (COCO) format (van Geffen et al., 2021a, https://doi.org/10.1594/PANGAEA.932795). As machine-learning algorithms need a large dataset to train on, the synthetic dataset was specifically created to be used for machine-learning algorithms to detect Siberian larch species.iv. Dataset 4 contains Sentinel-2 (S-2) Level-2 bottom-of-atmosphere processed labeled image patches with seasonal information and annotated vegetation categories covering the vegetation plots (van Geffen et al., 2021b, https://doi.org/10.1594/PANGAEA.933268). The dataset is created with the aim of providing a small ready-to-use validation and training dataset to be used in various vegetation-related machine-learning tasks. It enhances the data collection as it allows classification of a larger area with the provided vegetation classes. The SiDroForest data collection serves a variety of user communities. The detailed vegetation cover and structure information in the first two datasets are of use for ecological applications, on one hand for summergreen and evergreen needle-leaf forests and also for tundra–taiga ecotones. Datasets 1 and 2 further support the generation and validation of land cover remote-sensing products in radar and optical remote sensing. In addition to providing information on forest structure and vegetation composition of the vegetation plots, the third and fourth datasets are prepared as training and validation data for machine-learning purposes. For example, the synthetic tree-crown dataset is generated from the raw UAV images and optimized to be used in neural networks. Furthermore, the fourth SiDroForest dataset contains S-2 labeled image patches processed to a high standard that provide training data on vegetation class categories for machine-learning classification with JavaScript Object Notation (JSON) labels provided. The SiDroForest data collection adds unique insights into remote hard-to-reach circumboreal forest regions.
Why it matches plant phenotyping methodsUAV画像・点群から森林の3D構造や個体樹木の高さ・樹冠径を扱う再利用可能なデータセットを提供し、抽出結果の検証や機械学習に用いるため、植物表現型データ基盤が中心です。
abstractThe SiDroForest (Siberian drone-mapped forest inventory) data collection is an attempt to remedy the scarcity of forest structure data in the circumboreal region by providing adjusted and labeled tree-level and vegetation plot-level data for machine learning and upscaling purposes.
Reproduction assets foundThe paper is a data description paper for the SiDroForest collection; all four datasets (UAV-SfM point clouds/orthomosaics, individually labeled trees, synthetic tree-crown images, Sentinel-2 labeled patches) are published on PANGAEA with explicit public download availability.Dataset · publice future users time when attempting to classify
vegetation of central Siberian and eastern Siberian boreal forests.
5 Data availability
All four datasets of the SiDroForest data collection are published in the PANGAEA data repository and are available for download:
i.
UAV-SfM point clouds, point-cloud products, and orthoimages: https://doi.org/10.1594/PANGAEA.933263 (Kruse
et al., 2021b),
ii.
Individually labeled trees: https://doi.org/10.1594/PANGAEA.932821 (van Geffen et al., 2021c),
iii.
Synthetically created tree-crown dataset: https://doi.org/10.1594/PANGAEA.932795 (van Geffen et al., 2021a),
iv.
Sentinel-2 labeled image patches: https://doi.org/10.1594/PANGAEA.933268 (van Geffen et aOpen asset ↗PANGAEA · 10.1594/PANGAEA.933263lines:557-585Dataset · publicerian boreal forests.
5 Data availability
All four datasets of the SiDroForest data collection are published in the PANGAEA data repository and are available for download:
i.
UAV-SfM point clouds, point-cloud products, and orthoimages: https://doi.org/10.1594/PANGAEA.933263 (Kruse
et al., 2021b),
ii.
Individually labeled trees: https://doi.org/10.1594/PANGAEA.932821 (van Geffen et al., 2021c),
iii.
Synthetically created tree-crown dataset: https://doi.org/10.1594/PANGAEA.932795 (van Geffen et al., 2021a),
iv.
Sentinel-2 labeled image patches: https://doi.org/10.1594/PANGAEA.933268 (van Geffen et al., 2021b).
6 Conclusions
The circumboreal forests are covering large areas on the globe. EverOpen asset ↗PANGAEA · 10.1594/PANGAEA.932821lines:557-585Dataset · publice PANGAEA data repository and are available for download:
i.
UAV-SfM point clouds, point-cloud products, and orthoimages: https://doi.org/10.1594/PANGAEA.933263 (Kruse
et al., 2021b),
ii.
Individually labeled trees: https://doi.org/10.1594/PANGAEA.932821 (van Geffen et al., 2021c),
iii.
Synthetically created tree-crown dataset: https://doi.org/10.1594/PANGAEA.932795 (van Geffen et al., 2021a),
iv.
Sentinel-2 labeled image patches: https://doi.org/10.1594/PANGAEA.933268 (van Geffen et al., 2021b).
6 Conclusions
The circumboreal forests are covering large areas on the globe. Every new forest dataset collected, processed further, and published in a ready-to-use format for a wide range of biolOpen asset ↗PANGAEA · 10.1594/PANGAEA.932795lines:557-585Dataset · publicand orthoimages: https://doi.org/10.1594/PANGAEA.933263 (Kruse
et al., 2021b),
ii.
Individually labeled trees: https://doi.org/10.1594/PANGAEA.932821 (van Geffen et al., 2021c),
iii.
Synthetically created tree-crown dataset: https://doi.org/10.1594/PANGAEA.932795 (van Geffen et al., 2021a),
iv.
Sentinel-2 labeled image patches: https://doi.org/10.1594/PANGAEA.933268 (van Geffen et al., 2021b).
6 Conclusions
The circumboreal forests are covering large areas on the globe. Every new forest dataset collected, processed further, and published in a ready-to-use format for a wide range of biological and ecological applications is therefore quite rare and an important addition for scientific studiOpen asset ↗PANGAEA · 10.1594/PANGAEA.933268lines:557-585Code / dataset availability confirmedarXiv · OpenAlex · checked 14 Sept 2026
We propose a novel hybrid cable-based robot with manipulator and camera for high-accuracy, medium-throughput plant monitoring in a vertical hydroponic farm and, as an example application, demonstrate non-destructive plant mass estimation. Plant monitoring with high temporal and spatial resolution is important to both farmers and researchers to detect anomalies and develop predictive models for plant growth. The availability of high-quality, off-the-shelf structure-from-motion (SfM) and photogrammetry packages has enabled a vibrant community of roboticists to apply computer vision for non-destructive plant monitoring. While existing approaches tend to focus on either high-throughput (e.g. satellite, unmanned aerial vehicle (UAV), vehicle-mounted, conveyor-belt imagery) or high-accuracy/robustness to occlusions (e.g. turn-table scanner or robot arm), we propose a middle-ground that achieves high accuracy with a medium-throughput, highly automated robot. Our design pairs the workspace scalability of a cable-driven parallel robot (CDPR) with the dexterity of a 4 degree-of-freedom (DoF) robot arm to autonomously image many plants from a variety of viewpoints. We describe our robot design and demonstrate it experimentally by collecting daily photographs of 54 plants from 64 viewpoints each. We show that our approach can produce scientifically useful measurements, operate fully autonomously after initial calibration, and produce better reconstructions and plant property estimates than those of over-canopy methods (e.g. UAV). As example applications, we show that our system can successfully estimate plant mass with a Mean Absolute Error (MAE) of 0.586g and, when used to perform hypothesis testing on the relationship between mass and age, produces p-values comparable to ground-truth data (p=0.0020 and p=0.0016, respectively).
Why it matches plant phenotyping methods植物の多視点画像取得、SfM再構成、質量推定を中核とするロボット型フェノタイピング手法の開発・実証であり、単なる生物学的測定ではない。
abstractWe describe our robot design and demonstrate it experimentally by collecting daily photographs of 54 plants from 64 viewpoints each.
Satellite-based gross primary production (GPP) estimation has uncertainties due to shadow fraction caused by the geometric relationship between the complex forest structure and the Sun. The virtual forests allow shadow fraction estimation without 3D measurements, but require optimal structural parameters. In this study, we developed the reflectance simulator (Canopy-level Shadow and Reflectance Simulator, CSRS) that considers tree shadows and the method to determine the optimal canopy shape for shadow fraction estimation. The target forest is any tropical evergreen forest which accounts for 58% of tropical forests. Firstly, we analyzed the effects of canopy shape on the reflectance simulation based on virtual forests created with different canopy shapes. This result was checked by Tukey’s honestly significant difference (HSD) test. Secondly, the optimal canopy shape was determined by comparing the reflectance from Sentinel-2 Band 4 (red) bottom of atmosphere reflectance with those simulated from virtual forests. Finally, the shadow fraction estimated from the virtual forest was evaluated. Since the focus of this study was to derive the optimal canopy shape, unmanned aerial vehicle (UAV) structure from motion (SfM) was used to obtain the parameters other than canopy shape and to validate the estimated shadow fraction. The results showed that when the Sun zenith angle (SZA) was more than 20°, significant differences were observed among canopy shapes. The least root mean square error (RMSE) for reflectance simulation was 0.385 from the canopy shape of a half ellipsoid. Moreover, the half ellipsoid also showed the smallest RMSE in estimating shadow fraction (0.032), which indicated the reliability and applicability of CSRS. This study is the first attempt to determine the optimal canopy shape for estimating shadow fraction and is expected to improve the accuracy of GPP estimation in the future.
Why it matches plant phenotyping methods森林キャノピーの影分率という植物群落の状態を推定する反射シミュレータを開発し、UAV測定およびSentinel-2反射率との比較で検証しており、植物状態の取得・推定手法が中心である。
abstractwe developed the reflectance simulator (Canopy-level Shadow and Reflectance Simulator, CSRS) that considers tree shadows and the method to determine the optimal canopy shape for shadow fraction estimation.
Reproduction assets foundThe paper's CSRS reflectance/shadow simulation code is explicitly stated to be publicly available on the authors' GitHub repository. The ECOSTRESS Spectral Library is a generic external spectral database, not a paper-specific asset.Code · publicng—review and editing, W.T.; visualization, T.F.;
supervision, W.T.; project administration, W.T.; funding acquisition, W.T. All authors have read and
agreed to the published version of the manuscript.
Funding: This research received no external funding.
Data Availability Statement: The simulation code of CSRS is available from https://github.com/Takumi-Fuji6936/CSRS.git (accessed on 29 June 2022).
Conflicts of Interest: The authors declare no conflict of interest.
References
1. FAO. Assessment, Global Forest Resources 2020. Available online: https://www.fao.org/3/CA8753EN/CA8753EN.pdf (accessed
on 10 November 2021).
2. Beer, C.; Reichstein, M.; Tomelleri, E.; Ciais, P.; Jung, M.; CarvalhaisOpen asset ↗Takumi-Fuji6936/CSRSpdf-raw-page:13 lines:1-50Code / dataset availability confirmedOpenAlex · checked 13 Sept 2026
This study provides an accurate and efficient method to reconstruct detailed and high-resolution digital 3D models of carpological materials by photogrammetric method, in which only about 100 to 150 images are required for each model reconstruction. The 3D models reflect the realistic morphology and genuine color of the carpological materials. The 3D models are scaled to represent the true size of the materials even as small as 3 mm in diameter. The interfaces are interactive, in which the 3D models can be rotated in 360° to observe the structures and be zoomed to inspect the macroscopic details. This new platform is beneficial for developing a virtual herbarium of carpological collection which is thus the most important to botanical authentication and education.
Why it matches plant phenotyping methods植物の果実・種子等の形態を高解像度3D再構成するフォトグラメトリ手法とインタラクティブ基盤が中心であり、観察可能な植物形態を取得する方法論研究である。
abstractThis study provides an accurate and efficient method to reconstruct detailed and high-resolution digital 3D models of carpological materials by photogrammetric method
Reproduction assets foundThe authors publicly host the 3D models of carpological materials reconstructed in this study (100 models from the paper, over 250 released) in the 'Virtual Carpological Herbarium of Fruits and Seeds' online database. This is a paper-specific public asset directly reproducing the paper's phenotyping outputs. Software (Dataset · publicAll the 3D models were uploaded to an open online database ( https://syhuherbarium.sls.cuhk.edu.hk/collections/3d-specimen/ ; Username: syhuherbarium; Password: @CUHK). Currently, over 250 3D models were uploaded to the online database and more will be released in the future.Open asset ↗lines:94-123Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Herbaceous aboveground biomass (HAB) is a key indicator of grassland vegetation and indirect estimation tools, such as remote sensing imagery, increase the potential for covering larger areas in a timely and cost-efficient way. Structure from Motion (SfM) is an image analysis process that can create a variety of 3D spatial models as well as 2D orthomosaics from a set of images. Computed from Unmanned Aerial Vehicle (UAV) and ground camera measurements, the SfM potential to estimate the herbaceous aboveground biomass in Sahelian rangelands was tested in this study. Both UAV and ground camera recordings were used at three different scales: temporal, landscape, and national (across Senegal). All images were processed using PIX4D software (photogrammetry software) and were used to extract vegetation indices and heights. A random forest algorithm was used to estimate the HAB and the average estimation errors were around 150 g m - ² for fresh mass (20% relative error) and 60 g m - ² for dry mass (around 25% error). A comparison between different datasets revealed that the estimates based on camera data were slightly more accurate than those from UAV data. It was also found that combining datasets across scales for the same type of tool (UAV or camera) could be a useful option for monitoring HAB in Sahelian rangelands or in other grassy ecosystems.
Why it matches plant phenotyping methodsSfM画像解析とUAV・地上カメラを用いて、植生高や指数から草本地上部バイオマスを推定し、異なるデータセット・スケールで精度比較を行っており、植物形質推定手法が中心です。
abstractthe SfM potential to estimate the herbaceous aboveground biomass in Sahelian rangelands was tested in this study.
Reproduction assets foundThe paper's UAV and ground-camera SfM phenotyping datasets (National, Landscape, and Temporal UAV datasets) are explicitly deposited on Zenodo with DOIs given in the Open Research Badges and Data Availability sections, making them public, paper-specific, and directly actionable.Dataset · publicWriting – review & editing (equal). Emile Faye: Conceptualization (equal); Methodology (equal).
OPEN RESEARCH BADGES
This article has earned an Open Data Badge for making publicly available the digitally‐shareable data necessary to reproduce the reported results. The data is available at https://doi.org/10.5281/zenodo.6421543 , https://doi.org/10.5281/zenodo.5148337 and https://doi.org/10.5281/zenodo.5145395 .
Supporting information
Appendix S1
Click here for additional data file.
ACKNOWLEDGMENTS
This research was financed by the Carbon Sequestration and Green‐house Gas Emissions in (Agro) Sylvopastoral Ecosystems in the Sahelian CILSS States (CaSSECS) project, supported by the European UnOpen asset ↗zenodo · 10.5281/zenodo.5148337lines:571-608Dataset · publicaye: Conceptualization (equal); Methodology (equal).
OPEN RESEARCH BADGES
This article has earned an Open Data Badge for making publicly available the digitally‐shareable data necessary to reproduce the reported results. The data is available at https://doi.org/10.5281/zenodo.6421543 , https://doi.org/10.5281/zenodo.5148337 and https://doi.org/10.5281/zenodo.5145395 .
Supporting information
Appendix S1
Click here for additional data file.
ACKNOWLEDGMENTS
This research was financed by the Carbon Sequestration and Green‐house Gas Emissions in (Agro) Sylvopastoral Ecosystems in the Sahelian CILSS States (CaSSECS) project, supported by the European Union under the Development Smart InnovationOpen asset ↗zenodo · 10.5281/zenodo.5145395lines:571-608Dataset · publicview & editing (equal). Rasmus Fensholt: Writing – review & editing (equal). Emile Faye: Conceptualization (equal); Methodology (equal).
OPEN RESEARCH BADGES
This article has earned an Open Data Badge for making publicly available the digitally‐shareable data necessary to reproduce the reported results. The data is available at https://doi.org/10.5281/zenodo.6421543 , https://doi.org/10.5281/zenodo.5148337 and https://doi.org/10.5281/zenodo.5145395 .
Supporting information
Appendix S1
Click here for additional data file.
ACKNOWLEDGMENTS
This research was financed by the Carbon Sequestration and Green‐house Gas Emissions in (Agro) Sylvopastoral Ecosystems in the Sahelian CILSS States (CaSSEOpen asset ↗zenodo · 10.5281/zenodo.6421543lines:571-608Code / dataset availability confirmedCrossref · OpenAlex · checked 14 Sept 2026
Arctic vegetation communities are rapidly changing with climate warming, which impacts wildlife, carbon cycling and climate feedbacks. Accurately monitoring vegetation change is thus crucial, but scale mismatches between field and satellite-based monitoring cause challenges. Remote sensing from unmanned aerial vehicles (UAVs) has emerged as a bridge between field data and satellite-based mapping. We assess the viability of using high resolution UAV imagery and UAV-derived Structure from Motion (SfM) to predict cover, height and aboveground biomass (henceforth biomass) of Arctic plant functional types (PFTs) across a range of vegetation community types. We classified imagery by PFT, estimated cover and height, and modeled biomass from UAV-derived volume estimates. Predicted values were compared to field estimates to assess results. Cover was estimated with root-mean-square error (RMSE) 6.29-14.2% and height was estimated with RMSE 3.29-10.5 cm, depending on the PFT. Total aboveground biomass was predicted with RMSE 220.5 g m -2 , and per-PFT RMSE ranged from 17.14-164.3 g m -2 . Deciduous and evergreen shrub biomass was predicted most accurately, followed by lichen, graminoid, and forb biomass. Our results demonstrate the effectiveness of using UAVs to map PFT biomass, which provides a link towards improved mapping of PFTs across large areas using earth observation satellite imagery.
Why it matches plant phenotyping methodsUAV画像とSfMから植物機能タイプの被覆、草丈、地上部バイオマスを推定し、現地推定値との比較で精度評価を行うことが研究の中心であるため。
abstractWe assess the viability of using high resolution UAV imagery and UAV-derived Structure from Motion (SfM) to predict cover, height and aboveground biomass (henceforth biomass) of Arctic plant functional types (PFTs) across a range of vegetation community types.
Reproduction assets foundThe paper's plant-phenotyping data (UAV-derived PFT cover, canopy height, biomass, and field validation measurements) are explicitly stated to be publicly archived at the NSF Arctic Data Center under DOI 10.18739/A2R785Q5B. No author analysis code or trained model checkpoints are described with a public deposit.Dataset · publicns Attribution 4.0 International License (CC BY 4.0),
which permits unrestricted use, distribution, and reproduc-
tion in any medium, provided the original author(s) and
source are credited.
Data availability
Data supporting the results in this paper are publicly
archived at the National Science Foundation Arctic Data Cen-
ter: https://doi.org/10.18739/A2R785Q5B.Author information
Author ORCIDs
Kathleen M. Orndahlhttps://orcid.org/0000-0002-4873-4375
Author contributions
KMO and SJG conceived the ideas; KMO, LPWE, and JDH de-
signed the methodology; KMO, LPWE, JDH, and REP collected
the data; KMO, LPWE, and REP processed and curated the
data; KMO analyzed the data with input from MH; KMO ledOpen asset ↗10.18739/A2R785Q5Bpdf-raw-page:14 lines:1-99Code / dataset availability confirmedOpenAlex · checked 13 Sept 2026
While fire is an important ecological process, wildfire size and severity have increased as a result of climate change, historical fire suppression, and lack of adequate fuels management. Ladder fuels, which bridge the gap between the surface and canopy leading to more severe canopy fires, can inform management to reduce wildfire risk. Here, we compared remote sensing and field-based approaches to estimate ladder fuel density. We also determined if densities from different approaches could predict wildfire burn severity (Landsat-based Relativized delta Normalized Burn Ratio; RdNBR). Ladder fuel densities at 1-m strata and 4-m bins (1–4 m and 1–8 m) were collected remotely using a terrestrial laser scanner (TLS), a handheld-mobile laser scanner (HMLS), an unoccupied aerial system (UAS) with a multispectral camera and Structure from Motion (SfM) processing (UAS-SfM), and an airborne laser scanner (ALS) in 35 plots in oak woodlands in Sonoma County, California, United States prior to natural wildfires. Ladder fuels were also measured in the same plots using a photo banner. Linear relationships among ladder fuel densities estimated at broad strata (1–4 m, 1–8 m) were evaluated using Pearson’s correlation (r). From 1 to 4 m, most densities were significantly correlated across approaches. From 1 to 8 m, TLS densities were significantly correlated with HMLS, UAS-SfM and ALS densities and UAS-SfM and HMLS densities were moderately correlated with ALS densities. Including field-measured plot-level canopy base height (CBH) improved most correlations at medium and high CBH, especially those including UAS-SfM data. The most significant generalized linear model to predict RdNBR included interactions between CBH and ladder fuel densities at specific 1-m stratum collected using TLS, ALS, and HMLS approaches (R2 = 0.67, 0.66, and 0.44, respectively). Results imply that remote sensing approaches for ladder fuel density can be used interchangeably in oak woodlands, except UAS-SfM combined with the photo banner. Additionally, TLS, HMLS and ALS approaches can be used with CBH from 1 to 8 m to predict RdNBR. Future work should investigate how ladder fuel densities using our techniques can be validated with destructive sampling and incorporated into predictive models of wildfire severity and fire behavior at varying spatial scales.
Why it matches plant phenotyping methodsTLS、HMLS、UAS-SfM、ALSなど複数のセンシング手法で林分の梯子燃料密度を推定し、手法間比較・相関評価と火災燃焼重症度予測を行っており、植物群落形態の計測手法が中心である。
abstractHere, we compared remote sensing and field-based approaches to estimate ladder fuel density.
Reproduction assets foundThe authors deposited the study's ladder fuel density and related measurements in the USDA FS Research Data Archive (DOI 10.2737/RDS-2021-0101). The paper also uses publicly available Sonoma County ALS LiDAR data (sonomavegmap.org) as a remote sensing input for its ladder fuel analysis. No author analysis code or modelDataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://doi.org/10.2737/RDS-2021-0101 , FS Data Research Data Archive.Open asset ↗FS Data Research Data Archive · 10.2737/RDS-2021-0101lines:614-643Dataset · publicAirborne laser scanner (ALS) data were downloaded from existing data collected in 2013 for Sonoma County (QL1/2013). The imagery was collected using Leica ALS50 and ALS70 sensors at 5054 m altitude on a Beechcraft Airliner twin turboprop aircraft. These sensors have 1064 nm (NIR) lasers. The maximum RMSE for the georeferencing of this data was 0.2 cm due to the use of 9,685 ground control points ( Watershed Sciences, 2016 ). Data can be found at http://sonomavegmap.org/data-downloads/ .Open asset ↗lines:342-349Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 13 Sept 2026
Field / plotPhotogrammetry / SfM / MVSLiDAR / point cloudRootStem / branch2D/3D reconstructionSkeletonization / topologyArchitecture / morphology / geometry
Living architecture, changing in structure with annual growth, requires precise, regular characterisation. However, its geometric irregularity and topological complexity make documentation using traditional methods difficult and presents challenges in creating useful models for mechanical and physiological analyses. Two kinds of living architecture are examined: historic living root bridges grown in Meghalaya, India, and contemporary 'Baubotanik' structures designed and grown in Germany. These structures exhibit common features, in particular network-like structures of varying complexity that result from inosculations between shoots or roots. As an answer to this modelling challenge, we present the first extensive documentation of living architecture using photogrammetry and a subsequent skeleton extraction workflow that solves two problems related to the anastomoses and varying nearby elements specific to living architecture. Photogrammetry was used as a low cost method, supplying detailed point clouds of the structures' visible surfaces. A workflow based on voxel-thinning (using deletion templates and adjusted p-simplicity criteria) provides efficient, accurate skeletons. A volume reconstruction method is derived from the thinning process. The workflow is assessed on seven characteristics beneficial in representing living architecture in comparison with alternative skeleton extraction methods. The resulting models are ready for use in analytical tools, necessary for functional, responsible design.
Why it matches plant phenotyping methods植物の生体構造をフォトグラメトリで取得し、点群から骨格・体積を再構成するワークフロー自体が中心的な方法開発であり、植物構造の表現・解析に用いるため。
abstractwe present the first extensive documentation of living architecture using photogrammetry and a subsequent skeleton extraction workflow
Reproduction assets foundThe paper's Data availability statement explicitly provides public access to the authors' skeletonisation source code on GitHub and the photogrammetric point clouds (Freiburg pavilion, Ficus joint, Baubotanik joint) on the TUM media repository. Both are paper-specific, public, and actionable.Code · publicThe source code is available at: https://github.com/QiguanShu/skeleton-abstraction-of-point-cloud-by-voxel-thinningOpen asset ↗QiguanShu/skeleton-abstraction-of-point-cloud-by-voxel-thinninglines:141-214Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
Abstract Greenhouse‐based high‐throughput phenotyping (HTP) presents a useful approach for studying novel plant growth‐promoting bacteria (PGPB). Despite the potential of this approach to leverage genetic variability for breeding new maize ( Zea Mays L.) cultivars exhibiting highly stable symbiosis with PGPB, greenhouse‐based HTP platforms are not yet widely used because they are highly expensive; hence, it is challenging to perform HTP studies under a limited budget. In this study, we built a low‐cost greenhouse‐based HTP platform to collect growth‐related image‐derived phenotypes. We assessed 360 inbred maize lines with or without PGPB inoculation under nitrogen‐limited conditions. Plant height, canopy coverage, and canopy volume obtained from photogrammetry were evaluated five times during early maize development. A plant biomass index was constructed as a function of plant height and canopy coverage. Inoculation with PGPB promoted plant growth in early developmental stages. Phenotypic correlations between the image‐derived phenotypes and manual measurements were at least 0.47 in the later stages of plant development. The genomic heritability estimates of the image‐derived phenotypes ranged from 0.23 to 0.54. Moderate‐to‐strong genomic correlations between the plant biomass index and shoot dry mass (0.24–0.47) and between HTP‐based plant height and manually measured plant height (0.55–0.68) across the developmental stages showed the utility of our HTP platform. Collectively, our results demonstrate the usefulness of the low‐cost HTP platform for large‐scale genetic and management studies to capture plant growth.
Why it matches plant phenotyping methods低コストの温室HTPプラットフォームを構築し、画像から植物高・群落被覆・群落体積・バイオマス指標を抽出して手動測定と検証しており、表現型取得法が研究の中心である。
abstractPlant height, canopy coverage, and canopy volume obtained from photogrammetry were evaluated five times during early maize development.
Reproduction assets foundThe paper's Data Availability section states that the image data underlying this greenhouse maize phenotyping study are publicly deposited on Mendeley Data (https://doi.org/10.17632/frsfpgnsyz.1), which is an allowed URL. The genotype data deposit (10.17632/5gvznd2b3n.3) is also mentioned but is genomic/omics data and,Dataset · publicestimate genomic heritability
using the following formula:
ℎ2
𝑔 =
σ2
𝑔
σ2
𝑔+
σ2
𝑒
𝑛𝑟
The estimates of genomic correlation were obtained from
the estimated variance-covariance matrix in the bivariate
Bayesian GBLUP model.
2.9 Data availability
The genotype and image data are available at https://doi.org/10.17632/5gvznd2b3n.3 and https://doi.org/10.17632/frsfpgnsyz.1, respectively.
3 RESULTS
3.1 Image processing and data extraction
A total of 756 plots (plants) in each replication across time
were evaluated during plant development. Each collection of
images took approximately 10 min. The ground resolution of
the orthomosaics was approximately 2.30 mm pix–1, and the
GCP error was approximatOpen asset ↗pdf-raw-page:6 lines:1-131Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 8 Sept 2026
Selection for yield at high planting density has reshaped the leaf canopy of maize, improving photosynthetic productivity in high density settings. Further optimization of canopy architecture may be possible. However, measuring leaf angles, the widely studied component trait of leaf canopy architecture, by hand is a labor and time intensive process. Here, we use multiple, calibrated, 2D images to reconstruct the 3D geometry of individual sorghum plants using a voxel carving based algorithm. Automatic skeletonization and segmentation of these 3D geometries enable quantification of the angle of each leaf for each plant. The resulting measurements are both heritable and correlated with manually collected leaf angles. This automated and scaleable reconstruction approach was employed to measure leaf-by-leaf angles for a population of 366 sorghum plants at multiple time points, resulting in 971 successful reconstructions and 3,376 leaf angle measurements from individual leaves. A genome wide association study conducted using aggregated leaf angle data identified a known large effect leaf angle gene, several previously identified leaf angle QTL from a sorghum NAM population, and novel signals. Genome wide association studies conducted separately for three individual sorghum leaves identified a number of the same signals, a previously unreported signal shared across multiple leaves, and signals near the sorghum orthologs of two maize genes known to influence leaf angle. Automated measurement of individual leaves and mapping variants associated with leaf angle reduce the barriers to engineering ideal canopy architectures in sorghum and other grain crops.
Why it matches plant phenotyping methods3D画像再構成、骨格化、セグメンテーションによりソルガム個葉角度を自動定量する手法が研究の中心であり、手作業測定との検証と大規模適用も行っている。
abstractwe use multiple, calibrated, 2D images to reconstruct the 3D geometry of individual sorghum plants using a voxel carving based algorithm.
Reproduction assets foundThe paper's Data Availability statement provides three public, paper-specific assets: the voxel carving/skeletonization reconstruction code on GitHub, the raw RGB phenotyping images on Zenodo, and the phenotypic data, GWAS result files, and figure code on GitHub.Code · publicThe code for reconstruction and skeletonization is available at GitHub: https://github.com/cropsinsilico/SorghumVoxelCarving .Open asset ↗cropsinsilico/SorghumVoxelCarvinglines:351-493Code · publicThe phenotypic data, GWAS result files and code for main figures are available at GitHub: https://github.com/mtross2/Sorghum-3D-Reconstruction .Open asset ↗mtross2/Sorghum-3D-Reconstructionlines:351-493Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 9 Sept 2026
Growing evaluation in the early stages of crop development can be critical to eventual yield. Point clouds have been used for this purpose in tasks such as detection, characterization, phenotyping, and prediction on different crops with terrestrial mapping platforms based on laser scanning. 3D model generation requires the use of specialized measurement equipment, which limits access to this technology because of their complex and high cost, both hardware elements and data processing software. An unmanned 3D reconstruction mapping system of orchards or small crops has been developed to support the determination of morphological indices, allowing the individual calculation of the height and radius of the canopy of the trees to monitor plant growth. This paper presents the details on each development stage of a low-cost mapping system which integrates an Unmanned Ground Vehicle UGV and a 2D LiDAR to generate 3D point clouds. The sensing system for the data collection was developed from the design in mechanical, electronic, control, and software layers. The validation test was carried out on a citrus crop section by a comparison of distance and canopy height values obtained from our generated point cloud concerning the reference values obtained with a photogrammetry method. A 3D crop map was generated to provide a graphical view of the density of tree canopies in different sections which led to the determination of individual plant characteristics using a Python-assisted tool. Field evaluation results showed plant individual tree height and crown diameter with a root mean square error of around 30.8 and 45.7 cm between point cloud data and reference values.
Why it matches plant phenotyping methods低コストUGV・LiDARによる3D植物計測システムを開発し、樹冠形態指標を抽出・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。
abstractAn unmanned 3D reconstruction mapping system of orchards or small crops has been developed to support the determination of morphological indices, allowing the individual calculation of the height and radius of the canopy of the trees to monitor plant growth.
Reproduction assets foundThe paper's Data Availability Statement provides an authors' public GitHub repository containing their code implementation for the UGV-LiDAR citrus crop mapping/phenotyping system. No separate phenotype dataset or point cloud deposit is stated.Code · publicOur code implementation is available online at https://github.com/HaroldMurcia/miniRover_LiDAR_citrush_crop.git , accessed on 25 November 2021.Open asset ↗HaroldMurcia/miniRover_LiDAR_citrush_croplines:356-358Code / dataset availability confirmedEurope PMC · checked 13 Sept 2026
The PFuji-Size dataset is comprised of a collection of 3D point clouds of Fuji apple trees ( Malus domestica Borkh. cv. Fuji) scanned at different maturity stages and annotated for fruit detection and size estimation. Structure-from-motion and multi-view stereo techniques were used to generate the 3D point clouds of 6 complete Fuji apple trees containing a total of 615 apples. The resulting point clouds were 3D segmented by identifying the 3D points corresponding to each apple (3D instance segmentation), obtaining a single point cloud for each apple. All segmented apples were labelled with ground truth diameter annotations. Since the data was acquired in field conditions and at different maturity stages, the set includes different fruit diameters -from 26.9 mm to 94.8 mm- and different fruit occlusion percentages due to foliage. In addition, 25 apples were photographed 360° in laboratory conditions, obtaining high resolution 3D point clouds of this sub-set. To the best of the authors' knowledge, this is the first publicly available dataset for apple size estimation in field conditions. This dataset was used to evaluate different fruit size estimation methods in the research article titled "In-field apple size estimation using photogrammetry-derived 3D point clouds: comparison of 4 different methods considering fruit occlusion" (Gené-Mola et al., 2021).
Why it matches plant phenotyping methodsリンゴ果実の3D画像・点群から果径を推定するための公開データセットであり、アノテーション、3Dセグメンテーション、サイズ推定評価が中心的な方法論的貢献である。
abstractThe PFuji-Size dataset is comprised of a collection of 3D point clouds of Fuji apple trees ( Malus domestica Borkh. cv. Fuji) scanned at different maturity stages and annotated for fruit detection and size estimation.
Reproduction assets foundThe paper is a Data in Brief article describing the PFuji-Size dataset (raw images, 3D tree point clouds, apple segmentation masks, diameter/centre annotations), publicly deposited in Dataverse (CSUC) with DOI 10.34810/data141 and a direct URL. This is a paper-specific, public, actionable phenotyping dataset directly.Dataset · publicData accessibility
Repository name: Dataverse
Data identification number: https://doi.org/10.34810/data141
DOI: https://doi.org/10.34810/data141
Direct URL to data: https://dataverse.csuc.cat/dataset.xhtml?persistentId=doi:10.34810/data141Open asset ↗Dataverse · doi:10.34810/data141lines:54-87Code / dataset availability confirmedOpenAlex · checked 13 Sept 2026
Unmanned aerial vehicle (UAV) remote sensing technology can be used for fast and efficient monitoring of plant diseases and pests, but these techniques are qualitative expressions of plant diseases. However, the yellow leaf disease of arecanut in Hainan Province is similar to a plague, with an incidence rate of up to 90% in severely affected areas, and a qualitative expression is not conducive to the assessment of its severity and yield. Additionally, there exists a clear correlation between the damage caused by plant diseases and pests and the change in the living vegetation volume (LVV). However, the correlation between the severity of the yellow leaf disease of arecanut and LVV must be demonstrated through research. Therefore, this study aims to apply the multispectral data obtained by the UAV along with the high-resolution UAV remote sensing images to obtain five vegetation indexes such as the normalized difference vegetation index (NDVI), optimized soil adjusted vegetation index (OSAVI), leaf chlorophyll index (LCI), green normalized difference vegetation index (GNDVI), and normalized difference red edge (NDRE) index, and establish five algorithm models such as the back-propagation neural network (BPNN), decision tree, naïve Bayes, support vector machine (SVM), and k-nearest-neighbor classification to determine the severity of the yellow leaf disease of arecanut, which is expressed by the proportion of the yellowing area of a single areca crown (in percentage). The traditional qualitative expression of this disease is transformed into the quantitative expression of the yellow leaf disease of arecanut per plant. The results demonstrate that the classification accuracy of the test set of the BPNN algorithm and SVM algorithm is the highest, at 86.57% and 86.30%, respectively. Additionally, the UAV structure from motion technology is used to measure the LVV of a single areca tree and establish a model of the correlation between the LVV and the severity of the yellow leaf disease of arecanut. The results show that the relative root mean square error is between 34.763% and 39.324%. This study presents the novel quantitative expression of the severity of the yellow leaf disease of arecanut, along with the correlation between the LVV of areca and the severity of the yellow leaf disease of arecanut. Significant development is expected in the degree of integration of multispectral software and hardware, observation accuracy, and ease of use of UAVs owing to the rapid progress of spectral sensing technology and the image processing and analysis algorithms.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像とSfMにより、植物体ごとの病害重症度と生体植生量を定量推定する手法を開発・評価しており、表現型取得が研究の中心である。
abstractestablish five algorithm models such as the back-propagation neural network (BPNN), decision tree, naïve Bayes, support vector machine (SVM), and k-nearest-neighbor classification to determine the severity of the yellow leaf disease of arecanut
Reproduction assets foundThe paper's Data Availability Statement explicitly points to a publicly accessible dataset at the author's website (zixuanqiu.com), matching an allowed URL. The study's UAV multispectral imagery, vegetation index data, and 11,400 sample-point annotations for arecanut yellow leaf disease are the paper-specific phenotypcDataset · publicData Availability Statement: Data available in a publicly accessible repository that does not issue
DOIs Publicly available datasets were analyzed in this study. This data can be found here:
http://www.zixuanqiu.com/nd.jsp?id=39#_np=110_649 (accessed on 20 October 2021).Open asset ↗zixuanqiu.compdf-page:19 lines:1-58Code / dataset availability confirmedbioRxiv · Europe PMC · OpenAlex · Crossref · checked 15 Sept 2026
Manual phenotyping of tomato plants is time consuming and labor intensive. Due to the lack of low-cost and open-access 3D phenotyping tools, the dynamic 3D growth of tomato plants during all growth stages has not been fully explored. In this study, based on the 3D structural data points generated by employing structures from motion algorithms on multiple-view images, we proposed a dynamic 3D phenotyping pipeline, 4DPhenoMVS, to calculate and analyze 14 phenotypic traits of tomato plants covering the whole life cycle. The results showed that the R2 values between the phenotypic traits and the manual measurements stem length, plant height, and internode length were more than 0.8. In addition, to investigate the environmental influence on tomato plant growth and yield in the greenhouse, eight tomato plants were chosen and phenotyped during 7 growth stages according to different light intensities, temperatures, and humidities. The results showed that stronger light intensity and moderate temperature and humidity contribute to a higher growth rate and higher yield. In conclusion, we developed a low-cost and open-access 3D phenotyping pipeline for tomato plants, which will benefit tomato breeding, cultivation research, and functional genomics in the future. HighlightsBased on the 3D structural data points generated by employing structures from motion algorithms on multiple-view images, we developed a low-cost and open-access 3D phenotyping tool for tomato plants during all growth stages.
Why it matches plant phenotyping methods低コストの多視点画像・3D再構成によるトマト表現型抽出パイプラインを開発し、複数形質を手測定と検証しており、方法が研究の中心である。
abstractwe proposed a dynamic 3D phenotyping pipeline, 4DPhenoMVS, to calculate and analyze 14 phenotypic traits of tomato plants covering the whole life cycle.
Reproduction assets foundThe paper's Data Availability statement provides a public URL for downloading all phenotypic data and multiview tomato images used in the 4DPhenoMVS pipeline. Source code is referenced only via Supplementary Note S1 with no authors' public URL in the supplied text, so it is not included as an actionable asset.Dataset · publicng Agricultural University and
478
Shenzhen Institute of agricultural genomics (SZYJY2021005, SZYJY2021007). We
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thanked Harvest-Code Technology (Nanjing) Ltd. provided the materials and
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experimental resources.
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Data Availability
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All the phenotypic data and images can be viewed and downloaded via the link
484
(http://plantphenomics.hzau.edu.cn/download_checkiflogin_en.action).485
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References
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Aguilar MA, Pozo JL, Aguilar FJ, Sanchez-Hermosilla J, Negreiros J. 2008. 3d
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Surface Modelling of Tomato Plants Using Close-Range Photogrammetry. Archives
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of Photogrammetry, Remote Sensing and Spatial 37, B5, 139-144.
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An N, Welch SM, Markelz RJC, Baker RL, Palmer CM, Open asset ↗plantphenomics.hzau.edu.cnpdf-raw-page:24 lines:1-78Code / dataset availability confirmedOpenAlex · checked 13 Sept 2026
This work presents an advanced photogrammetric pipeline for inspecting apple trees in the field, automatically detecting fruits from videos and quantifying their size and number. The proposed approach is intended to facilitate and accelerate farmers’ and agronomists’ fieldwork, making apple measurements more objective and giving a more extended collection of apples measured in the field while also estimating harvesting/apple-picking dates. In order to do this rapidly and automatically, we propose a pipeline that uses smartphone-based videos and combines photogrammetry, deep learning and geometric algorithms. Synthetic, laboratory and on-field experiments demonstrate the accuracy of the results and the potential of the proposed method. Acquired data, labelled images, code and network weights, are available at 3DOM-FBK GitHub account.
Why it matches plant phenotyping methodsリンゴ果実の数とサイズを動画から自動抽出するフォトグラメトリ手法を開発し、実験で精度を検証しており、植物フェノタイピング手法が中心である。
abstractThis work presents an advanced photogrammetric pipeline for inspecting apple trees in the field, automatically detecting fruits from videos and quantifying their size and number.
Reproduction assets foundThe authors explicitly state that acquired data, labelled images, code, and network weights for the apple phenotyping pipeline are publicly available on the 3DOM-FBK GitHub account, with a concrete URL given in reference [56]. This is a paper-specific, public, actionable asset covering the Mask R-CNN retraining code/权重Code · publicData Availability Statement: Data acquired and used in the presented experiments, labelled im-
ages, code, and network weights, are available to the scientific community at 3DOM-FBK-GitHub
[56].Open asset ↗pdf-page:16 lines:1-58Dataset · publicAcquired
data, labelled images, code and network weights, are available at 3DOM-FBK GitHub account.Open asset ↗pdf-page:1 lines:1-67Code / dataset availability confirmedCrossref · Europe PMC · OpenAlex · checked 9 Sept 2026
Abstract Background Being able to accurately assess the 3D architecture of plant canopies can allow us to better estimate plant productivity and improve our understanding of underlying plant processes. This is especially true if we can monitor these traits across plant development. Photogrammetry techniques, such as structure from motion, have been shown to provide accurate 3D reconstructions of monocot crop species such as wheat and rice, yet there has been little success reconstructing crop species with smaller leaves and more complex branching architectures, such as chickpea. Results In this work, we developed a low-cost 3D scanner and used an open-source data processing pipeline to assess the 3D structure of individual chickpea plants. The imaging system we developed consists of a user programmable turntable and three cameras that automatically captures 120 images of each plant and offloads these to a computer for processing. The capture process takes 5–10 min for each plant and the majority of the reconstruction process on a Windows PC is automated. Plant height and total plant surface area were validated against “ground truth” measurements, producing R 2 > 0.99 and a mean absolute percentage error Conclusions Our results show that it is possible to use low-cost photogrammetry techniques to accurately reconstruct individual chickpea plants, a crop with a complex architecture consisting of many small leaves and a highly branching structure. We hope that our use of open-source software and low-cost hardware will encourage others to use this promising technique for more architecturally complex species.
Why it matches plant phenotyping methodsヒヨコマメ個体の3D形態を取得する低コスト撮像システムとオープンソース解析パイプラインを開発し、草丈・表面積を基準値で検証しており、フェノタイピング手法が中心である。
abstractIn this work, we developed a low-cost 3D scanner and used an open-source data processing pipeline to assess the 3D structure of individual chickpea plants.
Reproduction assets foundThe authors deposited the paper's 3D point clouds and meshed chickpea models in an open-access Zenodo repository (DOI 10.5281/zenodo.4018242). Processing scripts are only included as article additional files, and the source images are available only on request from the corresponding author.Dataset · publicThe dataset supporting the conclusions of this article (3D point clouds and meshed models) are available in an open-access Zenodo repository, https://doi.org/10.5281/zenodo.4018242 .Open asset ↗Zenodo · 10.5281/zenodo.4018242lines:149-193Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · bioRxiv · checked 15 Sept 2026
Abstract Greenhouse-based high-throughput phenotyping (HTP) presents a useful approach for studying novel plant growth-promoting bacteria (PGPB). Despite the potential of this approach to leverage genetic variability for breeding new maize cultivars exhibiting highly stable symbiosis with PGPB, greenhouse-based HTP platforms are not yet widely used because they are highly expensive; hence, it is challenging to perform HTP studies under a limited budget. In this study, we built a low-cost greenhouse-based HTP platform to collect growth-related image-derived phenotypes. We assessed 360 inbred maize lines with or without PGPB inoculation under nitrogen-limited conditions. Plant height, canopy coverage, and canopy volume obtained from photogrammetry were evaluated five times during early maize development. A plant biomass index was constructed as a function of plant height and canopy coverage. Inoculation with PGPB promoted plant growth. Phenotypic correlations between the image-derived phenotypes and manual measurements were at least 0.6. The genomic heritability estimates of the image-derived phenotypes ranged from 0.23 to 0.54. Moderate-to-strong genomic correlations between the plant biomass index and shoot dry mass (0.24–0.47) and between HTP-based plant height and manually measured plant height (0.55–0.68) across the developmental stages showed the utility of our HTP platform. Collectively, our results demonstrate the usefulness of the low-cost HTP platform for large-scale genetic and management studies to capture plant growth. Core ideas A low-cost greenhouse-based HTP platform was developed. Image-derived phenotypes presented moderate to high genomic heritabilities and correlations. Plant growth-promoting bacteria can improve plant resilience under nitrogen-limited conditions.
Why it matches plant phenotyping methods低コスト温室HTPプラットフォームを開発し、画像から植物高・キャノピー被覆・体積などの形質を抽出・検証しており、表現型取得手法が研究の中心である。
abstractIn this study, we built a low-cost greenhouse-based HTP platform to collect growth-related image-derived phenotypes.
Reproduction assets foundThe article describes a low-cost greenhouse HTP platform and image-derived phenotyping of a 360-line tropical maize association panel under PGPB inoculation. The only paper-specific public asset explicitly referenced is a Mendeley Data deposit containing information about the maize panel used in the experiment. No phenDataset · publiclines was used to study the response
172 to PGPB. Of these, 179 inbred lines were from the Luiz de Queiroz College of Agriculture-
173 University of Sao Paulo (ESALQ-USP) and 181 were from the Instituto de Desenvolvimento
174 Rural do Paraná (IAPAR). More information about this panel is available on the Mendeley
175 platform (https://data.mendeley.com/datasets/5gvznd2b3n).
176 The inbred lines were evaluated under two managements: with (B+) and without (B-)
177 PGPB inoculation under nitrogen stress. The B+ management consisted of a synthetic pop-
178 ulation of four PGPB. Bacillus thuringiensis RZ2MS9, Delftia sp. RZ4MS18 (Batista et al.,
179 2018, 2021), Pantoea agglomerans 33.1 (Quecine Open asset ↗Mendeley · 5gvznd2b3npdf-layout-page:8 lines:1-37Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 9 Sept 2026
Non-forest ecosystems, dominated by shrubs, grasses and herbaceous plants, provide ecosystem services including carbon sequestration and forage for grazing, and are highly sensitive to climatic changes. Yet these ecosystems are poorly represented in remotely sensed biomass products and are undersampled by in situ monitoring. Current global change threats emphasize the need for new tools to capture biomass change in non-forest ecosystems at appropriate scales. Here we developed and deployed a new protocol for photogrammetric height using unoccupied aerial vehicle (UAV) images to test its capability for delivering standardized measurements of biomass across a globally distributed field experiment. We assessed whether canopy height inferred from UAV photogrammetry allows the prediction of aboveground biomass (AGB) across low-stature plant species by conducting 38 photogrammetric surveys over 741 harvested plots to sample 50 species. We found mean canopy height was strongly predictive of AGB across species, with a median adjusted R 2 of 0.87 (ranging from 0.46 to 0.99) and median prediction error from leave-one-out cross-validation of 3.9%. Biomass per-unit-of-height was similar within but different among, plant functional types. We found that photogrammetric reconstructions of canopy height were sensitive to wind speed but not sun elevation during surveys. We demonstrated that our photogrammetric approach produced generalizable measurements across growth forms and environmental settings and yielded accuracies as good as those obtained from in situ approaches. We demonstrate that using a standardized approach for UAV photogrammetry can deliver accurate AGB estimates across a wide range of dynamic and heterogeneous ecosystems. Many academic and land management institutions have the technical capacity to deploy these approaches over extents of 1-10 ha -1 . Photogrammetric approaches could provide much-needed information required to calibrate and validate the vegetation models and satellite-derived biomass products that are essential to understand vulnerable and understudied non-forested ecosystems around the globe.
Why it matches plant phenotyping methodsUAV画像からキャノピー高を推定し、地上部バイオマスを予測するフォトグラメトリ手法の開発・検証・標準化が研究の中心であるため。
abstractHere we developed and deployed a new protocol for photogrammetric height using unoccupied aerial vehicle (UAV) images to test its capability for delivering standardized measurements of biomass across a globally distributed field experiment.
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits the study's UAV aerial images, marker/plot coordinates, and harvested dry biomass weights at the NERC Environmental Information Data Centre, and the photogrammetric processing and statistical analysis code at Zenodo. Both are paper-specific, public, and have作者Dataset · publicThe data collected for this publication, including aerial images, marker and plot coordinates and dry sample weights, as well as site and survey metadata, are available from the NERC Environmental Information Data Centre < https://doi.org/10.5285/1ec13364‐cbc6‐4ab5‐a147‐45a103853424 >.Open asset ↗NERC Environmental Information Data Centre · 10.5285/1ec13364‐cbc6‐4ab5‐a147‐45a103853424lines:222-246Code · publicCode for photogrammetric processing and statistical analysis is available at Zenodo < https://doi.org/10.5281/zenodo.4783021 >Open asset ↗Zenodo · 10.5281/zenodo.4783021lines:222-246Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Field / plotPhotogrammetry / SfM / MVSCalibration / preprocessing
Unmanned aerial vehicle (UAV) and structure from motion (SfM) photogrammetry techniques are widely used for field-based, high-throughput plant phenotyping nowadays, but some of the intermediate processes throughout the workflow remain manual. For example, geographic information system (GIS) software is used to manually assess the 2D/3D field reconstruction quality and cropping region of interests (ROIs) from the whole field. In addition, extracting phenotypic traits from raw UAV images is more competitive than directly from the digital orthomosaic (DOM). Currently, no easy-to-use tools are available to implement previous tasks for commonly used commercial SfM software, such as Pix4D and Agisoft Metashape. Hence, an open source software package called easy intermediate data processor (EasyIDP; MIT license) was developed to decrease the workload in intermediate data processing mentioned above. The functions of the proposed package include (1) an ROI cropping module, assisting in reconstruction quality assessment and cropping ROIs from the whole field, and (2) an ROI reversing module, projecting ROIs to relative raw images. The result showed that both cropping and reversing modules work as expected. Moreover, the effects of ROI height selection and reversed ROI position on raw images to reverse calculation were discussed. This tool shows great potential for decreasing workload in data annotation for machine learning applications.
Why it matches plant phenotyping methodsUAV植物フェノタイピングの中間データ処理を担うオープンソースPythonパッケージを開発しており、ROI抽出・逆投影などの画像解析ワークフローが中心である。
abstractThe functions of the proposed package include (1) an ROI cropping module, assisting in reconstruction quality assessment and cropping ROIs from the whole field, and (2) an ROI reversing module, projecting ROIs to relative raw images.
Reproduction assets foundThe paper's own analysis software, the EasyIDP Python package implementing the ROI cropping and reversing modules used for the UAV phenotyping measurements, is explicitly stated to be publicly downloadable under an MIT license from the authors' GitHub repository.Code · publicest
IoU Euclidean distance raw image was selected to mark the manual reference, and the
overall trend of the indicators was simply analyzed.
2.6. Implementation
The cropping and reversing modules mentioned above were implemented into a Py-
thon package called EasyIDP using the MIT license. The source code can be downloaded
from https://github.com/HowcanoeWang/EasyIDP. For specific package documentation,
please refer to https://github.com/HowcanoeWang/EasyIDP/wiki. Although the source
codes were cross-platform owing to the characteristics of the Python language, they were
programmed and tested on a Windows 10 64-bit platform and an Intel CPU with a math
kernel library (MKL). More than 8 GB ROpen asset ↗HowcanoeWang/EasyIDPpdf-raw-page:8 lines:1-38Code / dataset availability confirmedOpenAlex · Crossref · checked 14 Sept 2026
Abstract High‐throughput 3D phenotyping is a rapidly emerging field that has widespread application for measurement of individual plants. Despite this, high‐throughput plant phenotyping is rarely used in ecological studies due to financial and logistical limitations. We introduce EasyDCP, a Python package for 3D phenotyping, which uses photogrammetry to automatically reconstruct 3D point clouds of individuals within populations of container plants and output phenotypic trait data. Here we give instructions for the imaging setup and the required hardware, which is minimal and do‐it‐yourself, and introduce the functionality and workflow of EasyDCP. We compared the performance of EasyDCP against a high‐end commercial laser scanner for the acquisition of plant height and projected leaf area. Both tools had strong correlations with ground truth measurement, and plant height measurements were more accurate using EasyDCP (plant height: EasyDCP r 2 = 0.96, Laser r 2 = 0.86; projected leaf area: EasyDCP r 2 = 0.96, Laser r 2 = 0.96). EasyDCP is an open‐source software tool to measure phenotypic traits of container plants with high‐throughput and low labour and financial costs.
Why it matches plant phenotyping methodsEasyDCPは、フォトグラメトリによる3D植物表現型取得と自動形質抽出のためのソフトウェア・撮像ワークフローを開発し、レーザースキャナおよび実測値と比較検証しており、方法が研究の中心です。
abstractWe introduce EasyDCP, a Python package for 3D phenotyping, which uses photogrammetry to automatically reconstruct 3D point clouds of individuals within populations of container plants and output phenotypic trait data.
Reproduction assets foundThe paper's EasyDCP source code is publicly available on GitHub, and the performance-test data (source images, point clouds, trait data, R files) plus code and documentation are archived on Zenodo.Code · public| 1681
Methods in Ecology and Evolu on
FELDMAN et al.
EasyDCP_Creation (Section 2.2), which creates a 3D point
cloud from 2D images; and EasyDCP_Analysis (Section 2.3),
which analyses that point cloud and performs trait calcula-
tion. EasyDCP source code and documentation are available
on GitHub (https://github.com/UTokyo-FieldPhenomics-Lab/EasyDCP).2.1 | Image acquisition
Plants must be imaged prior to EasyDCP measurement, and the
image acquisition area can be set up according to the user's needs
(Figure 2a,b). The image acquisition area should have as little in-
clination as possible. One printed target page (.pdf provided with
the software) must be placed in a corner oOpen asset ↗UTokyo-FieldPhenomics-Lab/EasyDCPpdf-raw-page:3 lines:1-111Dataset · public.
PEER REVIEW
The peer review history for this article is available at https://publo
ns.
com/publon/10.1111/2041-210X.13645.
DATA AVAILABILITY STATEMENT
Data from the performance test (source images, point clouds, trait
data and R files), EasyDCP source code, example scripts and detailed
documentation are archived using Zenodo https://doi.org/10.5281/zenodo.4756537 (Feldman et al., 2021).
ORCID
Alexander Feldman https://orcid.org/0000-0002-1162-5917
Haozhou Wang https://orcid.org/0000-0001-6135-402X
Yuya Fukano https://orcid.org/0000-0001-9057-4742
Yoichiro Kato https://orcid.org/0000-0002-7131-0220
Seishi Ninomiya https://orcid.org/0000-0002-2123-4354
Wei Guo https://orcid.org/0000-0002-Open asset ↗Zenodo · 10.5281/zenodo.4756537pdf-raw-page:6 lines:1-102Code / dataset availability confirmedOpenAlex · checked 13 Sept 2026
Estimation of urban tree canopy parameters plays a crucial role in urban forest management. Unmanned aerial vehicles (UAV) have been widely used for many applications particularly forestry mapping. UAV-derived images, captured by an onboard camera, provide a means to produce 3D point clouds using photogrammetric mapping. Similarly, small UAV mounted light detection and ranging (LiDAR) sensors can also provide very dense 3D point clouds. While point clouds derived from both photogrammetric and LiDAR sensors can allow the accurate estimation of critical tree canopy parameters, so far a comparison of both techniques is missing. Point clouds derived from these sources vary according to differences in data collection and processing, a detailed comparison of point clouds in terms of accuracy and completeness, in relation to tree canopy parameters using point clouds is necessary. In this research, point clouds produced by UAV-photogrammetry and -LiDAR over an urban park along with the estimated tree canopy parameters are compared, and results are presented. The results show that UAV-photogrammetry and -LiDAR point clouds are highly correlated with R2 of 99.54% and the estimated tree canopy parameters are correlated with R2 of higher than 95%.
Why it matches plant phenotyping methodsUAVフォトグラメトリとLiDARによる樹冠パラメータ推定を比較・精度評価しており、植物形態形質の取得手法が研究の中心である。
abstracta detailed comparison of point clouds in terms of accuracy and completeness, in relation to tree canopy parameters using point clouds is necessary
Reproduction assets foundThe authors state that the UAV-LiDAR and photogrammetric point clouds used for tree canopy parameter estimation are freely available as Supplementary Materials via an MDPI link, making the paper's core phenotyping sensor data (3D point clouds) publicly accessible.Dataset · publicThe LiDAR and photogrammetric point clouds used in this research
are freely available (https://susy.mdpi.com/user/manuscripts/displayFile/d71a32682356d1cece4c0Open asset ↗pdf-page:14 lines:1-60Code / dataset availability confirmedCrossref · checked 13 Sept 2026
The ability to quantify changes in the structural complexity of reefs and individual coral colonies that build them is vital to understanding, managing, and restoring the function of these ecosystems. However, traditional methods for quantifying coral growth in situ fail to accurately quantify the diversity of morphologies observed both among and within species that contribute to topographical complexity. Three-dimensional (3D) photogrammetry has emerged as a powerful tool for the quantification of reefscape complexity but has yet to be broadly adopted for quantifying the growth and morphology of individual coral colonies. Here we debut a high-throughput method for colony-level 3D photogrammetry and apply this technique to explore the relationship between linear extension and other growth metrics in Acropora cervicornis . We fate-tracked 156 individual coral transplants to test whether initial growth can be used to predict subsequent patterns of growth. We generated photographic series of fragments in a restoration nursery immediately before transplanting to natural reef sites and re-photographed coral at 6 months and 1 year post-transplantation. Photosets were used to build 3D models with Agisoft Metashape, which was automated to run on a high-performance computing system using a custom script to serially process models without the need for additional user input. Coral models were phenotyped in MeshLab to obtain measures of total linear extension (TLE), surface area, volume, and volume of interstitial space (i.e., the space between branches). 3D-model based measures of TLE were highly similar to by-hand measurements made in the field ( r = 0.98), demonstrating that this method is compatible with established techniques without additional in water effort. However, we identified an allometric relationship between the change in TLE and the volume of interstitial space, indicating that growth in higher order traits is not necessarily a linear function of growth in branch length. Additionally, relationships among growth measures weakened when comparisons were made across time points, implying that the use of early growth to predict future performance is limited. Taken together, results show that 3D photogrammetry is an information rich method for quantifying colony-level growth and its application can help address contemporary questions in coral biology.
Why it matches plant phenotyping methodsサンゴ個体群の3Dフォトグラメトリによる形態・成長形質取得法を開発し、自動処理と既存手法との比較検証を行っているため、植物体（サンゴ）の表現型計測法が中心である。
abstractHere we debut a high-throughput method for colony-level 3D photogrammetry
Reproduction assets foundThe paper's phenotype dataset (TLE, SA, V, Vinter for 156 A. cervicornis colonies) is publicly deposited in the authors' GitHub repository Frontiers3Dmorphology, and the custom Metashape automation scripts are in Coral3DPhotogram; both are paper-specific, public, and actionable.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://github.com/wyattmillion/Frontiers3Dmorphology .Open asset ↗Frontiers3Dmorphologylines:321-372Code · publicAll bioinformatic scripts used to run Metashape on the command line can be found at https://github.com/wyattmillion/Coral3DPhotogram .Open asset ↗Coral3DPhotogramlines:271-276Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
One of the objectives of many studies conducted by breeding programs is to characterize and select rootstocks well-adapted to drought conditions. In recent years, field high-throughput phenotyping methods have been developed to characterize plant traits and to identify the most water use efficient varieties and rootstocks. However, none of these studies have been able to quantify the behavior of crop evapotranspiration in almond rootstocks under different water regimes. In this study, remote sensing phenotyping methods were used to assess the evapotranspiration of almond cv. “Marinada” grafted onto a rootstock collection. In particular, the two-source energy balance and Shuttleworth and Wallace models were used to, respectively, estimate the actual and potential evapotranspiration of almonds grafted onto 10 rootstock under three different irrigation treatments. For this purpose, three flights were conducted during the 2018 and 2019 growing seasons with an aircraft equipped with a thermal and multispectral camera. Stem water potential (Ψstem) was also measured concomitant to image acquisition. Biophysical traits of the vegetation were firstly assessed through photogrammetry techniques, spectral vegetation indices and the radiative transfer model PROSAIL. The estimates of canopy height, leaf area index and daily fraction of intercepted radiation had root mean square errors of 0.57 m, 0.24 m m–1 and 0.07%, respectively. Findings of this study showed significant differences between rootstocks in all of the evaluated parameters. Cadaman® and Garnem® had the highest canopy vigor traits, evapotranspiration, Ψstem and kernel yield. In contrast, Rootpac® 20 and Rootpac® R had the lowest values of the same parameters, suggesting that this was due to an incompatibility between plum-almond species or to a lower water absorption capability of the rooting system. Among the rootstocks with medium canopy vigor, Adesoto and IRTA 1 had a lower evapotranspiration than Rootpac® 40 and Ishtara®. Water productivity (WP) (kg kernel/mm water evapotranspired) tended to decrease with Ψstem, mainly in 2018. Cadaman® and Garnem® had the highest WP, followed by INRA GF-677, IRTA 1, IRTA 2, and Rootpac® 40. Despite the low Ψstem of Rootpac® R, the WP of this rootstock was also high.
Why it matches plant phenotyping methodsリモートセンシングによる植物形質・蒸発散の推定が研究の中心で、熱・マルチスペクトル画像、フォトグラメトリ、モデルを用いた推定精度も評価している。
abstractIn recent years, field high-throughput phenotyping methods have been developed to characterize plant traits and to identify the most water use efficient varieties and rootstocks.
Reproduction assets foundThe paper's data availability statement points to the author's public GitHub profile (Héctor Nieto, pyTSEB developer) as the location of the datasets analyzed, which include the remote sensing phenotyping measurements (thermal/multispectral imagery-derived ETa, LAI, fiPAR, Ψstem relationships) and the TSEB-based model.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://github.com/hectornieto .Open asset ↗hectornietolines:1046-1107Code / dataset availability confirmedOpenAlex · Crossref · checked 13 Sept 2026
As a key canopy structure parameter, the estimation method of the Leaf Area Index (LAI) has always attracted attention. To explore a potential method to estimate forest LAI from 3D point cloud at low cost, we took photos from different angles of the drone and set five schemes (O (0°), T15 (15°), T30 (30°), OT15 (0° and 15°) and OT30 (0° and 30°)), which were used to reconstruct 3D point cloud of forest canopy based on photogrammetry. Subsequently, the LAI values and the leaf area distribution in the vertical direction derived from five schemes were calculated based on the voxelized model. Our results show that the serious lack of leaf area in the middle and lower layers determines that the LAI estimate of O is inaccurate. For oblique photogrammetry, schemes with 30° photos always provided better LAI estimates than schemes with 15° photos (T30 better than T15, OT30 better than OT15), mainly reflected in the lower part of the canopy, which is particularly obvious in low-LAI areas. The overall structure of the single-tilt angle scheme (T15, T30) was relatively complete, but the rough point cloud details could not reflect the actual situation of LAI well. Multi-angle schemes (OT15, OT30) provided excellent leaf area estimation (OT15: R2 = 0.8225, RMSE = 0.3334 m2/m2; OT30: R2 = 0.9119, RMSE = 0.1790 m2/m2). OT30 provided the best LAI estimation accuracy at a sub-voxel size of 0.09 m and the best checkpoint accuracy (OT30: RMSE [H] = 0.2917 m, RMSE [V] = 0.1797 m). The results highlight that coupling oblique photography and nadiral photography can be an effective solution to estimate forest LAI.
Why it matches plant phenotyping methodsUAV斜め写真測量と3D点群・ボクセル解析を用いて森林キャノピーのLAIを推定する手法を開発・比較検証しており、植物形態形質の取得方法が研究の中心である。
abstractTo explore a potential method to estimate forest LAI from 3D point cloud at low cost, we took photos from different angles of the drone and set five schemes
Reproduction assets foundThe paper's authors publicly released the voxelization/LAI extraction code on GitHub; phenotype data (UAV images, point clouds, LAI-2200 measurements) are only available upon request.Code · publicData Availability Statement: The source codes developed in this study were donated to GitHub
(https://github.com/TOTOROLLC/Forest‐Stand‐LAI‐Remote‐Sensing‐Retrieval‐Based‐on‐Photo‐
grammetry (accessed on 7 January 2021)). And the data used to support the findings of this study
are available from the corresponding author upon request.Open asset ↗https://github.com/TOTOROLLC/Forest‐Stand‐LAI‐Remote‐Sensing‐Retrieval‐Based‐on‐Photo‐pdf-page:15 lines:1-59Code / dataset availability confirmedEurope PMC · OpenAlex · checked 13 Sept 2026
The uptake of technologies such as airborne laser scanning (ALS) and more recently digital aerial photogrammetry (DAP) enable the characterization of 3-dimensional (3D) forest structure. These forest structural attributes are widely applied in the development of modern enhanced forest inventories. As an alternative to extensive ALS or DAP based forest inventories, regional forest attribute maps can be built from relationships between ALS or DAP and wall-to-wall satellite data products. To date, a number of different approaches exist, with varying code implementations using different programming environments and tailored to specific needs. With the motivation for open, simple and modern software, we present FOSTER (Forest Structure Extrapolation in R), a versatile and computationally efficient framework for modeling and imputation of 3D forest attributes. FOSTER derives spectral trends in remote sensing time series, implements a structurally guided sampling approach to sample these often spatially auto correlated datasets, to then allow a modelling approach (currently k-NN imputation) to extrapolate these 3D forest structure measures. The k-NN imputation approach that FOSTER implements has a number of benefits over conventional regression based approaches including lower bias and reduced over fitting. This paper provides an overview of the general framework followed by a demonstration of the performance and outputs of FOSTER. Two ALS-derived variables, the 95th percentile of first returns height (elev_p95) and canopy cover above mean height (cover), were imputed over a research forest in British Columbia, Canada with relative RMSE of 18.5% and 11.4% and relative bias of -0.6% and 1.4% respectively. The processing sequence developed within FOSTER represents an innovative and versatile framework that should be useful to researchers and managers alike looking to make forest management decisions over entire forest estates.
Why it matches plant phenotyping methods森林の3D構造属性（樹冠高・樹冠被覆）をリモートセンシングから推定するRソフトウェアと処理フレームワークが研究の中心であり、植物キャノピー形質の計測・推定手法に該当する。
abstractwe present FOSTER (Forest Structure Extrapolation in R), a versatile and computationally efficient framework for modeling and imputation of 3D forest attributes.
Reproduction assets foundThe paper's authors publicly released the FOSTER R package source code (the computational framework implementing the paper's k-NN forest structure imputation analysis) on GitHub and CRAN, as stated in the Data Availability section.Code · publicFOSTER source code is available from GitHub ( https://github.com/mqueinnec/foster ) and also hosted on the Comprehensive R Archive Network (CRAN; https://cran.r-project.org/package=foster ).Open asset ↗mqueinnec/fosterlines:29-34Code · publicFOSTER source code is available from GitHub ( https://github.com/mqueinnec/foster ) and also hosted on the Comprehensive R Archive Network (CRAN; https://cran.r-project.org/package=foster ).Open asset ↗fosterlines:29-34Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
Abstract Background:The use of 3D based high-throughput phenotyping improves theefficiency of crop management and monitoring practices. Thestructure-from-motion and multi-view stereo photogrammetry (SfM-MVS)technique, applicable to common RGB digital cameras, has been widely used forthis and can be implemented by many commercial and open-source tools. Byusing such tools, several outputs such as digital orthophoto map (DOM), digitalsurface model (DSM), and point cloud data (PCD) can be generated. However,there is a gap between these outputs and the final 3D plant phenotyping. Forexample, calculating plant height and canopy ground cover requires thesegmentation of each plot from the whole DOM, DSM, or original image. Theseintermediate processes are time-consuming, and to the best of our knowledge,there are no easy-to-use alternatives currently available. Results: In this study, a software package called EasyIDP (easy intermediatedata processor) was developed to link the products of SfM-MVS techniques with3D based plant phenotyping. A lotus (Nelumbo nucifera) breeding field was usedto demonstrate the following points: 1) clipping (segmenting) SfM-MVS productsaccording to a given plot boundary or region of interest (ROI); 2) transformingthe ROI of the SfM-MVS products into high-quality raw images to assist inobject detection; and 3) evaluating the accuracy of the previous transformationusing manual annotation. Conclusions: The proposed intermediate data processing tool showed anacceptable accuracy and potential to process the products from SfM-MVStechniques. By using the EasyIDP, a bridge between SfM-MVS products andplant phenotyping was conveniently achieved.
Why it matches plant phenotyping methodsEasyIDPはSfM-MVS生成物を植物表現型抽出へ接続する中間処理ソフトウェアとして開発・評価されており、表現型取得ワークフローが中心である。
abstractThe proposed intermediate data processing tool showed anacceptable accuracy and potential to process the products from SfM-MVStechniques.
Reproduction assets foundThe paper is a software article for EasyIDP, whose source code is publicly released on GitHub, and the authors explicitly state that the example data (UAV/SfM-MVS phenotyping case-study data) and Jupyter notebook analysis codes are available in a companion public repository (EasyIDP.paper). Both are paper-specific,公开,和Code · publica U19A2061.
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Ethics approval and consent to participate
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Not applicable.
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Consent for publication
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Not applicable.
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Availability of data and materials
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The download link of the example data, and Jupyter notebook codes for drawing all results figures, and the LaTeX
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codes of this manuscript, are available on https://github.com/HowcanoeWang/EasyIDP.paper.486
Competing interests
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The authors declare that they have no competing interests.
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Author details
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1
International Field Phenomics Research Laboratory, Institute for Sustainable Agro-ecosystem Services, Graduate
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School of Agricultural and Life Science, The University of Tokyo, 188-0002 Tokyo, Japan. 2
Key LaOpen asset ↗HowcanoeWang/EasyIDP.paperpdf-raw-page:19 lines:1-164Code / dataset availability confirmedEurope PMC · OpenAlex · checked 13 Sept 2026
Premise Measuring plant productivity is critical to understanding complex community interactions. Many traditional methods for estimating productivity, such as direct measurements of biomass and cover, are resource intensive, and remote sensing techniques are emerging as viable alternatives. Methods We explore drone-based remote sensing tools to estimate productivity in a tallgrass prairie restoration experiment and evaluate their ability to predict direct measures of productivity. We apply these various productivity measures to trace the evolution of plant productivity and the traits underlying it. Results The correlation between remote sensing data and direct measurements of productivity varies depending on vegetation diversity, but the volume of vegetation estimated from drone-based photogrammetry is among the best predictors of biomass and cover regardless of community composition. The commonly used normalized difference vegetation index (NDVI) is a less accurate predictor of biomass and cover than other equally accessible vegetation indices. We found that the traits most strongly correlated with productivity have lower phylogenetic signal, reflecting the fact that high productivity is convergent across the phylogeny of prairie species. This history of trait convergence connects phylogenetic diversity to plant community assembly and succession. Discussion Our study demonstrates (1) the importance of considering phylogenetic diversity when setting management goals in a threatened North American grassland ecosystem and (2) the utility of remote sensing as a complement to ground measurements of grassland productivity for both applied and fundamental questions.
Why it matches plant phenotyping methodsドローン遠隔センシングとフォトグラメトリで植物群落の生産性・バイオマス・被覆を推定し、地上測定との予測性能を比較検証しており、植物形質取得手法が中心的です。
abstractWe explore drone-based remote sensing tools to estimate productivity in a tallgrass prairie restoration experiment and evaluate their ability to predict direct measures of productivity.
Reproduction assets foundThe authors publicly deposited the scripts and data used in this drone-based prairie phenotyping study (biomass, cover, vegetation index measurements, trait data) on GitHub and archived on Zenodo, with explicit availability statements and URLs in the article.Dataset · publicraw measurements can be found in the data sets provided in GitHub ( https://github.com/lanescher/prairie-remote-sensing-2020/tree/master/DATA )Open asset ↗https://github.com/lanescher/prairie-remote-sensing-2020/tree/master/DATAlines:88-96Code · publicScripts and data used in these analyses, as well as all supplements referenced in this article, are available on GitHub ( https://github.com/lanescher/prairie‐remote‐sensing‐2020 ) and on Zenodo ( https://doi.org/10.5281/zenodo.3981500 ; Scher et al., 2020 ).Open asset ↗https://github.com/lanescher/prairie‐remote‐sensing‐2020lines:956-978Dataset · publicScripts and data used in these analyses, as well as all supplements referenced in this article, are available on GitHub ( https://github.com/lanescher/prairie‐remote‐sensing‐2020 ) and on Zenodo ( https://doi.org/10.5281/zenodo.3981500 ; Scher et al., 2020 ).Open asset ↗https://doi.org/10.5281/zenodo.3981500 · 10.5281/zenodo.3981500lines:956-978Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 9 Sept 2026
The development of high-throughput genotyping and phenotyping has provided access to many tools to accelerate plant breeding programs. Unmanned Aerial Systems (UAS)-based remote sensing is being broadly implemented for field-based high-throughput phenotyping due to its low cost and the capacity to rapidly cover large breeding populations. The Structure-from-Motion photogrammetry processes aerial images taken from multiple perspectives over a field to an orthomosaic photo of a complete field experiment, allowing spectral or morphological trait extraction from the canopy surface for each individual field plot. However, some phenotypic information observable in each raw aerial image seems to be lost to the orthomosaic photo, probably due to photogrammetry processes such as pixel merging and blending. To formally assess this, we introduced a set of image processing methods to extract phenotypes from orthorectified raw aerial images and compared them to the negative control of extracting the same traits from processed orthomosaic images. We predict that standard measures of accuracy in terms of the broad-sense heritability of the remote sensing spectral traits will be higher using the orthorectified photos than with the orthomosaic image. Using three case studies, we therefore compared the broad-sense heritability of phenotypes in wheat breeding nurseries including, (1) canopy temperature from thermal imaging, (2) canopy normalized difference vegetation index (NDVI), and (3) early-stage ground cover from multispectral imaging. We evaluated heritability estimates of these phenotypes extracted from multiple orthorectified aerial images via four statistical models and compared the results with heritability estimates of these phenotypes extracted from a single orthomosaic image. Our results indicate that extracting traits directly from multiple orthorectified aerial images yielded increased estimates of heritability for all three phenotypes through proper modeling, compared to estimation using traits extracted from the orthomosaic image. In summary, the image processing methods demonstrated in this study have the potential to improve the quality of the plant trait extracted from high-throughput imaging. This, in turn, can enable breeders to utilize phenomics technologies more effectively for improved selection.
Why it matches plant phenotyping methodsUAS画像から植物形質を抽出する画像処理手法を導入し、オルソモザイク画像との比較で精度・遺伝率を検証しており、フェノタイピング手法が中心である。
abstractwe introduced a set of image processing methods to extract phenotypes from orthorectified raw aerial images and compared them to the negative control of extracting the same traits from processed orthomosaic images.
Reproduction assets foundThe paper publicly deposits its plot-level orthomosaic and orthorectified images (the phenotyping inputs/outputs of this study) at a KSU repository. The authors' Python analysis code (bip, traitExtraction) is mentioned via GitHub footnotes, but those URLs are not in the allowed list, so they cannot be included as verifDataset · publicData associated with these experiments, including the cropped, plot-level orthomosaic images and corresponding orthorectified images, can be accessed at the public repository 7 .Open asset ↗lines:528-572Supplement · publicSupplementary Table 2 ), were used to extract two independent datasets for the CT trait.Open asset ↗lines:333-343Code / dataset availability confirmedEurope PMC · OpenAlex · checked 9 Sept 2026
Plant height (PH) data collected at high temporal resolutions can give insight into how genotype and environmental variation influence plant growth. However, in order to increase the temporal resolution of PH data collection, more robust, rapid, and low-cost methods are needed to evaluate field plots than those currently available. Due to their low cost and high functionality, unmanned aerial vehicles (UAVs) provide an efficient means for collecting height at various stages throughout development. We have developed a procedure for utilizing structure from motion algorithms to collect PH from RGB drone imagery and have used this platform to characterize a yield trial consisting of 24 maize hybrids planted in replicate under two dates and three planting densities. PH data was collected using both weekly UAV flights and manual measurements. The comparisons of UAV-based and manually acquired PH measurements revealed sources of error in measuring PH and were used to develop a robust pipeline for generating UAV-based PH estimates. This pipeline was utilized to document differences in the rate of growth between genotypes and planting dates. Our results also demonstrate that growth rates generated by PH measurements collected at multiple timepoints early in development can be useful in improving predictions of PH at the end of the season. This method provides a low cost, high throughput method for evaluating plant growth in response to environmental stimuli on a plot basis that can be implemented at the scale of a breeding program.
Why it matches plant phenotyping methodsUAV画像とSfMによる圃場作物の草丈推定手法を開発し、手測定との比較で検証した研究であり、表現型取得パイプラインが中心です。
abstractWe have developed a procedure for utilizing structure from motion algorithms to collect PH from RGB drone imagery
Reproduction assets foundThe paper's data availability statement explicitly deposits the authors' image analysis and trait extraction scripts in a public GitHub repository, which directly implements the UAV plant-height phenotyping pipeline described in the paper. No phenotype dataset deposit is stated; supporting information files are not URLCode · publicThe scripts and processes used to perform the image analyses and trait extraction are available at https://github.com/SBTirado/UAV_PH.git .Open asset ↗SBTirado/UAV_PHlines:182-204Code / dataset availability confirmedbioRxiv · Europe PMC · OpenAlex · checked 14 Sept 2026
Field / plotPhotogrammetry / SfM / MVSLiDAR / point cloudStem / branchMorphology / geometry measurement2D/3D reconstructionArchitecture / morphology / geometry
Quantitative assessment of the effects of forest management on tree size and shape has been challenging as there has been a lack of methodologies for characterizing differences and possible changes comprehensively in space and time. Terrestrial laser scanning (TLS) and photogrammetric point clouds provide three-dimensional (3D) information on tree stem reconstructions required for characterizing differences between stem shapes and growth allocation. This data set includes 3D reconstructions of stems of Scots pine (Pinus sylvestris L.) trees from sample plots with different thinning treatments. The thinning treatments include two intensities of thinning, three thinning types as well as control (i.e. no thinning treatment since the establishment). The data set can be used in developing point clouds processing algorithms for single tree stem reconstruction and for investigating variation in stem size and shape of Scots pine trees. Additionally, it offers possibilities in characterizing the effects of various thinning treatments on stem size and shape of Scots pine trees from boreal forests. Data setZenodo https://zenodo.org/record/3701271 Data set licenseAttribution 4.0 International (CC BY 4.0)
Why it matches plant phenotyping methodsTLSと写真測量による樹幹の3D再構成データセットであり、樹幹サイズ・形状という植物形質の抽出アルゴリズム開発と評価に直接利用できるため、方法中心のデータセットとして含める。
abstractTerrestrial laser scanning (TLS) and photogrammetric point clouds provide three-dimensional (3D) information on tree stem reconstructions required for characterizing differences between stem shapes and growth allocation.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThis data set includes three packed zip files that can be downloaded from
https://zenodo.org/record/3701271. The zip files include text files of stem points of each tree within
the sample plots from the three test sites.Open asset ↗Zenodopdf-page:4 lines:1-36Code / dataset availability confirmedbioRxiv · Europe PMC · OpenAlex · checked 9 Sept 2026
Plant height (PH) data collected at high temporal resolutions can give insight into important growth parameters useful for identifying elite material in plant breeding programs and developing management guidelines in production settings. However, in order to increase the temporal resolution of PH data collection, more robust, rapid and low-cost methods are needed to evaluate field plots than those currently available. Due to their low cost and high functionality, unmanned aerial vehicles (UAVs) can be an efficient means for collecting height at various stages throughout development. We have developed a procedure for utilizing structure from motion algorithms to collect PH from RGB drone imagery and have used this platform to characterize a yield trial consisting of 24 maize hybrids planted in replicate under two dates and three planting densities in St Paul, MN in the summer of 2018. The field was imaged weekly after planting using a DJI Phantom 4 Advanced drone to extract PH and hand measurements were collected following aerial imaging of the field. In this work, we test the error in UAV PH measurements and compare it to the error obtained within manually acquired PH measurements. We also propose a method for improving the correspondence of manual and UAV measured height and evaluate the utility of using UAV obtained PH data for assessing growth of maize genotypes and for estimating end-season height.
Why it matches plant phenotyping methodsUAV画像とSfMを用いたトウモロコシ草丈抽出法の開発、手測定との誤差比較・検証、育種試験への実質的な適用が中心である。
abstractWe have developed a procedure for utilizing structure from motion algorithms to collect PH from RGB drone imagery
Reproduction assets foundThe paper explicitly states that the authors' custom MATLAB image-analysis and trait-extraction scripts for UAV-derived plant height are publicly available in a GitHub repository. No phenotype dataset or imagery deposit is stated in the supplied blocks.Code · publicThe scripts and processes used to perform the image analyses and trait extract are available at
https://github.com/SBTirado/UAV_PH.git.Open asset ↗SBTirado/UAV_PHpdf-page:6 lines:1-52Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 13 Sept 2026
Image-based modeling, and more precisely, Structure from Motion (SfM) and Multi-View Stereo (MVS), is emerging as a flexible, self-service, remote sensing tool for generating fine-grained digital surface models (DSMs) in the Earth sciences and ecology. However, drone-based SfM + MVS applications have developed at a rapid pace over the past decade and there are now many software options available for data processing. Consequently, understanding of reproducibility issues caused by variations in software choice and their influence on data quality is relatively poorly understood. This understanding is crucial for the development of SfM + MVS if it is to fulfill a role as a new quantitative remote sensing tool to inform management frameworks and species conservation schemes. To address this knowledge gap, a lightweight multirotor drone carrying a Ricoh GR II consumer-grade camera was used to capture replicate, centimeter-resolution image datasets of a temperate, intensively managed grassland ecosystem. These data allowed the exploration of method reproducibility and the impact of SfM + MVS software choice on derived vegetation canopy height measurement accuracy. The quality of DSM height measurements derived from four different, yet widely used SfM-MVS software-Photoscan, Pix4D, 3DFlow Zephyr, and MICMAC, was compared with in situ data captured on the same day as image capture. We used both traditional agronomic techniques for measuring sward height, and a high accuracy and precision differential GPS survey to generate independent measurements of the underlying ground surface elevation. Using the same replicate image dataset ( n = 3) as input, we demonstrate that there are 1.7, 2.0, and 2.5 cm differences in RMSE (excluding one outlier) between the outputs from different SfM + MVS software using High, Medium, and Low quality settings, respectively. Furthermore, we show that there can be a significant difference, although of small overall magnitude between replicate image datasets ( n = 3) processed using the same SfM + MVS software, following the same workflow, with a variance in RMSE of up to 1.3, 1.5, and 2.7 cm (excluding one outlier) for "High," "Medium," and "Low" quality settings, respectively. We conclude that SfM + MVS software choice does matter, although the differences between products processed using "High" and "Medium" quality settings are of small overall magnitude.
Why it matches plant phenotyping methodsSfM/MVSソフトウェアの選択が植生キャノピー高の推定精度と再現性に与える影響を比較検証しており、植物形質取得手法の技術評価が中心である。
abstractWe used both traditional agronomic techniques for measuring sward height, and a high accuracy and precision differential GPS survey to generate independent measurements of the underlying ground surface elevation.
Reproduction assets foundThe paper's own drone image datasets, DGPS ground survey points, and sward height measurements are deposited publicly on Dryad, as stated in the Data Availability Statement. No author analysis code is explicitly deposited.Dataset · publicData available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.q7c400k (Forsmoo et al., 2019 ).Open asset ↗Dryad Digital Repository · 10.5061/dryad.q7c400klines:57-85Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Abstract Key Message This study showed that digital terrestrial photogrammetry is able to produce accurate estimates of stem volume and diameter across a range of species and tree sizes that showed strong correspondence when compared with traditional inventory techniques. This paper demonstrates the utility of the technology for characterizing trees in complex habitats such as boreal mixedwood forests. Context Accurate knowledge of tree stem taper and volume are key components of forest inventories to manage and study forest resources. Recent developments have seen the increasing use of ground-based point clouds, including from digital terrestrial photogrammetry (DTP), to provide accurate estimates of these key forest attributes. Aims In this study, we evaluated the utility of DTP based on a small set of photos (12 per tree) for estimating stem volume and taper on a set of 15 trees from 6 different species (Populus tremuloides, Picea glauca, Pinus contorta latifolia, Betula papyrifera, Picea mariana, Abies balsamea) in a boreal mixedwood forest in Alberta, Canada. Methods We constructed accurate photogrammetric point clouds and derived taper and volume from three point cloud–based methods, which were then compared with estimates from conventional, field-based measurements. All methods were evaluated for their accuracy based on field-measured taper and volume of felled trees. Results Of the methods tested, we found that the point cloud–derived diameters in a taper curve matching approach performed the best at estimating diameters at the lowest parts of the stem ( 50% of total height). Using the field-measured DBH and height as inputs to calculate stem volume yielded the most accurate predictions; however, these were not significantly different from the best point cloud-based estimates. Conclusion The methodology confirmed that using a small set of photographs provided accurate estimates of individual tree DBH, taper, and volume across a range of species and size gradients (10.8–40.4 cm DBH).
Why it matches plant phenotyping methods樹木のDBH、幹のテーパー、体積という個体レベルの植物形質を、デジタル地上写真測量と点群処理で推定し、従来測定および伐倒木データで精度検証しているため、フェノタイピング手法が中心である。
abstractThis study showed that digital terrestrial photogrammetry is able to produce accurate estimates of stem volume and diameter across a range of species and tree sizes
Reproduction assets foundThe paper publicly releases an example DTP point cloud (tree 11) as a ResearchGate dataset with a DOI, plus a web viewer of the same tree. No analysis code or full dataset is reported.Dataset · publicCoops
with support from West Fraser Timber Co. Ltd. Additional funding for
field data collection was provided by Alberta Agriculture and Forestry
and West Fraser Timber Co. Ltd.
Data availability An example point cloud showing DTP reconstruction
of tree 11 can be found in the ResearchGate repository (Mulverhill et al.
2019) at https://doi.org/10.13140/RG.2.2.23986.86725. Additionally, a
web viewer showing this tree can be found at http://irss-pov.forestry.ubc.ca/tree_11.html
83 Page 10 of 12 Annals of Forest Science (2019) 76: 83Open asset ↗ResearchGate · 10.13140/RG.2.2.23986.86725pdf-raw-page:10 lines:87-102Dataset · publicided by Alberta Agriculture and Forestry
and West Fraser Timber Co. Ltd.
Data availability An example point cloud showing DTP reconstruction
of tree 11 can be found in the ResearchGate repository (Mulverhill et al.
2019) at https://doi.org/10.13140/RG.2.2.23986.86725. Additionally, a
web viewer showing this tree can be found at http://irss-pov.forestry.ubc.ca/tree_11.html
83 Page 10 of 12 Annals of Forest Science (2019) 76: 83Open asset ↗pdf-raw-page:10 lines:87-102Code / dataset availability confirmedOpenAlex · checked 13 Sept 2026
Forest structure is a crucial component in the assessment of whether a forest is likely to act as a carbon sink under changing climate. Detailed 3D structural information about the tundra–taiga ecotone of Siberia is mostly missing and still underrepresented in current research due to the remoteness and restricted accessibility. Field based, high-resolution remote sensing can provide important knowledge for the understanding of vegetation properties and dynamics. In this study, we test the applicability of consumer-grade Unmanned Aerial Vehicles (UAVs) for rapid calculation of stand metrics in treeline forests. We reconstructed high-resolution photogrammetric point clouds and derived canopy height models for 10 study sites from NE Chukotka and SW Yakutia. Subsequently, we detected individual tree tops using a variable-window size local maximum filter and applied a marker-controlled watershed segmentation for the delineation of tree crowns. With this, we successfully detected 67.1% of the validation individuals. Simple linear regressions of observed and detected metrics show a better correlation (R2) and lower relative root mean square percentage error (RMSE%) for tree heights (mean R2 = 0.77, mean RMSE% = 18.46%) than for crown diameters (mean R2 = 0.46, mean RMSE% = 24.9%). The comparison between detected and observed tree height distributions revealed that our tree detection method was unable to representatively identify trees 15–20 m to capture homogeneous and representative forest stands. Additionally, we identify sources of omission and commission errors and give recommendations for their mitigation. In summary, the efficiency of the used method depends on the complexity of the forest’s stand structure.
Why it matches plant phenotyping methodsUAV画像から点群・樹冠高モデルを生成し、個体樹頂検出と樹冠分割によって樹高・樹冠径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 reconstructed high-resolution photogrammetric point clouds and derived canopy height models for 10 study sites from NE Chukotka and SW Yakutia.
Reproduction assets foundThe paper's pre-processed photogrammetric point clouds used to derive forest metrics are publicly deposited in PANGAEA (doi:10.1594/PANGAEA.902259). Other URLs (Pix4D, R packages) are generic third-party tools, not paper-specific assets.Dataset · publicWe successfully detected a total of 4719 trees and derived individual tree, stand structure, and site
morphological metrics from 10 photogrammetric point clouds (Table 3; pre-processed point clouds are
available for download at https://doi.org/10.1594/PANGAEA.902259).Open asset ↗PANGAEA · 10.1594/PANGAEA.902259pdf-page:8 lines:1-56Code / dataset availability confirmedEurope PMC · OpenAlex · checked 13 Sept 2026
Pine processionary moth (PPM) feeds on conifer foliage and periodically result in outbreaks leading to large scale defoliation, causing decreased tree growth, vitality and tree reproduction capacity. Multispectral high-resolution imagery acquired from a UAS platform was successfully used to assess pest tree damage at the tree level in a pine-oak mixed forest. We generated point clouds and multispectral orthomosaics from UAS through photogrammetric processes. These were used to automatically delineate individual tree crowns and calculate vegetation indices such as the normalized difference vegetation index (NDVI) and excess green index (ExG) to objectively quantify defoliation of trees previously identified. Overall, our research suggests that UAS imagery and its derived products enable robust estimation of tree crowns with acceptable accuracy and the assessment of tree defoliation by classifying trees along a gradient from completely defoliated to non-defoliated automatically with 81.8% overall accuracy. The promising results presented in this work should inspire further research and applications involving a combination of methods allowing the scaling up of the results on multispectral imagery by integrating satellite remote sensing information in the assessments over large spatial scales.
Why it matches plant phenotyping methodsUASマルチスペクトル画像から樹冠を抽出し、植食による樹木の落葉・被害状態を自動定量化する手法が研究の中心であり、精度評価も行っている。
abstractMultispectral high-resolution imagery acquired from a UAS platform was successfully used to assess pest tree damage at the tree level in a pine-oak mixed forest.
Reproduction assets foundThe paper's UAS multispectral imagery, derived point clouds/orthomosaics, and field validation data were deposited in open access on Zenodo (DOI 10.5281/zenodo.2539199), directly supporting this paper's defoliation phenotyping analysis. Other URLs (Pix4D, rLiDAR, FAO) are generic tools or cited references, not paper-Dataset · publicData Availability There are not restrictions and data has been deposited to Zenodo in open access. Doi: 10.5281/zenodo.2539199 ( https://zenodo.org/record/2539199#.XEHO61xKhPY ).Open asset ↗Zenodo · 10.5281/zenodo.2539199lines:34-39Code / dataset availability confirmedEurope PMC · OpenAlex · checked 13 Sept 2026
In orchards, measuring crown characteristics is essential for monitoring the dynamics of tree growth and optimizing farm management. However, it lacks a rapid and reliable method of extracting the features of trees with an irregular crown shape such as trained peach trees. Here, we propose an efficient method of segmenting the individual trees and measuring the crown width and crown projection area (CPA) of peach trees with time-series information, based on gathered images. The images of peach trees were collected by unmanned aerial vehicles in an orchard in Okayama, Japan, and then the digital surface model was generated by using a Structure from Motion (SfM) and Multi-View Stereo (MVS) based software. After individual trees were identified through the use of an adaptive threshold and marker-controlled watershed segmentation in the digital surface model, the crown widths and CPA were calculated, and the accuracy was evaluated against manual delineation and field measurement, respectively. Taking manual delineation of 12 trees as reference, the root-mean-square errors of the proposed method were 0.08 m ( R 2 = 0.99) and 0.15 m ( R 2 = 0.93) for the two orthogonal crown widths, and 3.87 m 2 for CPA ( R 2 = 0.89), while those taking field measurement of 44 trees as reference were 0.47 m ( R 2 = 0.91), 0.51 m ( R 2 = 0.74), and 4.96 m 2 ( R 2 = 0.88). The change of growth rate of CPA showed that the peach trees grew faster from May to July than from July to September, with a wide variation in relative growth rates among trees. Not only can this method save labour by replacing field measurement, but also it can allow farmers to monitor the growth of orchard trees dynamically.
Why it matches plant phenotyping methodsUAV画像とSfM/MVS、画像分割によりモモ樹の樹冠幅・樹冠投影面積を抽出し、手動 delineation と圃場測定で精度検証しており、植物表現型取得法が研究の中心である。
abstractwe propose an efficient method of segmenting the individual trees and measuring the crown width and crown projection area (CPA) of peach trees with time-series information
Reproduction assets foundThe paper explicitly states that source codes and sample data for the peach crown characterization method are available at the authors' public GitHub repository, which matches an allowed URL.Code · publicThe crown geometry is derived using two kinds of DSM (bare-branch DSM and foliated DSM) by image analysis techniques in the following five steps (source codes and sample data are available at our surpport page: https://github.com/UTokyo-FieldPhenomics-Lab/Characterization-of-peach-tree-crown):Open asset ↗UTokyo-FieldPhenomics-Lab/Characterization-of-peach-tree-crownlines:46-69Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
In the value chain, yields are key information for both growers and other stakeholders in market supply and exports. However, orchard yields are often still based on an extrapolation of tree production which is visually assessed on a limited number of trees; a tedious and inaccurate task that gives no yield information at a finer scale than the orchard plot. In this work, we propose a method to accurately map individual tree production at the orchard scale by developing a trade-off methodology between mechanistic yield modelling and extensive fruit counting using machine vision systems. A methodological toolbox was developed and tested to estimate and map tree species, structure, and yields in mango orchards of various cropping systems (from monocultivar to plurispecific orchards) in the Niayes region, West Senegal. Tree structure parameters (height, crown area and volume), species, and mango cultivars were measured using unmanned aerial vehicle (UAV) photogrammetry and geographic, object-based image analysis. This procedure reached an average overall accuracy of 0.89 for classifying tree species and mango cultivars. Tree structure parameters combined with a fruit load index, which takes into account year and management effects, were implemented in predictive production models of three mango cultivars. Models reached satisfying accuracies with R2 greater than 0.77 and RMSE% ranging from 20% to 29% when evaluated with the measured production of 60 validation trees. In 2017, this methodology was applied to 15 orchards overflown by UAV, and estimated yields were compared to those measured by the growers for six of them, showing the proper efficiency of our technology. The proposed method achieved the breakthrough of rapidly and precisely mapping mango yields without detecting fruits from ground imagery, but rather, by linking yields with tree structural parameters. Such a tool will provide growers with accurate yield estimations at the orchard scale, and will permit them to study the parameters that drive yield heterogeneity within and between orchards.
Why it matches plant phenotyping methodsUAVフォトグラメトリと画像解析により樹体構造・品種・個体収量を推定・地図化する手法を開発し、検証・実 orchard 適用しており、植物フェノタイピングが中心である。
abstractIn this work, we propose a method to accurately map individual tree production at the orchard scale by developing a trade-off methodology between mechanistic yield modelling and extensive fruit counting using machine vision systems.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicSupplementary Materials: The following are available online at http://www.mdpi.com/2072-4292/10/12/1900/
s1, Figure S1: Image abacus used by expert in the field to estimate load index for (a) ‘Kent’, (b) ‘Keitt’, (c) and
‘BDH’ cultivar. Load index categories (low, medium and high) are displayed in column and different tree heights
(small, medium and tall) are represented in line. Table S1: Mean fruit weight and standard deviation (SD) for the
three variety in Niayes region. Table S2: Description of the 150 calibration trees: cultivar; number of fruit detected
by the KNN-based machine vision and yield measured; load index; and tree structure parameters (tree height,
crown area and volume).Open asset ↗pdf-page:18 lines:1-56Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
Geometric dimensions of plants are significant parameters for showing plant dynamic responses to environmental variations. An image-based high-throughput phenotyping platform was developed to automatically measure geometric dimensions of plants in a greenhouse. The goal of this paper was to evaluate the accuracy in geometric measurement using the Structure from Motion (SfM) method from images acquired using the automated image-based platform. Images of nine artificial objects of different shapes were taken under 17 combinations of three different overlaps in x and y directions, respectively, and two different spatial resolutions (SRs) with three replicates. Dimensions in x, y and z of these objects were measured from 3D models reconstructed using the SfM method to evaluate the geometric accuracy. A metric power of unit (POU) was proposed to combine the effects of image overlap and SR. Results showed that measurement error of dimension in z is the least affected by overlap and SR among the three dimensions and measurement error of dimensions in x and y increased following a power function with the decrease of POU (R2 = 0.78 and 0.88 for x and y respectively). POUs from 150 to 300 are a preferred range to obtain reasonable accuracy and efficiency for the developed image-based high-throughput phenotyping system. As a study case, the developed system was used to measure the height of 44 plants using an optimal POU in greenhouse environment. The results showed a good agreement (R2 = 92% and Root Mean Square Error = 9.4 mm) between the manual and automated method.
Why it matches plant phenotyping methods温室画像型ハイスループット表現型解析プラットフォームの3D幾何計測精度をSfMで評価し、植物体高への適用も検証しており、表現型取得手法が研究の中心である。
abstractAn image-based high-throughput phenotyping platform was developed to automatically measure geometric dimensions of plants in a greenhouse.
Reproduction assets foundThe paper's image-derived measurement dataset (images of nine objects under 17 POUs in three replicates) is explicitly deposited as online Supplementary Materials at the MDPI URL, which is an allowed URL. No author analysis code or trained models are stated as publicly available.Dataset · publicersity of Missouri for providing experimental materials and supplies. We also would like to thank colleagues Chin Nee Vong and Aijing Feng from Precision and Automated Agriculture Laboratory at the University of Missouri for their kind help in conducting experiments.
Supplementary Materials
The following are available online at http://www.mdpi.com/1424-8220/18/7/2270/s1 .
Click here for additional data file.
Author Contributions
J.Z. (Jing Zhou) conducted the experiment, developed the software, analyzed the data, and wrote the paper. X.F. developed the platform and facilities in greenhouse, supervised J.Z. (Jing Zhou)’s experimental work, and revised the manuscript. L.S. and J.Z. (Jianfeng ZOpen asset ↗lines:86-189Code / dataset availability confirmedEurope PMC · OpenAlex · checked 13 Sept 2026
Photogrammetry-based three-dimensional reconstruction of objects is becoming increasingly appealing in research areas unrelated to computer vision. It has the potential to facilitate the assessment of forest inventory-related parameters by enabling or expediting resource measurements in the field. We hereby compare several implementations of photogrammetric algorithms (CMVS/PMVS, CMPMVS, MVE, OpenMVS, SURE and Agisoft PhotoScan) with respect to their performance in vegetation assessment. The evaluation is based on (i) a virtual scene where the precise location and dimensionality of objects is known a priori and is thus conducive to a quantitative comparison and (ii) using series of in situ acquired photographs of vegetation with overlapping field of view where the photogrammetric outcomes are compared qualitatively. Performance is quantified by computing receiver operating characteristic curves that summarize the type-I and type-II errors between the reference and reconstructed tree models. Similar artefacts are observed in synthetic- and in situ -based reconstructions.
Why it matches plant phenotyping methods植生・樹木の3次元形状を推定するフォトグラメトリ手法群を比較・定量評価しており、植物の構造的形質取得が研究の中心である。
abstractWe hereby compare several implementations of photogrammetric algorithms (CMVS/PMVS, CMPMVS, MVE, OpenMVS, SURE and Agisoft PhotoScan) with respect to their performance in vegetation assessment.
Reproduction assets foundThe paper's original UAS-based aerial and synthetic imagery used for the photogrammetric software comparison is publicly deposited on Dryad (10.5061/dryad.2459s12), a paper-specific, directly actionable asset. Other URLs (CloudCompare, MeshLab, OpenMVS, VisualSFM, POV-Ray, OpenCV) are generic third-party tools, not theDataset · publicOriginal aerial UAS-based and synthetics imagery data used for the comparison of photogrammetric algorithms are available on the Dryad Digital Repository: http://dx.doi.org/10.5061/dryad.2459s12 [ 21 ].Open asset ↗Dryad Digital Repository · 10.5061/dryad.2459s12lines:227-281Code / dataset availability confirmedEurope PMC · OpenAlex · checked 14 Sept 2026
Remotely sensing recent growth, herbivory, or disturbance of herbaceous and woody vegetation in dryland ecosystems requires high spatial resolution and multi-temporal depth. Three dimensional (3D) remote sensing technologies like lidar, and techniques like structure from motion (SfM) photogrammetry, each have strengths and weaknesses at detecting vegetation volume and extent, given the instrument's ground sample distance and ease of acquisition. Yet, a combination of platforms and techniques might provide solutions that overcome the weakness of a single platform. To explore the potential for combining platforms, we compared detection bias amongst two 3D remote sensing techniques (lidar and SfM) using three different platforms [ground-based, small unmanned aerial systems (sUAS), and manned aircraft]. We found aerial lidar to be more accurate for characterizing the bare earth (ground) in dense herbaceous vegetation than either terrestrial lidar or aerial SfM photogrammetry. Conversely, the manned aerial lidar did not detect grass and fine woody vegetation while the terrestrial lidar and high resolution near-distance (ground and sUAS) SfM photogrammetry detected these and were accurate. UAS SfM photogrammetry at lower spatial resolution under-estimated maximum heights in grass and shrubs. UAS and handheld SfM photogrammetry in near-distance high resolution collections had similar accuracy to terrestrial lidar for vegetation, but difficulty at measuring bare earth elevation beneath dense herbaceous cover. Combining point cloud data and derivatives (i.e., meshes and rasters) from two or more platforms allowed for more accurate measurement of herbaceous and woody vegetation (height and canopy cover) than any single technique alone. Availability and costs of manned aircraft lidar collection preclude high frequency repeatability but this is less limiting for terrestrial lidar, sUAS and handheld SfM. The post-processing of SfM photogrammetry data became the limiting factor at larger spatial scale and temporal repetition. Despite the utility of sUAS and handheld SfM for monitoring vegetation phenology and structure, their spatial extents are small relative to manned aircraft.
Why it matches plant phenotyping methods複数の3Dリモートセンシング手法・プラットフォームを比較検証し、植生高・キャノピー被覆などの植物形質の測定精度とデータ融合効果を評価しており、フェノタイピング手法が中心です。
abstractCombining point cloud data and derivatives (i.e., meshes and rasters) from two or more platforms allowed for more accurate measurement of herbaceous and woody vegetation (height and canopy cover) than any single technique alone.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicwe provide only abbreviated descriptions of the technical details involving the image processing and point cloud alignment workflows in our main text, and instead provide those details in a supplemental public GitHub repository: https://github.com/tyson-swetnam/srer-wgewOpen asset ↗tyson-swetnam/srer-wgewlines:34-44