Plants are geometrically and topologically complex objects, and methods and devices that produce plant point clouds often miss parts due to self occlusions, making further analysis, such as phenotypic trait extraction or 3D reconstruction, difficult. We introduce A-Occ-Plant , a novel method for point cloud completion. The first novelty of our algorithm is converting point clouds into a set of images, which are then completed using 2D amodal segmentation. The images are then converted into a complete point cloud by using view-consistent Gaussian splats. The second novelty is the use of a coarse-to-fine hierarchical Transformer with cross-scale attention. The completed soft masks are fused into a continuous 3D density field using Gaussian splatting, removing the need for external pose estimation or fixed-size inputs. We introduce a synthetic dataset using a procedural model and a real-world plant reconstruction benchmark with artificially generated occlusions. We further benchmark A-Occ-Plant against representative 3D point-cloud completion methods, demonstrate that it recovers downstream phenotypic traits (leaf count, leaf angle, plant height), and show that it generalizes to another crops (soybean). A-Occ-Plant achieves a 264.8% improvement in LPIPS and an 8.3% gain in SSIM compared to the current state of the art, while using only 2.3% of the parameters and running 39.4× faster. We release our code at https://github.com/JaeLee18/PlantPhenomics_Occlusion.
Why it matches plant phenotyping methods植物の遮蔽点群を補完し、葉数・葉角度・草丈という表現型形質を復元する手法を開発しており、データセット作成とベンチマーク検証も中心的に行っている。
abstractWe introduce A-Occ-Plant , a novel method for point cloud completion.
Reproduction assets foundThe paper explicitly releases authors' code and sample data (inference code, sample data for reproducing results) via a Google Drive project download and a GitHub repository, both with explicit availability statements and public URLs.Code · publicThe full code and data at https://github.com/JaeLee18/PlantPhenomics_Occlusion .Open asset ↗JaeLee18/PlantPhenomics_Occlusionlines:386-410Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Efficient large-scale 3D reconstruction of orchard environments is essential for robotic inspection and precision agriculture, yet existing methods struggle with unstructured scenes, variable illumination, and computational bottlenecks. We propose InspectGaussian, a coarse-to-fine Gaussian reconstruction framework tailored for orchard inspection robots. The pipeline integrates an RGB-D-based data acquisition strategy using ORB-SLAM3, which is enhanced by a dense mapping module for robust large-scale pose estimation and point cloud generation. A divide-and-conquer strategy is then employed: individual plant views are extracted via a YOLO-World-based detection and 3D matching algorithm, followed by plant-specific reconstruction using an improved 3D Gaussian Splatting (3DGS) method incorporating depth regularization and region-aware refinement. Experimental results in citrus orchards demonstrate that InspectGaussian achieves 96% average precision and 93% recall in plant view extraction, while surpassing state-of-the-art methods in reconstruction fidelity (31.226 PSNR, 0.915 SSIM, 0.067 LPIPS) and point cloud accuracy (7 mm error). These results confirm its effectiveness in capturing fine structural and textural details while maintaining scalability and efficiency. This framework provides a practical solution for high-throughput, in-field plant phenotyping and lays the foundation for intelligent orchard monitoring and management.
Why it matches plant phenotyping methods植物個体の3D再構成とRGB-D・検出・Gaussian Splattingを統合した手法開発であり、植物の構造的形質取得を目的とするため、フェノタイピング手法が中心である。
abstractWe propose InspectGaussian, a coarse-to-fine Gaussian reconstruction framework tailored for orchard inspection robots.
Reproduction assets foundThe paper's authors explicitly state their analysis code is publicly available on GitHub. Phenotype datasets (RGB-D orchard image sequences, LiDAR point clouds, manual trait measurements) are only available upon request, so they do not qualify as public assets.Code · publicOur code are available at https://github.com/zlhzau/InspectGaussian.git .Open asset ↗zlhzau/InspectGaussianlines:489-515Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Accurate localization of plant growth points is essential for precision agriculture applications, including electro-weeding and laser weeding. While crop and weed detection has been extensively studied, existing methods focus primarily on object-level recognition and often neglect fine-grained growth point localization. To address this limitation, we propose a novel training strategy, epoch-based prior annealing (EPA), which incorporates the excess green minus excess red (ExG-ExR) index as prior knowledge and introduces schedule factor and gain factor to effectively steer keypoint regression. The experimental results show that incorporating EPA improves keypoint localization performance, with mAP50 increasing by 0.024 and mAP50:95 by 0.011, while maintaining bounding box detection performance. The parameter sensitivity experiments confirmed that both excessively strong and weak guidance can hinder training. Furthermore, analysis of parameters and computational cost shows that the additional overhead introduced by the EPA strategy accounts for less than 0.5% of the total, and be considered negligible. In summary, the proposed EPA strategy significantly improves the accuracy, robustness, and generalizability of plant and growth point detection models, offering a practical and scalable solution for precision agricultural applications.
Why it matches plant phenotyping methods植物の生長点を画像から局在化する学習戦略を開発し、その性能を実験的に検証しており、植物器官の表現型取得が中心です。
abstractwe propose a novel training strategy, epoch-based prior annealing (EPA), which incorporates the excess green minus excess red (ExG-ExR) index as prior knowledge and introduces schedule factor and gain factor to effectively steer keypoint regression.
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 · publicYou can access the dataset used in this study at the following links: https://github.com/cropandweed/cropandweed-dataset.Open asset ↗cropandweed/cropandweed-datasethtml-lines:497-528Code / dataset availability confirmedarXiv · OpenAlex · checked 13 Sept 2026
Automating tasks in orchards is challenging because of the large amount of variation in the environment and occlusions. One of the challenges is apple pose estimation, where key points, such as the calyx, are often occluded. Recently developed pose estimation methods no longer rely on these key points, but still require them for annotations, making annotating challenging and time-consuming. Due to the abovementioned occlusions, there can be conflicting and missing annotations of the same fruit between different images. Novel 3D reconstruction methods can be used to simplify annotating and enlarge datasets. We propose a novel pipeline consisting of 3D Gaussian Splatting to reconstruct an orchard scene, simplified annotations, automated projection of the annotations to images, and the training and evaluation of a pose estimation method. Using our pipeline, 105 manual annotations were required to obtain 28,191 training labels, a reduction of 99.6%. Experimental results indicated that training with labels of fruits that are $\leq95\%$ occluded resulted in the best performance, with a neutral F1 score of 0.927 on the original images and 0.970 on the rendered images. Adjusting the size of the training dataset had small effects on the model performance in terms of F1 score and pose estimation accuracy. It was found that the least occluded fruits had the best position estimation, which worsened as the fruits became more occluded. It was also found that the tested pose estimation method was unable to correctly learn the orientation estimation of apples.
Why it matches plant phenotyping methodsリンゴの姿勢推定アノテーションを大幅に効率化する3D再構成・自動ラベル投影パイプラインを開発し、姿勢推定性能も評価しているため、植物フェノタイピング手法が中心である。
abstractWe propose a novel pipeline consisting of 3D Gaussian Splatting to reconstruct an orchard scene, simplified annotations, automated projection of the annotations to images, and the training and evaluation of a pose estimation method.
Reproduction assets foundThe paper explicitly provides two paper-specific public assets: the authors' phenotyping/pose-estimation pipeline code on GitHub and the collected apple orchard image dataset on a 4TU DOI. Both are directly used for the paper's measurements and analysis.Dataset · publicIn total, 367 images were collected. The dataset is available at https://doi.org/10.4121/976c94f2-028f-4291-adfd-20eb82b0f647Open asset ↗10.4121/976c94f2-028f-4291-adfd-20eb82b0f647lines:92-108Code / dataset availability confirmedarXiv · checked 15 Sept 2026
StrawberryField / plotLiDAR / point cloudFlowerObject detectionPose / keypoint estimation
The small scale of urban farms and the commercial availability of low-cost robots (such as the FarmBot) that automate simple tending tasks enable an accessible platform for plant phenotyping. We have used a FarmBot with a custom camera end-effector to estimate strawberry plant flower pose (for robotic pollination) from acquired 3D point cloud models. We describe a novel algorithm that translates individual occupancy grids along orthogonal axes of a point cloud to obtain 2D images corresponding to the six viewpoints. For each image, 2D object detection models for flowers are used to identify 2D bounding boxes which can be converted into the 3D space to extract flower point clouds. Pose estimation is performed by fitting three shapes (superellipsoids, paraboloids and planes) to the flower point clouds and compared with manually labeled ground truth. Our method successfully finds approximately 80% of flowers scanned using our customized FarmBot platform and has a mean flower pose error of 7.7 degrees, which is sufficient for robotic pollination and rivals previous results. All code will be made available at https://github.com/harshmuriki/flowerPose.git.
Why it matches plant phenotyping methodsカスタムカメラ付きロボットによる3D花姿勢推定アルゴリズムとプラットフォームを開発・評価しており、花の姿勢という植物形質の取得が中心である。
abstractenable an accessible platform for plant phenotyping
Reproduction assets foundThe paper's flower pose estimation pipeline (translating occupancy grid, 2D/3D conversion, shape fitting) has an explicit authors' code deposit statement with a public GitHub URL, phrased as future availability ('will be made available'), so actionability is likely but not fully confirmed. No public dataset of the FarmCode · publiclower point clouds and compared with manually labeled ground truth. Our method successfully finds approximately 80% of flowers scanned using our customized FarmBot platform and has a mean flower pose error of 7.7 degrees, which is sufficient for robotic pollination and rivals previous results. All code will be made available at https://github.com/harshmuriki/flowerPose.git .
I Introduction
Urban farms [ 1 ] provide healthy food to local communities and can serve as platforms for education and sustainability. Unlike their rural counterparts, urban farms are usually small in scale and commercially available robotic systems such as the FarmBot [ 2 ] have been developed to help automate basic cuOpen asset ↗harshmuriki/flowerPoselines:1-53Code / dataset availability confirmedCrossref · checked 13 Sept 2026
To obtain the complete shape and pose of corn under occlusion, this study proposes a point cloud completion algorithm for completing the fragmented corn point cloud after segmentation. Considering that this work focuses on a single-class crop—corn—the proposals mainly focus on the deep learning model size and the completion of the overall shape of the corn. In this work, the 3D corn models derived from segmentation are employed to systematically output the fragmented point cloud data in batches. The Shape Coding PointAttN (SCPAN) algorithm is also proposed, which is based on PointAttN. The model’s structure is simplified to output sparse point clouds and minimize computational complexity, and a gated multilayer perceptron (MLP) containing 3D position coding is introduced to enhance the model’s spatial awareness. In addition, the prior shape encoder module is initially trained and subsequently integrated into the model to enhance its focus on shape characteristics. Compared to the original model, PointAttN, SCPAN achieves a 34.2% reduction in the number of parameters, and the inference time is reduced by 30 ms while maintaining comparable accuracy. The experimental results show that the proposed method can complete the corn point cloud more effectively, using a small model to help estimate the pose and dimensions of corn accurately. This work supports the precise phenotypic analysis of corn and similar crops, such as citrus and tomatoes, and promotes the development of smart agricultural technology.
Why it matches plant phenotyping methodsトウモロコシの遮蔽点群を補完し、形状・姿勢・寸法を推定する計算手法の開発が中心であり、植物表現型取得への応用も明示されている。
abstractTo obtain the complete shape and pose of corn under occlusion, this study proposes a point cloud completion algorithm
Reproduction assets foundThe paper's Data Availability Statement points to an authors' GitHub repository for the corn point cloud completion code (SCPAN). The phenotype dataset itself is only available upon request from the corresponding author, so it does not qualify as a public asset.Code · publiccorresponding author upon reasonable request. The related code will be released at https://github.Open asset ↗pdf-page:14 lines:1-60Code / dataset availability confirmedEurope PMC · Crossref · checked 14 Sept 2026
Posture is a critical phenotypic trait that reflects crop growth and serves as an essential indicator for both agricultural production and scientific research. Accurate pose estimation enables real-time tracking of crop growth processes, but in field environments, challenges such as variable backgrounds, dense planting, occlusions, and morphological changes hinder precise posture analysis. To address these challenges, we propose PFLO (Pose Estimation Model of Field Maize Based on YOLO Architecture), an end-to-end model for maize pose estimation, coupled with a novel data processing method to generate bounding boxes and pose skeleton data from a"keypoint-line"annotated phenotypic database which could mitigate the effects of uneven manual annotations and biases. PFLO also incorporates advanced architectural enhancements to optimize feature extraction and selection, enabling robust performance in complex conditions such as dense arrangements and severe occlusions. On a fivefold validation set of 1,862 images, PFLO achieved 72.2% pose estimation mean average precision (mAP50) and 91.6% object detection mean average precision (mAP50), outperforming current state-of-the-art models. The model demonstrates improved detection of occluded, edge, and small targets, accurately reconstructing skeletal poses of maize crops. PFLO provides a powerful tool for real-time phenotypic analysis, advancing automated crop monitoring in precision agriculture.
Why it matches plant phenotyping methodsトウモロコシの姿勢という植物表現型を、圃場画像から推定するモデルとデータ処理法を開発・検証しており、フェノタイピング手法が研究の中心である。
abstractwe propose PFLO (Pose Estimation Model of Field Maize Based on YOLO Architecture), an end-to-end model for maize pose estimation, coupled with a novel data processing method to generate bounding boxes and pose skeleton data
This paper presents a NeRF-based framework for point cloud (PCD) reconstruction, specifically designed for indoor high-throughput plant phenotyping facilities. Traditional NeRF-based reconstruction methods require cameras to move around stationary objects, but this approach is impractical for high-throughput environments where objects are rapidly imaged while moving on conveyors or rotating pedestals. To address this limitation, we develop a variant of NeRF-based PCD reconstruction that uses a single stationary camera to capture images as the object rotates on a pedestal. Our workflow comprises COLMAP-based pose estimation, a straightforward pose transformation to simulate camera movement, and subsequent standard NeRF training. A defined Region of Interest (ROI) excludes irrelevant scene data, enabling the generation of high-resolution point clouds (10M points). Experimental results demonstrate excellent reconstruction fidelity, with precision-recall analyses yielding an F-score close to 100.00 across all evaluated plant objects. Although pose estimation remains computationally intensive with a stationary camera setup, overall training and reconstruction times are competitive, validating the method's feasibility for practical high-throughput indoor phenotyping applications. Our findings indicate that high-quality NeRF-based 3D reconstructions are achievable using a stationary camera, eliminating the need for complex camera motion or costly imaging equipment. This approach is especially beneficial when employing expensive and delicate instruments, such as hyperspectral cameras, for 3D plant phenotyping. Future work will focus on optimizing pose estimation techniques and further streamlining the methodology to facilitate seamless integration into automated, high-throughput 3D phenotyping pipelines.
Why it matches plant phenotyping methods植物フェノタイピング施設向けに、固定カメラ画像からNeRFで植物の3D点群を再構成する手法を開発・評価しており、表現型取得が研究の中心である。
abstractThis paper presents a NeRF-based framework for point cloud (PCD) reconstruction, specifically designed for indoor high-throughput plant phenotyping facilities.
Reproduction assets foundThe paper explicitly releases its full SC-NeRF dataset (raw 4K videos, frames, COLMAP poses, NeRF checkpoints, and final 10M-point clouds for six plant/produce objects) on Hugging Face, and states that all datasets and the authors' code are available at the project page. Both are paper-specific, public, and actionable.Code · publicd delicate instruments, such as hyperspectral cameras, for 3D plant phenotyping. Future work will focus on optimizing pose estimation techniques and further streamlining the methodology to facilitate seamless integration into automated, high-throughput 3D phenotyping pipelines. We provide all datasets and our code, available at https://baskargroup.github.io/SC-NeRF/
Figure 1 : Schematic of the stationary camera imaging system for NeRF-based point cloud reconstruction in high-throughput plant phenotyping. In this setup, each plant is conveyed to a rotating turntable marked against a matte black background. Over a full 30-second rotation, a tripod-mounted stationary camera captures high-resoOpen asset ↗lines:1-53Code / dataset availability confirmedEurope PMC · OpenAlex · checked 7 Sept 2026
Plant phenotype detection plays a crucial role in understanding and studying plant biology, agriculture, and ecology. It involves the quantification and analysis of various physical traits and characteristics of plants, such as plant height, leaf shape, angle, number, and growth trajectory. By accurately detecting and measuring these phenotypic traits, researchers can gain insights into plant growth, development, stress tolerance, and the influence of environmental factors, which has important implications for crop breeding. Among these phenotypic characteristics, the number of leaves and growth trajectory of the plant are most accessible. Nonetheless, obtaining these phenotypes is labor intensive and financially demanding. With the rapid development of computer vision technology and artificial intelligence, using maize field images to fully analyze plant-related information can greatly eliminate repetitive labor and enhance the efficiency of plant breeding. However, it is still difficult to apply deep learning methods in field environments to determine the number and growth trajectory of leaves and stalks due to the complex backgrounds and serious occlusion problems of crops in field environments. To preliminarily explore the application of deep learning technology to the acquisition of the number of leaves and stalks and the tracking of growth trajectories in field agriculture, in this study, we developed a deep learning method called Point-Line Net, which is based on the Mask R-CNN framework, to automatically recognize maize field RGB images and determine the number and growth trajectory of leaves and stalks. The experimental results demonstrate that the object detection accuracy (mAP50) of our Point-Line Net can reach 81.5%. Moreover, to describe the position and growth of leaves and stalks, we introduced a new lightweight "keypoint" detection branch that achieved a magnitude of 33.5 using our custom distance verification index. Overall, these findings provide valuable insights for future field plant phenotype detection, particularly for datasets with dot and line annotations.
Why it matches plant phenotyping methodsトウモロコシの葉・茎の数と成長軌跡をRGB画像から抽出する深層学習手法を開発し、精度評価も行っており、植物表現型取得が研究の中心である。
abstractin this study, we developed a deep learning method called Point-Line Net, which is based on the Mask R-CNN framework, to automatically recognize maize field RGB images and determine the number and growth trajectory of leaves and stalks.
Reproduction assets foundThe authors explicitly deposit the code (and data) supporting this maize leaf/stalk trajectory phenotyping study in a public GitHub repository, matching an allowed URL.Code · publicThe computer code and data that support the findings of this study are deposited in a GitHub repository at https://github.com/VEGETALOADING/Point-Line-Net .Open asset ↗VEGETALOADING/Point-Line-Netlines:319-524Code / dataset availability confirmedEurope PMC · OpenAlex · checked 7 Sept 2026
The pod and seed counts are important yield-related traits in soybean. High-precision soybean breeders face the major challenge of accurately phenotyping the number of pods and seeds in a high-throughput manner. Recent advances in artificial intelligence, especially deep learning (DL) models, have provided new avenues for high-throughput phenotyping of crop traits with increased precision. However, the available DL models are less effective for phenotyping pods that are densely packed and overlap in in situ soybean plants; thus, accurate phenotyping of the number of pods and seeds in soybean plant is an important challenge. To address this challenge, the present study proposed a bottom-up model, DEKR-SPrior (disentangled keypoint regression with structural prior), for in situ soybean pod phenotyping, which considers soybean pods and seeds analogous to human people and joints, respectively. In particular, we designed a novel structural prior (SPrior) module that utilizes cosine similarity to improve feature discrimination, which is important for differentiating closely located seeds from highly similar seeds. To further enhance the accuracy of pod location, we cropped full-sized images into smaller and high-resolution subimages for analysis. The results on our image datasets revealed that DEKR-SPrior outperformed multiple bottom-up models, viz., Lightweight-OpenPose, OpenPose, HigherHRNet, and DEKR, reducing the mean absolute error from 25.81 (in the original DEKR) to 21.11 (in the DEKR-SPrior) in pod phenotyping. This paper demonstrated the great potential of DEKR-SPrior for plant phenotyping, and we hope that DEKR-SPrior will help future plant phenotyping.
Why it matches plant phenotyping methods大豆の莢・種子数を高スループットに推定する画像解析モデルを開発し、既存モデルと比較検証しているため、植物表現型取得手法が研究の中心です。
abstractthe present study proposed a bottom-up model, DEKR-SPrior (disentangled keypoint regression with structural prior), for in situ soybean pod phenotyping
Reproduction assets foundThe paper's DEKR-SPrior analysis code is publicly available on GitHub with an explicit availability statement and URL. The homemade soybean pod/seed image datasets are not publicly deposited and require contacting the corresponding author.Code · publicThe source code is publicly available. It can be accessed at the following GitHub repository: https://github.com/Cyncihe/DEKR-SPrior.gitOpen asset ↗https://github.com/Cyncihe/DEKR-SPrior.gitlines:300-403Code / dataset availability confirmedOpenAlex · Europe PMC · Crossref · checked 14 Sept 2026
Abstract Cucumis melo L., commonly known as melon, is a crucial horticultural crop. The selection and breeding of superior melon germplasm resources play a pivotal role in enhancing its marketability. However, current methods for melon appearance phenotypic analysis rely primarily on expert judgment and intricate manual measurements, which are not only inefficient but also costly. Therefore, to expedite the breeding process of melon, we analyzed the images of 117 melon varieties from two annual years utilizing artificial intelligence (AI) technology. By integrating the semantic segmentation model Dual Attention Network (DANet), the object detection model RTMDet, the keypoint detection model RTMPose, and the Mobile-Friendly Segment Anything Model (MobileSAM), a deep learning algorithm framework was constructed, capable of efficiently and accurately segmenting melon fruit and pedicel. On this basis, a series of feature extraction algorithms were designed, successfully obtaining 11 phenotypic traits of melon. Linear fitting verification results of selected traits demonstrated a high correlation between the algorithm-predicted values and manually measured true values, thereby validating the feasibility and accuracy of the algorithm. Moreover, cluster analysis using all traits revealed a high consistency between the classification results and genotypes. Finally, a user-friendly software was developed to achieve rapid and automatic acquisition of melon phenotypes, providing an efficient and robust tool for melon breeding, as well as facilitating in-depth research into the correlation between melon genotypes and phenotypes.
Why it matches plant phenotyping methodsメロン果実・果梗を画像から分割し、特徴抽出によって11形質を推定する深層学習フレームワークを開発・検証し、ソフトウェア化しているため、植物フェノタイピング手法が中心である。
abstractBy integrating the semantic segmentation model Dual Attention Network (DANet), the object detection model RTMDet, the keypoint detection model RTMPose, and the Mobile-Friendly Segment Anything Model (MobileSAM), a deep learning algorithm framework was constructed, capable of efficiently and accurately segmenting melon fruit and pedicel.
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 · publicAdditionally, we have developed a simple melon phenotypic traits extraction software, which can be downloaded via https://github.com/hongbinz13/Melon-Phenotype-Extractor/releases/tag/software .Open asset ↗https://github.com/hongbinz13/Melon-Phenotype-Extractor · Melon-Phenotype-Extractorlines:109-139Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Image segmentation is commonly used to estimate the location and shape of plants and their external structures. Segmentation masks are then used to localize landmarks of interest and compute other geometric features that correspond to the plant's phenotype. Despite its prevalence, segmentation-based approaches are laborious (requiring extensive annotation to train) and error-prone (derived geometric features are sensitive to instance mask integrity). Here, we present a segmentation-free approach that leverages deep learning-based landmark detection and grouping, also known as pose estimation. We use a tool originally developed for animal motion capture called SLEAP (Social LEAP Estimates Animal Poses) to automate the detection of distinct morphological landmarks on plant roots. Using a gel cylinder imaging system across multiple species, we show that our approach can reliably and efficiently recover root system topology at high accuracy, few annotated samples, and faster speed than segmentation-based approaches. In order to make use of this landmark-based representation for root phenotyping, we developed a Python library ( sleap-roots ) for trait extraction directly comparable to existing segmentation-based analysis software. We show that pose-derived root traits are highly accurate and can be used for common downstream tasks including genotype classification and unsupervised trait mapping. Altogether, this work establishes the validity and advantages of pose estimation-based plant phenotyping. To facilitate adoption of this easy-to-use tool and to encourage further development, we make sleap-roots , all training data, models, and trait extraction code available at: https://github.com/talmolab/sleap-roots and https://osf.io/k7j9g/.
Why it matches plant phenotyping methods根系のランドマーク検出・形状復元・形質抽出を行う深層学習ベースの植物フェノタイピング手法を開発・検証し、専用ライブラリも提供しているため。
abstractHere, we present a segmentation-free approach that leverages deep learning-based landmark detection and grouping, also known as pose estimation.
Reproduction assets foundThe authors explicitly make all paper-specific assets public: the sleap-roots trait-extraction codebase on GitHub, a separate repository with figure-replication code, and an OSF deposit containing labeled training data, trained pose-estimation models, and analysis files for the root phenotyping measurements.Code · publicthe specific code utilized for replicating the figures presented in this study can be found in a separate GitHub repository here: https://github.com/talmolab/Berrigan_et_al_sleap-rootsOpen asset ↗talmolab/Berrigan_et_al_sleap-roots · Berrigan_et_al_sleap-rootslines:485-526Dataset · publicThe datasets generated and/or analyzed during the current study are available in the Open Science Framework (OSF) repository. This includes the labeled data, predictive models, and analysis files, which can be accessed via the following link: https://osf.io/k7j9g/Open asset ↗osf.io/k7j9g · k7j9glines:485-526Code / dataset availability confirmedOpenAlex · checked 13 Sept 2026
Rapid and precise tree Diameter at Breast Height (DBH) measurement is pivotal in forest inventories. While the recent advancements in LiDAR and Structure from Motion (SFM) technologies have paved the way for automated DBH measurements, the significant equipment costs and the complexity of operational procedures continue to constrain the ubiquitous adoption of these technologies for real-time DBH assessments. In this research, we introduce KAN-Forest, a real-time DBH measurement and key point localization algorithm utilizing RGB-D (Red, Green, Blue-Depth) imaging technology. Firstly, we improved the YOLOv5-seg segmentation module with a Channel and Spatial Attention (CBAM) module, augmenting its efficiency in extracting the tree’s edge features in intricate forest scenarios. Subsequently, we devised an image processing algorithm for real-time key point localization and DBH measurement, leveraging historical data to fine-tune current frame assessments. This system facilitates real-time image data upload via wireless LAN for immediate host computer processing. We validated our approach on seven sample plots, achieving bbAP50 and segAP50 scores of: 90.0%(+3.0%), 90.9%(+0.9%), respectively with the improved YOLOv5-seg model. The method exhibited a DBH estimation RMSE of 17.61∼54.96 mm (R2=0.937), and secured 78% valid DBH samples at a 59 FPS. Our system stands as a cost-effective, portable, and user-friendly alternative to conventional forest survey techniques, maintaining accuracy in real-time measurements compared to SFM- and LiDAR-based algorithms. The integration of WLAN and its inherent scalability facilitates deployment on Unmanned Ground Vehicles (UGVs) to improve the efficiency of forest inventory. We have shared the algorithms and datasets on Github for peer evaluations.
Why it matches plant phenotyping methodsRGB-D画像とアルゴリズムを用いて樹木のDBHという明示的な形態形質をリアルタイム推定し、精度検証とシステム実装を行った研究であり、植物フェノタイピング手法が中心である。
abstractwe introduce KAN-Forest, a real-time DBH measurement and key point localization algorithm utilizing RGB-D (Red, Green, Blue-Depth) imaging technology.
Reproduction assets foundThe authors explicitly state that the code used in this DBH measurement research is publicly available on GitHub (KAN-Forest repository), making it a paper-specific, public, actionable code asset. The phenotype/trait datasets (DBH measurements and forest images) are only available upon request from the corresponding作者,Code · publicwe have made
the code used in this research available on GitHub at: https://github.com/CharmingZh/KAN-ForestOpen asset ↗CharmingZh/KAN-Forestpdf-page:27 lines:1-59Code / dataset availability confirmedOpenAlex · Europe PMC · bioRxiv · checked 7 Sept 2026
Abstract Image segmentation is commonly used to estimate the location and shape of plants and their external structures. Segmentation masks are then used to localize landmarks of interest and compute other geometric features that correspond to the plant’s phenotype. Despite its prevalence, segmentation-based approaches are laborious (requiring extensive annotation to train), and error-prone (derived geometric features are sensitive to instance mask integrity). Here we present a segmentation-free approach which leverages deep learning-based landmark detection and grouping, also known as pose estimation. We use a tool originally developed for animal motion capture called SLEAP (Social LEAP Estimates Animal Poses) to automate the detection of distinct morphological landmarks on plant roots. Using a gel cylinder imaging system across multiple species, we show that our approach can reliably and efficiently recover root system topology at high accuracy, few annotated samples, and faster speed than segmentation-based approaches. In order to make use of this landmark-based representation for root phenotyping, we developed a Python library ( sleap-roots ) for trait extraction directly comparable to existing segmentation-based analysis software. We show that landmark-derived root traits are highly accurate and can be used for common downstream tasks including genotype classification and unsupervised trait mapping. Altogether, this work establishes the validity and advantages of pose estimation-based plant phenotyping. To facilitate adoption of this easy-to-use tool and to encourage further development, we make sleap-roots , all training data, models, and trait extraction code available at: https://github.com/talmolab/sleap-roots and https://osf.io/k7j9g/ .
Why it matches plant phenotyping methods植物根の形態ランドマークをポーズ推定で検出し、根系形質を抽出する手法とソフトウェアを開発・検証した研究であり、植物フェノタイピング手法が中心である。
abstractHere we present a segmentation-free approach which leverages deep learning-based landmark detection and grouping, also known as pose estimation.
Reproduction assets foundThe paper makes its root phenotyping assets public: labeled training data, trained SLEAP models, and analysis files on OSF, the sleap-roots trait-extraction codebase on GitHub, and a separate figure-replication code repository.Dataset · publicThe datasets generated and/or analyzed during the current study are available in the Open Science Framework (OSF) repository. This includes the labeled data, predictive models, and analysis files which can be accessed via the following link: https://osf.io/k7j9g/ .Open asset ↗osf.io/k7j9glines:752-811Code · publicAdditionally, the specific code utilized for replicating the figures presented in this study can be found in a separate GitHub repository here: https://github.com/talmolab/Berrigan_et_al_sleap-roots .Open asset ↗talmolab/Berrigan_et_al_sleap-rootslines:752-811Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
RootClassificationMorphology / geometry measurementPose / keypoint estimationRoot system architecture
Image segmentation is commonly used to estimate the location and shape of plants and their external structures. Segmentation masks are then used to localize landmarks of interest and compute other geometric features that correspond to the plant’s phenotype. Despite its prevalence, segmentation-based approaches are laborious (requiring extensive annotation to train), and error-prone (derived geometric features are sensitive to instance mask integrity). Here we present a segmentation-free approach which leverages deep learning-based landmark detection and grouping, also known as pose estimation. We use a tool originally developed for animal motion capture called SLEAP (Social LEAP Estimates Animal Poses) to automate the detection of distinct morphological landmarks on plant roots. Using high-throughput phenotyping method Root Architecture 3-D Imaging Cylinder (RADICYL) across multiple species, we show that our approach can reliably and efficiently recover root system topology at greater accuracy, faster speed, and with fewer annotated samples than segmentation-based approaches. In order to make use of this landmark-based representation for root phenotyping, we developed a Python library ( sleap-roots ) for trait extraction directly comparable to existing segmentation-based analysis software. We show that landmark-derived root traits are highly accurate and can be used for common downstream tasks including genotype classification and unsupervised trait mapping. Altogether, this work establishes the validity and advantages of pose estimation-based plant phenotyping. To facilitate adoption of this easy-to-use tool and to encourage further development, we make sleap-roots , all training data, models, and trait extraction code available at: https://github.com/talmolab/sleap-roots .
Why it matches plant phenotyping methods植物根のランドマーク検出による表現型抽出法を開発・検証し、ソフトウェアと学習データも提供しているため、方法が中心的です。
abstractHere we present a segmentation-free approach which leverages deep learning-based landmark detection and grouping, also known as pose estimation.
Reproduction assets foundThe authors explicitly state they make their sleap-roots Python library, all training data, trained models, and trait extraction code publicly available on GitHub, directly supporting this paper's root pose-estimation phenotyping analysis.Code · publice classification and unsupervised
trait mapping. Altogether, this work establishes the validity and advantages of pose estimation-based plant phenotyping. To
facilitate adoption of this easy-to-use tool and to encourage further development, we make sleap-roots , all training data,
models, and trait extraction code available at: https://github.com/talmolab/sleap-roots.
Elizabeth M. Berrigan, Lin Wang, Hannah Carrillo, Kimberly Echegoyen, Mikayla Kappes, Jorge Torres,
Angel Ai-Perreira, Erica McCoy, Emily Shane, Charles Copeland, Lauren Ragel, Charidimos Georgousakis,
Sanghwa Lee, Dawn Reynolds, Avery Talgo, Juan Gonzalez, Ling Zhang, Ashish Rajurkar, Michel Ruiz,
Erin Daniels, Liezl Maree, SOpen asset ↗talmolab/sleap-rootspdf-layout-page:1 lines:1-47Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Intelligent detection and localization of mature citrus fruits is a critical challenge in developing an automatic harvesting robot. Variable illumination conditions and different occlusion states are some of the essential issues that must be addressed for the accurate detection and localization of citrus in the orchard environment. In this paper, a novel method for the detection and localization of mature citrus using improved You Only Look Once (YOLO) v5s with binocular vision is proposed. First, a new loss function (polarity binary cross-entropy with logit loss) for YOLO v5s is designed to calculate the loss value of class probability and objectness score, so that a large penalty for false and missing detection is applied during the training process. Second, to recover the missing depth information caused by randomly overlapping background participants, Cr-Cb chromatic mapping, the Otsu thresholding algorithm, and morphological processing are successively used to extract the complete shape of the citrus, and the kriging method is applied to obtain the best linear unbiased estimator for the missing depth value. Finally, the citrus spatial position and posture information are obtained according to the camera imaging model and the geometric features of the citrus. The experimental results show that the recall rates of citrus detection under non-uniform illumination conditions, weak illumination, and well illumination are 99.55%, 98.47%, and 98.48%, respectively, approximately 2-9% higher than those of the original YOLO v5s network. The average error of the distance between the citrus fruit and the camera is 3.98 mm, and the average errors of the citrus diameters in the 3D direction are less than 2.75 mm. The average detection time per frame is 78.96 ms. The results indicate that our method can detect and localize citrus fruits in the complex environment of orchards with high accuracy and speed. Our dataset and codes are available at https://github.com/AshesBen/citrus-detection-localization.
Why it matches plant phenotyping methods収穫ロボット向けの位置検出が主目的だが、果実形状・姿勢・3D直径を画像から抽出し、精度を検証する技術開発が中心であり、再利用可能な植物器官形質の推定に該当する。
abstracta novel method for the detection and localization of mature citrus using improved You Only Look Once (YOLO) v5s with binocular vision is proposed.
Reproduction assets foundThe authors explicitly state that their citrus image dataset (4855 binocular image groups with depth maps) and analysis code are publicly available on GitHub, matching the allowed URL.Dataset · publicnt occlusion conditions in natural orchards. Future work will focus on few-shot learning and reduce the number of citrus fruits in the training dataset to improve citrus detection and localization.
Data availability statement
The original contributions presented in this study are publicly available. This data can be found here: https://github.com/AshesBen/citrus-detection-localization .
Author contributions
All authors contributed to the method and result of the study, dataset generation, model training and testing, analysis of results, and the drafting, revising, and approving of the contents of the manuscript.
Funding
We acknowledged support from the Natural Science Foundation of GuangdongOpen asset ↗AshesBen/citrus-detection-localizationlines:335-356Code · public98 mm, and the average errors of the citrus diameters in the 3D direction are less than 2.75 mm. The average detection time per frame is 78.96 ms. The results indicate that our method can detect and localize citrus fruits in the complex environment of orchards with high accuracy and speed. Our dataset and codes are available at https://github.com/AshesBen/citrus-detection-localization .
Keywords: citrus detection, citrus localization, binocular vision, YOLO v5s, loss function
status released display-pdf yes is-olf no is-manuscript no is-preprint no is-journal-matter no is-scanned no is-retracted no
Received 2022 Jun 18; Accepted 2022 Jul 12; Collection date 2022.
IntroductionOpen asset ↗AshesBen/citrus-detection-localizationlines:1-28Code / dataset availability confirmedCrossref · Europe PMC · checked 8 Sept 2026
Abstract Background In recent years, there has been an increase of interest in plant behaviour as represented by growth-driven responses. These are generally classified into nastic (internally driven) and tropic (environmentally driven) movements. Nastic movements include circumnutations, a circular movement of plant organs commonly associated with search and exploration, while tropisms refer to the directed growth of plant organs toward or away from environmental stimuli, such as light and gravity. Tracking these movements is therefore fundamental for the study of plant behaviour. Convolutional neural networks, as used for human and animal pose estimation, offer an interesting avenue for plant tracking. Here we adopted the Social LEAP Estimates Animal Poses (SLEAP) framework for plant tracking. We evaluated it on time-lapse videos of cases spanning a variety of parameters, such as: (i) organ types and imaging angles (e.g., top-view crown leaves vs. side-view shoots and roots), (ii) lighting conditions (full spectrum vs. IR), (iii) plant morphologies and scales (100 μm-scale Arabidopsis seedlings vs. cm-scale sunflowers and beans), and (iv) movement types (circumnutations, tropisms and twining). Results Overall, we found SLEAP to be accurate in tracking side views of shoots and roots, requiring only a low number of user-labelled frames for training. Top views of plant crowns made up of multiple leaves were found to be more challenging, due to the changing 2D morphology of leaves, and the occlusions of overlapping leaves. This required a larger number of labelled frames, and the choice of labelling “skeleton” had great impact on prediction accuracy, i.e., a more complex skeleton with fewer individuals (tracking individual plants) provided better results than a simpler skeleton with more individuals (tracking individual leaves). Conclusions In all, these results suggest SLEAP is a robust and versatile tool for high-throughput automated tracking of plants, presenting a new avenue for research focusing on plant dynamics.
Why it matches plant phenotyping methods植物の成長運動を抽出するため、SLEAPを植物追跡へ適応し、多様な器官・撮像条件・形態・運動で精度を評価している。植物表現型取得手法が中心である。
abstractHere we adopted the Social LEAP Estimates Animal Poses (SLEAP) framework for plant tracking.
Reproduction assets foundThe paper's Availability of data and materials statement points to a public Zenodo deposit containing the paper-specific time-lapse videos, SLEAP .slp labelled training files, and predicted output analysis files used in this study.Dataset · publicThe datasets during and/or analysed during the current study available at: https://zenodo.org/record/5764169#.YbCK0_FBxqt , https://doi.org/10.5281/zenodo.5764169 , which includes: (1) raw videos of the timelapse for each analysis. (2) The.slp files for each video analysis, which can be loaded into SLEAP and contain the 5, 10 or 20 labelled training frames.Open asset ↗zenodo · 10.5281/zenodo.5764169lines:134-177Code / dataset availability confirmedEurope PMC · OpenAlex · checked 8 Sept 2026
This work focuses on the problem of non-contact measurement for vegetables in agricultural automation. The application of computer vision in assisted agricultural production significantly improves work efficiency due to the rapid development of information technology and artificial intelligence. Based on object detection and stereo cameras, this paper proposes an intelligent method for vegetable recognition and size estimation. The method obtains colorful images and depth maps with a binocular stereo camera. Then detection networks classify four kinds of common vegetables (cucumber, eggplant, tomato and pepper) and locate six points for each object. Finally, the size of vegetables is calculated using the pixel position and depth of keypoints. Experimental results show that the proposed method can classify four kinds of common vegetables within 60 cm and accurately estimate their diameter and length. The work provides an innovative idea for solving the vegetable's non-contact measurement problems and can promote the application of computer vision in agricultural automation.
Why it matches plant phenotyping methods野菜の長さ・直径という植物器官形質を、ステレオカメラ、深度画像、キーポイント検出で非接触推定する手法が研究の中心であるため。
abstractThis work focuses on the problem of non-contact measurement for vegetables in agricultural automation.
Reproduction assets foundThe authors publicly released both the vegetable keypoint dataset (1600 COCO-format images with ROI boxes and six keypoints) and the implementation code for their size estimation method on GitHub. Labelme is a generic third-party annotation tool and is excluded.Code · publicThe implementation code of our size estimation method can be accessed on https://github.com/BourneZ130/VegetableDetection , accessed on 15 February 2022.Open asset ↗BourneZ130/VegetableDetectionlines:76-141Code / dataset availability confirmedCrossref · Europe PMC · OpenAlex · checked 10 Sept 2026
Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here we present the details and rationale for major developments in the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning.
Why it matches plant phenotyping methods植物画像から表現型を抽出するオープンソース解析ソフトウェアの開発・改良が中心であり、植物フェノタイピング手法論文に該当する。
abstractPlant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis.
Reproduction assets foundThe paper explicitly states that scripts, notebooks, SQL schema, and simple input data for its figures/results are on GitHub, that Setaria images come from publicly available datasets on the PlantCV data page, and that PlantCV v2.1 is archived on Zenodo.Code · publicScripts, notebooks, SQL schema, and simple input data associated with the figures and results presented in this paper are available on GitHub at https://github.com/danforthcenter/plantcv-v2-paper .Open asset ↗danforthcenter/plantcv-v2-paperlines:27-34Dataset · publicImages of Setaria viridis (A10) and Setaria italica (B100) are from publicly available datasets that are available at http://plantcv.danforthcenter.org/pages/data.htmlOpen asset ↗lines:27-34