ArticlePlant methods2022
Segmentation of structural parts of rosebush plants with 3D point-based deep learning methods.
Article in Plant methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- DDCANet for SiOFrontiers in plant science · 2026Article
- Deep learning for three-dimensional (3D) plant phenomics.Plant phenomics (Washington, D.C.) · 2025Review
- HALF: Histogram of Angles in Linked Features for 3D Point Cloud Data Segmentation of Plants for Robust Sensing.Sensors (Basel, Switzerland) · 2025Article
- A dual-task segmentation network based on multi-head hierarchical attention for 3D plant point cloud.Frontiers in plant science · 2025Article
- Automated measurement of field crop phenotypic traits using UAV 3D point clouds and an improved PointNet+.Frontiers in plant science · 2025Article
- GCASSN: a graph convolutional attention synergistic segmentation network for 3D plant point cloud segmentation.Frontiers in plant science · 2025Article
- Crops3D: a diverse 3D crop dataset for realistic perception and segmentation toward agricultural applications.Scientific data · 2024Article
- Three-dimensional branch segmentation and phenotype extraction of maize tassel based on deep learning.Plant methods · 2023Article
- How to make sense of 3D representations for plant phenotyping: a compendium of processing and analysis techniques.Plant methods · 2023Review
- Cotton plant part 3D segmentation and architectural trait extraction using point voxel convolutional neural networks.Plant methods · 2023Article
- Phenotyping of Silique Morphology in Oilseed Rape Using Skeletonization with Hierarchical Segmentation.Plant phenomics (Washington, D.C.) · 2023Article
- Digital whole-community phenotyping: tracking morphological and physiological responses of plant communities to environmental changes in the field.Frontiers in plant science · 2023Article
- 3D data-augmentation methods for semantic segmentation of tomato plant parts.Frontiers in plant science · 2023Article
- MASPC_Transform: A Plant Point Cloud Segmentation Network Based on Multi-Head Attention Separation and Position Code.Sensors (Basel, Switzerland) · 2022Article
- A graph-based approach for simultaneous semantic and instance segmentation of plant 3D point clouds.Frontiers in plant science · 2022Article
- Improved Point-Cloud Segmentation for Plant Phenotyping Through Class-Dependent Sampling of Training Data to Battle Class Imbalance.Frontiers in plant science · 2022Article
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4 authors.
Funding
Abstract
backgroundSegmentation of structural parts of 3D models of plants is an important step for plant phenotyping, especially for monitoring architectural and morphological traits. Current state-of-the art approaches rely on hand-crafted 3D local features for modeling geometric variations in plant structures. While recent advancements in deep learning on point clouds have the potential of extracting relevant local and global characteristics, the scarcity of labeled 3D plant data impedes the exploration of this potential.
resultsWe adapted six recent point-based deep learning architectures (PointNet, PointNet++, DGCNN, PointCNN, ShellNet, RIConv) for segmentation of structural parts of rosebush models. We generated 3D synthetic rosebush models to provide adequate amount of labeled data for modification and pre-training of these architectures. To evaluate their performance on real rosebush plants, we used the ROSE-X data set of fully annotated point cloud models. We provided experiments with and without the incorporation of synthetic data to demonstrate the potential of point-based deep learning techniques even with limited labeled data of real plants.
conclusionThe experimental results show that PointNet++ produces the highest segmentation accuracy among the six point-based deep learning methods. The advantage of PointNet++ is that it provides a flexibility in the scales of the hierarchical organization of the point cloud data. Pre-training with synthetic 3D models boosted the performance of all architectures, except for PointNet.
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