ArticlePlant methods2023
Cotton plant part 3D segmentation and architectural trait extraction using point voxel convolutional neural networks.
Article in Plant methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Integrating 3D phenotyping and functional-structural plant models for crop ideotype breeding.Nature communications · 2026Review
- Beyond high-throughput: leveraging plant phenotyping to improve understanding and prediction of plant growth through process-based models.The New phytologist · 2026Review
- Deep learning for three-dimensional (3D) plant phenomics.Plant phenomics (Washington, D.C.) · 2025Review
- Structural parameter determination and pruning pattern analysis of pear tree shoots for dormant pruning.Plant phenomics (Washington, D.C.) · 2025Article
- Automatic 3D Plant Organ Instance Segmentation Method Based on PointNeXt and Quickshift+.Plant phenomics (Washington, D.C.) · 2025Article
- Phenotypic dynamics and temporal heritability of tomato architectural traits using an unmanned ground vehicle-based plant phenotyping system.Horticulture research · 2025Article
- Mapping of cotton bolls and branches with high-granularity through point cloud segmentation.Plant methods · 2025Article
- A cotton organ segmentation method with phenotypic measurements from a point cloud using a transformer.Plant methods · 2025Article
- KAN-GLNet: An enhanced PointNet++ model for canola silique segmentation and counting.PloS one · 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
- Article
- Using high-throughput phenotype platform MVS-Pheno to reconstruct the 3D morphological structure of wheat.AoB PLANTS · 2024Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundPlant architecture can influence crop yield and quality. Manual extraction of architectural traits is, however, time-consuming, tedious, and error prone. The trait estimation from 3D data addresses occlusion issues with the availability of depth information while deep learning approaches enable learning features without manual design. The goal of this study was to develop a data processing workflow by leveraging 3D deep learning models and a novel 3D data annotation tool to segment cotton plant parts and derive important architectural traits.
resultsThe Point Voxel Convolutional Neural Network (PVCNN) combining both point- and voxel-based representations of 3D data shows less time consumption and better segmentation performance than point-based networks. Results indicate that the best mIoU (89.12%) and accuracy (96.19%) with average inference time of 0.88 s were achieved through PVCNN, compared to Pointnet and Pointnet++. On the seven derived architectural traits from segmented parts, an R
conclusionThis plant part segmentation method based on 3D deep learning enables effective and efficient architectural trait measurement from point clouds, which could be useful to advance plant breeding programs and characterization of in-season developmental traits. The plant part segmentation code is available at https://github.com/UGA-BSAIL/plant_3d_deep_learning .
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