ArticlePlant methods2025
Mapping of cotton bolls and branches with high-granularity through point cloud segmentation.
Article in Plant methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Cotton field population phenotyping analysis based on 3D Gaussian reconstruction and dynamic spatial constraints.Frontiers in plant science · 2026Article
- VGDS-PointNet++ for organ segmentation and phenotypic trait estimation in greenhouse tomato seedlings.Frontiers in plant science · 2026Article
- A scalable framework for UAV-based point cloud reconstruction and organ segmentation of field-grown cotton in complex field environments.Frontiers in plant science · 2026Article
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Authors and funding
6 authors.
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Abstract
High resolution three-dimensional (3D) point clouds enable the mapping of cotton boll spatial distribution, aiding breeders in better understanding the correlation between boll positions on branches and overall yield and fiber quality. This study developed a segmentation workflow for point clouds of 18 cotton genotypes to map the spatial distribution of bolls on the plants. The data processing workflow includes two independent approaches to map the vertical and horizontal distribution of cotton bolls. The vertical distribution was mapped by segmenting bolls using PointNet++ and identifying individual instances through Euclidean clustering. For horizontal distribution, TreeQSM segmented the plant into the main stem and individual branches. PointNet++ and Euclidean clustering were then used to achieve cotton boll instance segmentation. The horizontal distribution was determined by calculating the Euclidean distance of each cotton boll relative to the main stem. Additionally, branch types were classified using point cloud meshing completion and the Dijkstra shortest path algorithm. The results highlight that the accuracy and mean intersection over union (mIoU) of the 2-class segmentation based on PointNet++ reached 0.954 and 0.896 on the whole plant dataset, and 0.968 and 0.897 on the branch dataset, respectively. The coefficient of determination (R
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