VGDS-PointNet++ for organ segmentation and phenotypic trait estimation in greenhouse tomato seedlings

作者：Feiyi Wang, Tianyu Zhu, Lyuwen Huang, Ce Feng, Xi LU, Wenchong Min, Zheng Wang, Xiaohui Hu, Yanming Nie · 发表于：Frontiers in Plant Science · 年份：2026 · DOI：10.3389/fpls.2026.1753706 · 被引用次数：1 · 研究领域：Smart Agriculture and AI、Greenhouse Technology and Climate Control、Leaf Properties and Growth Measurement

Introduction To accurately segment point clouds and quickly calculate leaf length and stem diameter, thereby enabling phenotypic analysis and variety selection of greenhouse tomato plants, this paper proposes a voxel grid downsampling (VGDS)–PointNet++-based model for point cloud segmentation and trait calculation. Methods The point clouds of the tomato canopy were acquired using a depth camera. After labeling, point cloud augmentation was performed, and the tomato point cloud dataset (TPCD) containing 1,552 sets of data was rebuilt. Voxel grid downsampling was applied to replace the original sampling strategy of PointNet++. Models of PointNet, PointNet++, VGDS-PointNet++, and Point Transformer were trained with the TPCD and compared on segmentation quality with accuracy and mean Intersection over Union (mIoU). After segmentation, skeletal morphology was fitted for non-occluded leaves by applying a series of surface fitting techniques. The leaf lengths and stem diameters were automatically calculated and compared with the manually measured values. Results The validation results showed that the average runtime of voxel grid downsampling was 0.132 s, which was lower than under the same number of sampled points. Compared to the other three models, the proposed model had higher accuracy and mIoU, reaching up to 96.80% and 88.95%, respectively. The proposal’s accuracy and mIoU increased by 3.9% and 4.45% over PointNet++, respectively. The determination coefficient R 2 between the ...