KAN-GLNet: An enhanced PointNet++ model for canola silique segmentation and counting.

ABSTRACT: Accurate analysis of plant phenotypic traits is crucial for crop breeding and precision agriculture. This study proposes a lightweight semantic segmentation model named KAN-GLNet (Kolmogorov-Arnold Network with Global-Local Feature Modulation), based on an enhanced PointNet++ architecture and integrated with an optimized Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, to achieve high-precision segmentation and automatic counting of canola siliques. A multi-view point cloud acquisition platform was built, and high-fidelity canola point clouds were reconstructed using Neural Radiance Fields (NeRF) technology. The proposed model includes three key modules: Reverse Bottleneck Kolmogorov-Arnold Network Convolution, a Global-Local Feature Modulation (GLFN) block,

SUBMITTER: Liu J 

PROVIDER: S-EPMC12622843 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature