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

Jiajun Liu, Bei Zhou, Jie Liu, Xike Zhang, Jiangshu Wei, Yao Zhang, Junjie Wu, Changping Wu and Di Hu

PLOS ONE, 2025, vol. 20, issue 11, 1-23

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, and a contrastive learning-based normalization module called ContraNorm. KAN-GLNet contains only 5.72M parameters and achieves 94.50% mIoU, 96.72% mAcc, and 97.77% OAcc in semantic segmentation tasks, outperforming all baseline models. In addition, the DBSCAN workflow was optimized, achieving a counting accuracy of 97.45% in the instance segmentation task. This method achieves an excellent balance between segmentation accuracy and model complexity, providing an efficient solution for high-throughput plant phenotyping. The code and dataset have been made publicly available at: https://anonymous.4open.science/r/KAN-GLNet-6432/.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0336622

DOI: 10.1371/journal.pone.0336622

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