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

Jiajun Liu¹, Bei Zhou^1,2, Jie Liu³

¹College of Information Engineering, Sichuan Agricultural University, Yaan, China.

|November 17, 2025

Summary

This study introduces KAN-GLNet, a lightweight AI model for precise canola silique segmentation and counting, crucial for crop breeding and precision agriculture. It offers high accuracy with minimal parameters, enabling efficient plant phenotyping.

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Area of Science:

Agricultural Science
Computer Vision
Plant Biology

Background:

Accurate plant phenotypic trait analysis is vital for crop breeding and precision agriculture.
Existing methods for plant phenotyping often lack efficiency and precision.

Purpose of the Study:

To develop a lightweight yet accurate semantic segmentation model for canola siliques.
To enable high-throughput plant phenotyping through automated analysis.

Main Methods:

Proposed KAN-GLNet (Kolmogorov-Arnold Network with Global-Local Feature Modulation), an enhanced PointNet++ architecture.
Integrated an optimized Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for counting.
Utilized Neural Radiance Fields (NeRF) for high-fidelity point cloud reconstruction.

Main Results:

KAN-GLNet achieved 94.50% mIoU, 96.72% mAcc, and 97.77% OAcc in semantic segmentation, outperforming baseline models.
Optimized DBSCAN achieved 97.45% accuracy in instance segmentation (counting).
The model has only 5.72M parameters, balancing accuracy and complexity.

Conclusions:

KAN-GLNet provides an efficient and accurate solution for canola silique segmentation and counting.
This method advances high-throughput plant phenotyping capabilities.
The developed model and dataset are publicly available for research.