RsegNet: An Advanced Methodology for Individual Rubber Tree Segmentation and Structural Parameter Extraction from UAV LiDAR Point Clouds.

Abstract

As an important tropical cash crop, rubber trees play a key role in the rubber industry and ecosystem. However, a significant challenge in precision agriculture and refined management of rubber plantation lies in the limitations of traditional point cloud segmentation methods, which struggle to accurately extract structural parameters and capture the spatial layout of individual rubber trees. Therefore, we propose an optimized dual-channel clustering method for the UAV LiDAR-based Rubber Tree Point Cloud Segmentation Network (RsegNet) for improved assessment of rubber tree architecture and traits. Firstly, we designed a cosine feature extraction network, termed CosineU-Net, to address the branch-and-leaf overlap problem by calculating the cosine similarity of the spatial and positional features of each point, leveraging deep learning approaches to improve feature representation. Secondly, we constructed a dual-channel clustering module reducing prediction error in rubber tree point cloud data, integrating multi-class association and background classification to tackle background interference. The cluster identification and separation accuracy in high-dimensional data processing is enhanced through a dynamic clustering optimization algorithm. In our self-built dataset and across five regions of the FOR-instance forest dataset, RsegNet achieved the best performance compared to five state-of-the-art networks, reaching an F-score of 86.1%. This method calculated structural attributes including height, crown diameter, and volume for rubber trees in three areas under different environments in Danzhou City, Hainan Province, providing robust support for precise monitoring, plantation management, and health assessment.

Plant phenotyping relevance

UAV LiDAR点群の個体分割・構造形質抽出手法を開発し、精度比較と樹高・樹冠径・体積の算出まで行っており、植物フェノタイピング手法が中心である。

Code and data availability

The paper's Data Availability statement explicitly deposits some datasets, model weights, and code at a public GitHub repository (https://github.com/aaaaasleep/Rsegnet), which is paper-specific and actionable. The 36 homemade rubber tree point cloud datasets are only available by contacting the corresponding author, so