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

文献类型： 外文期刊

作者： Wang, Hengrui 1 ; Ye, Zilin 1 ; Zhang, Qin 1 ; Wang, Mingfang 1 ; Zhou, Guoxiong 1 ; Wang, Xiangjun 2 ; Li, Li 1 ; Lin, Shuqi 1 ;

作者机构： 1.Cent South Univ Forestry & Technol, Changsha 410004, Hunan, Peoples R China

2.Chinese Acad Trop Agr Sci, Rubber Res Inst, Haikou 571101, Peoples R China

关键词： Rubber tree point cloud segmentation; RsegNet; CosineU-Net; Dual-channel clustering module; Dynamic clustering optimization algorithm

期刊名称：PLANT PHENOMICS （ 影响因子：6.4； 五年影响因子：7.1 ）

ISSN： 2643-6515

年卷期： 2025 年 7 卷 3 期

页码：

收录情况： SCI

摘要： 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.

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