A Fine-Scale Segmentation Method for Individual Rubber Trees Based on UAV LiDAR Point Cloud

Abstract

As a key tropical economic crop, rubber trees play a vital role in both the global rubber industry and the health of ecological systems. Fine-grained segmentation of rubber tree point clouds is essential for accurately extracting structural parameters and achieving effective monitoring and management. However, existing unsupervised segmentation methods are often affected by ground noise and overlapping tree crowns, leading to suboptimal segmentation results and posing significant challenges for individual rubber tree segmentation. To address these issues, this study proposes a fine-grained segmentation network for rubber trees based on UAV LiDAR point clouds, termed RTreeNet. First, we designed a multiscale feature aggregation (MSFA) module to tackle the issue of leaf overlap by capturing geometric features at the edges of tree crowns. Second, we proposed a cosine-space cross attention (CSCA) module, which calculates the cosine similarity of vertical and horizontal features for each point, effectively eliminating interference from ground noise. Additionally, a adaptive coati particle optimization algorithm (ACPA) was proposed to determine the optimal learning rate for the network, further enhancing segmentation accuracy. The experimental evaluation demonstrates that the proposed RTreeNet outperforms seven state-of-the-art (SOTA) point cloud segmentation architectures and four conventional segmentation algorithms on our custom dataset, achieving a mean intersection over union (mIoU) of 86.3% and an F-score of 92.5%. In the generalization experiment, RTreeNet showed high accuracy and stability on three public datasets. The method also measured the specific structural parameters (tree height, crown diameter, and breast diameter) of rubber trees in the two regions, providing strong technical support for the refined management of rubber trees, agricultural planning, pest control, and rubber yield prediction.