RTCrownNet: A dual-channel deep learning framework for accurate rubber tree crown extraction from UAV LiDAR point clouds

As an important economic crop in tropical regions, the natural rubber yield of rubber trees is closely related to their crown structure. Accurately extracting tree crowns is fundamental for obtaining key growth parameters and evaluating yield potential. However, existing methods face three major challenges when processing LiDAR point cloud data of rubber trees: ambiguous boundaries due to complex canopy structures, difficult segmentation caused by background interference, and learning rate optimization issues. To address these challenges, this paper proposes a single-tree crown extraction method based on UAV LiDAR point clouds (RTCrownNet). First, a Dual-Stream Collaborative Feature Fusion Module (DS-CFM) is designed to integrate local geometric details and global semantic information, enabling accurate identification of complex crown boundaries. Second, a Residual-Augmented Graph Convolution Module (RAGC) is proposed to encode the topological relationships of point clouds using graph structures, enhancing the model’s ability to distinguish between overlapping leaves and ground areas. Additionally, an Adaptive Coati Differential Evolution Algorithm (ACDE) is developed, which constructs a dual-track parallel search framework to automatically optimize learning rates, accelerate model convergence, and enhance generalization performance. Experimental results show that RTCrownNet outperforms three traditional methods and seven deep learning networks on a self-built rubber tree point cloud dataset, achieving an instance mean intersection over union (mIoU) of 87.31% and an F-score of 95.24%. In generalization experiments, the method demonstrates excellent performance on the Wytham Woods temperate deciduous forest dataset and the FOR-instance dataset covering different forest types in five countries, verifying the model’s versatility. This study provides reliable technical support for precise monitoring, intelligent management, and resource evaluation of rubber trees, and holds significant importance for promoting the sustainable development of the rubber industry. • Dual-stream fusion module enables accurate crown boundary delineation. • Graph convolution with residual structure models topology and suppresses noise. • Adaptive Coati Differential Evolution Algorithm automates learning rate tuning. • Our method achieves 87.31% instance mIoU in rubber tree point cloud segmentation.