Multimodal collaborative UAV framework for single rubber tree parsing.

Accurate individual-tree segmentation in rubber tree plantations is essential for resource inventory and agro-forestry management, but it remains difficult in densely planted stands because adjacent crowns often overlap, point density varies markedly along the vertical canopy structure, and tree morphology differs across growth stages. To address these challenges, we introduce MDA-SegNet, a multimodal point-cloud instance segmentation framework designed for densely planted rubber plantations. The Multimodal Deformable Encoding (MDE) module integrates top-view orthophoto cues with light detection and ranging (LiDAR) geometry, providing complementary crown-boundary information for more consistent separation of adjacent trees. The Z-Order Selective Mamba (ZOS-Mamba) module converts three-dimensional spatial structures into locality-preserving sequences and models long-range vertical dependencies under uneven point-density distributions. The Adaptive Lemming Optimization Clustering (ALOC) module further refines instance separation by adapting to canopy overlap and morphological heterogeneity. Experiments were conducted on our self-constructed rubber tree dataset (RT-Set) and five public forest datasets. Compared with current state-of-the-art models, MDA-SegNet achieved higher F-score and mean Intersection over Union (mIoU), showing stronger robustness and cross-domain generalizability. On RT-Set, it obtained an F-score of 87.32% and a Recall of 82.90%, with improvements of 6.86 and 7.24 percentage points, respectively. These results indicate that MDA-SegNet can provide reliable individual-tree segmentation for dense rubber plantations.