Accurate counting of rubber trees is important for yield estimation, refined field management, and the sustainable development of the rubber industry. In deep-learning-based counting, reliable canopy delineation is a prerequisite for accurate segmentation and subsequent statistical analysis. However, maintaining stable canopy segmentation under long-term and complex field conditions remains difficult, mainly because of overlapping crowns, interference from background vegetation, and pronounced seasonal changes. To address these issues, this study proposes CSAF, a rubber tree canopy segmentation model designed for high-precision canopy segmentation and counting from UAV imagery. CSAF consists of three main components: the Boundary Continuity Modelling Module (BCMM), the Physical Morphology Constraint Module (PMCM), and the Cross-Temporal Learning Adaptively Module (CTLAM). BCMM improves the representation of complex canopy boundaries by using the Fourier transform to suppress feature noise and state-space modelling to refine boundary responses. PMCM introduces a Poisson diffusion prior to constrain the segmentation results according to canopy morphology, thereby reducing the influence of irregular background vegetation. CTLAM adjusts the replay ratio and scale through a state-feedback mechanism, which helps mitigate inter-seasonal variation and improve cross-temporal generalization. In addition, this study presents RT-Set, a UAV-based rubber tree canopy dataset containing 5281 high-resolution images that cover the full growth cycle of rubber trees, from the budding stage to the defoliation stage. RT-Set provides a dedicated benchmark for precise rubber tree canopy segmentation and counting. On RT-Set, CSAF achieves an AP₅₀ of 76.63%, outperforming the compared methods. Experiments on three additional public datasets further show that CSAF remains competitive with mainstream approaches, including DetecTree2 and Cascade Mask R-CNN. In the counting experiments, CSAF also shows high accuracy and stable performance, suggesting its practical value for rubber tree counting under complex field conditions. The RT-Set dataset is available at https://github.com/zengjiangquan1/RT-Set.

Accurate individual-tree segmentation in rubber tree plantations is essential for resource inventory and agroforestry 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.

Rubber products have become an important strategic resource in the global economy. However, individual rubber tree segmentation in plantation environments remains challenging due to canopy background interference and significant morphological variations among trees. To address these issues, we propose a high-precision segmentation network,TM-WSNet (Spatial Geometry Enhanced Hybrid Feature Extraction Module–Wavelet Grid Feature Fusion Encoder Segmentation Network). First, we introduce SGTramba, a hybrid feature extraction module combining Grouped Transformer and Mamba architectures, designed to reduce confusion between tree crown boundaries and surrounding vegetation or background elements. Second, we propose the WGMS encoder, which enhances structural feature recognition by applying wavelet-based spatial grid downsampling and multiscale feature fusion, effectively handling variations in canopy shape and tree height. Third, a scale optimization algorithm (SCPO) is developed to adaptively search for the optimal learning rate, addressing uneven learning across different resolution scales. We evaluate TM-WSNet on a self-constructed dataset (RubberTree) and two public datasets (ShapeNetPart and ForestSemantic), where it consistently achieves high segmentation accuracy and robustness. In practical field tests, our method accurately predicts key rubber tree parameters—height, crown width, and diameter at breast height with coefficients of determination (R²) of 1.00, 0.99, and 0.89, respectively. These results demonstrate TM-WSNet's strong potential for supporting precision rubber yield estimation and health monitoring in complex plantation environments.

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.