Accurate individual tree crown (ITC) segmentation from scanned point clouds is a fundamental task in forest biomass monitoring and forest ecology management. Light detection and ranging (LiDAR) as a mainstream tool for forest survey is advancing the pattern of forest data acquisition. In this study, we performed a novel deep learning framework directly processing the forest point clouds belonging to the four forest types (i.e., the nursery base, the monastery garden, the mixed forest, and the defoliated forest) to realize the ITC segmentation. The specific steps of our approach were as follows: first, a voxelization strategy was conducted to subdivide the collected point clouds with various tree species from various forest types into many voxels. These voxels containing point clouds were taken as training samples for the PointNet deep learning framework to identify the tree crowns at the voxel scale. Second, based on the initial segmentation results, we used the height-related gradient information to accurately depict the boundaries of each tree crown. Meanwhile, the retrieved tree crown breadths of individual trees were compared with field measurements to verify the effectiveness of our approach. Among the four forest types, our results revealed the best performance for the nursery base (tree crown detection rate r = 0.90; crown breadth estimation R² > 0.94 and root mean squared error (RMSE) < 0.2m). A sound performance was also achieved for the monastery garden and mixed forest, which had complex forest structures, complicated intersections of branches and different building types, with r = 0.85, R² > 0.88 and RMSE < 0.6 m for the monastery garden and r = 0.80, R² > 0.85 and RMSE < 0.8 m for the mixed forest. For the fourth forest plot type with the distribution of crown defoliation across the woodland, we achieved the performance with r = 0.82, R² > 0.79 and RMSE < 0.7 m. Our method presents a robust framework inspired by the deep learning technology and computer graphics theory that solves the ITC segmentation problem and retrieves forest parameters under various forest conditions.

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.

Digital twins of forests (trees) are computational virtual recreations of forests (trees) in which the entity distributions and physical processes in real-world forests (trees) are duplicated. It is expected that conventional forest science and management can be undertaken in a digital twin of forests (trees) if the recreation of a real-world forest (tree) has accurate and comprehensive enough information. However, due to the various differences between the current tree model and the real tree, these envisioned digital twins of the forests (trees) stay a theoretical concept. In this study, we developed a processing strategy that partially integrated computational virtual measurement (CVM) process into the tree modeling workflow. Owing to the feature of CVM, partial tree skeleton reconstruction procedures were considered to have higher mechanical objectivity compared to conventional mathematical modeling methods. The reason was that we developed a novel method called virtual diameter tape (VDT), which could provide a certain percentage of modeling elements using CVM. Technically, VDT was able to virtually measure diameters and spatial distribution of cross-sectional area of trees, including the basal area, from point clouds. VDT simulated the physical scenario of diameter tapes, observing point clouds of trees. Diameter and the cross-sectional area of stem and branches were obtained by two consecutive physical measurement processes, one in the forest sample site and another in the virtual space. At the same time, VDT obtained better or a similar accuracy compared to the mathematical methods, i.e., Hough transform-based methods, using the same data sets. The root-mean-square deviation (RMSE) of retrieval of diameter at breast height (DBH) using VDT was 1.02 cm, while DBH obtained from three conventional methods varied from 1.29 cm to 1.73 cm. Based on VDT measurement results, tree skeleton reconstruction and actual forest scenario rendering of our sample plots were further implemented. Beyond the visual consistency, we believe that our work might be a small and solid step in the technological evolution from tree models to the digital twin of forests (trees).

The precise acquisition of phenotypic parameters for individual trees in plantation forests is important for forest management and resource exploration. The use of Light-Detection and Ranging (LiDAR) technology mounted on Unmanned Aerial Vehicles (UAVs) has become a critical method for forest resource monitoring. Achieving the accurate segmentation of individual tree crowns (ITCs) from UAV LiDAR data remains a significant technical challenge, especially in broad-leaved plantations such as rubber plantations. In this study, we designed an individual tree segmentation framework applicable to dense rubber plantations with complex canopy structures. First, the feature extraction module of PointNet++ was enhanced to precisely extract understory branches. Then, a graph-based segmentation algorithm focusing on the extracted branch and trunk points was designed to segment the point cloud of the rubber plantation. During the segmentation process, a directed acyclic graph is constructed using components generated through grey image clustering in the forest. The edge weights in this graph are determined according to scores calculated using the topologies and heights of the components. Subsequently, ITC segmentation is performed by trimming the edges of the graph to obtain multiple subgraphs representing individual trees. Four different plots were selected to validate the effectiveness of our method, and the widths obtained from our segmented ITCs were compared with the field measurement. As results, the improved PointNet++ achieved an average recall of 94.6% for tree trunk detection, along with an average precision of 96.2%. The accuracy of tree-crown segmentation in the four plots achieved maximal and minimal R² values of 98.2% and 92.5%, respectively. Further comparative analysis revealed that our method outperforms traditional methods in terms of segmentation accuracy, even in rubber plantations characterized by dense canopies with indistinct boundaries. Thus, our algorithm exhibits great potential for the accurate segmentation of rubber trees, facilitating the acquisition of structural information critical to rubber plantation management.

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.

Accurate individual tree segmentation (ITS) in dense rubber plantations is a challenging task due to overlapping canopies, indistinct tree apexes, and intricate branch structures. To address these challenges, we propose a bottom-up, multi-feature fusion method for segmenting rubber trees using UAV-LiDAR point clouds. Our approach first involves performing a trunk extraction based on branch-point density variations and neighborhood directional features, which allows for the precise separation of trunks from overlapping canopies. Next, we introduce a multi-feature fusion strategy that replaces single-threshold constraints, integrating geometric, directional, and density attributes to classify core canopy points, boundary points, and overlapping regions. Disputed points are then iteratively assigned to adjacent trees based on neighborhood growth angle consistency, enhancing the robustness of the segmentation. Experiments conducted in rubber plantations with varying canopy closure (low, medium, and high) show accuracies of 0.97, 0.98, and 0.95. Additionally, the crown width and canopy projection area derived from the segmented individual tree point clouds are highly consistent with ground truth data, with R² values exceeding 0.98 and 0.97, respectively. The proposed method provides a reliable foundation for 3D tree modeling and biomass estimation in structurally complex plantations, advancing precision forestry and ecosystem assessment by overcoming the critical limitations of existing ITS approaches in high-closure tropical agroforests.