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TM-WSNet: A precise segmentation method for individual rubber trees based on UAV LiDAR point cloud.

Lele Yan¹, Guoxiong Zhou¹, Miying Yan¹

¹Central South University of Forestry and Technology, Changsha, 410004, China.

Plant Phenomics (Washington, D.C.)

|December 19, 2025

Summary

This summary is machine-generated.

We developed TM-WSNet, a novel segmentation network for precise rubber tree identification in plantations. This method accurately estimates tree parameters, aiding in yield prediction and health monitoring.

Keywords:

Hybrid feature extraction module Multi-level feature fusion Rubber tree segmentation Scale optimization algorithm Wavelet grid sampling

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Area of Science:

Agricultural Engineering
Computer Vision
Remote Sensing

Background:

Individual rubber tree segmentation is crucial for plantation management but challenging due to canopy interference and tree variability.
Existing methods struggle with accurately distinguishing tree boundaries and handling diverse tree morphologies.

Purpose of the Study:

To develop a high-precision segmentation network (TM-WSNet) for accurate individual rubber tree segmentation in complex plantation environments.
To improve the estimation of key rubber tree parameters for precision agriculture applications.

Main Methods:

Proposed TM-WSNet, incorporating a hybrid feature extraction module (SGTramba) using Grouped Transformer and Mamba architectures.
Introduced a Wavelet Grid Feature Fusion Encoder (WGMS) for enhanced structural feature recognition and multiscale fusion.
Implemented a Scale Optimization Algorithm (SCPO) for adaptive learning rate adjustment across different resolutions.

Main Results:

TM-WSNet achieved high segmentation accuracy and robustness on multiple datasets (RubberTree, ShapeNetPart, ForestSemantic).
Field tests demonstrated accurate prediction of rubber tree height (R²=1.00), crown width (R²=0.99), and diameter at breast height (R²=0.89).

Conclusions:

TM-WSNet offers a significant advancement in individual rubber tree segmentation technology.
The network shows strong potential for supporting precision rubber yield estimation and health monitoring in complex agricultural settings.