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RsegNet: An Advanced Methodology for Individual Rubber Tree Segmentation and Structural Parameter Extraction from UAV

Hengrui Wang1, Zilin Ye1, Qin Zhang1

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

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Summary

A new method, RsegNet, accurately segments rubber tree point clouds using UAV LiDAR. This improves precision agriculture by enabling detailed assessment of tree architecture and traits for better plantation management.

Keywords:
CosineU-NetDual-channel clustering moduleDynamic clustering optimization algorithmRsegNetRubber tree point cloud segmentation

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Area of Science:

  • Agricultural Engineering
  • Remote Sensing
  • Computer Vision

Background:

  • Rubber trees are vital tropical crops requiring precise management.
  • Traditional point cloud segmentation methods fail to accurately extract rubber tree structural parameters and spatial layout.
  • Limitations hinder precision agriculture and refined management in rubber plantations.

Purpose of the Study:

  • To develop an optimized dual-channel clustering method for UAV LiDAR-based rubber tree point cloud segmentation.
  • To improve the assessment of rubber tree architecture and traits.
  • To enhance precision monitoring, plantation management, and health assessment.

Main Methods:

  • Proposed RsegNet, a UAV LiDAR-based network for rubber tree point cloud segmentation.
  • Designed CosineU-Net for feature extraction, addressing branch-and-leaf overlap using cosine similarity.
  • Developed a dual-channel clustering module with dynamic optimization for improved accuracy and reduced background interference.

Main Results:

  • RsegNet achieved superior performance compared to five state-of-the-art networks on a self-built dataset and the FOR-instance forest dataset.
  • The network reached an F-score of 86.1%, demonstrating high accuracy in segmentation.
  • Successfully calculated structural attributes like height, crown diameter, and volume for rubber trees.

Conclusions:

  • The proposed RsegNet method significantly improves rubber tree point cloud segmentation accuracy.
  • This approach provides robust support for precise monitoring, plantation management, and health assessment of rubber trees.
  • The method offers a valuable tool for advancing precision agriculture in rubber plantations.