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Vision-Based Localization Method for Picking Points in Tea-Harvesting Robots.

Jingwen Yang1, Xin Li1, Xin Wang1

  • 1Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei 230036, China.

Sensors (Basel, Switzerland)
|November 9, 2024
PubMed
Summary

This study introduces an advanced visual positioning system for tea-picking robots, enhancing picking point accuracy in complex environments. The method improves tea bud detection and localization, enabling more efficient robotic tea harvesting.

Keywords:
RGB-Ddeep learningpicking point localizationtea

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Area of Science:

  • Robotics
  • Computer Vision
  • Agricultural Engineering

Background:

  • Tea-picking robots require precise recognition and localization of picking points in unstructured environments.
  • Existing methods face challenges in accuracy and robustness due to environmental variability.

Purpose of the Study:

  • To develop and validate a novel visual positioning method for tea-picking robots using RGB-D information fusion.
  • To enhance the accuracy and reliability of identifying and locating tea bud picking points.

Main Methods:

  • An improved T-YOLOv8n model was developed for enhanced detection and segmentation of tea buds and stems across multi-scale scenes.
  • A layered visual servoing strategy integrating a RealSense depth sensor and robotic arm was designed for near and far view coordination.
  • Fusion of stem mask information with depth data was employed to calculate precise 3D picking point coordinates.

Main Results:

  • The improved T-YOLOv8n model achieved 80.8% detection accuracy for tea buds in far-view datasets.
  • Mean Average Precision (mAP) for tea stem detection reached 93.6% (bounding box) and 93.7% (mask) in near-view datasets, surpassing the baseline.
  • The integrated system demonstrated an 86.4% picking point localization success rate with a low mean depth measurement error of 1.43 mm.

Conclusions:

  • The proposed RGB-D fusion visual positioning method significantly improves picking point recognition accuracy for tea-picking robots.
  • The system effectively reduces depth information fluctuations, providing robust technical support for intelligent and rapid premium tea harvesting.