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Lightweight tea bud detection method based on improved YOLOv5.

Kun Zhang1, Bohan Yuan2, Jingying Cui1

  • 1College of Physics and Electronic Engineering, Xinyang Normal University, Xinyang, 464000, China.

Scientific Reports
|December 29, 2024
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Summary
This summary is machine-generated.

This study introduces a lightweight tea bud detection model using modified YOLOv5, enhancing accuracy and efficiency for intelligent tea picking robots. The improved model offers reduced parameters and operations while boosting detection performance.

Keywords:
EfficientNetV2Lightweight modelTea bud detectionYOLOv5

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Area of Science:

  • Agricultural Engineering
  • Computer Vision
  • Machine Learning

Background:

  • Automated tea bud plucking is crucial for improving efficiency and accuracy in tea production.
  • Current mobile deployment of intelligent picking systems faces challenges due to computational resource limitations.

Purpose of the Study:

  • To develop a lightweight tea bud identification model for intelligent tea picking.
  • To enhance picking accuracy and labor efficiency while reducing deployment pressure on mobile terminals.

Main Methods:

  • Modified YOLOv5 architecture incorporating EfficientNetV2 as the backbone.
  • Integration of Ghost modules (ghost convolution, C3ghost) in the neck network for parameter reduction.
  • Implementation of CARAFE upsampling module to enhance feature aggregation and detection precision.

Main Results:

  • Achieved a mean average precision of 85.79% for tea bud detection.
  • Reduced model parameters by 40.94% and floating-point operations by 68.15% compared to original YOLOv5.
  • Increased mean average precision by 1.67% points with a lightweight design.

Conclusions:

  • The proposed lightweight YOLOv5 model effectively detects tea buds with improved efficiency and reduced computational load.
  • This research provides a theoretical foundation for developing advanced intelligent tea-picking robots.
  • The model demonstrates superior performance in tea shot detection compared to other YOLO series algorithms.