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TBC-YOLOv7: a refined YOLOv7-based algorithm for tea bud grading detection.

Siyang Wang1,2,3, Dasheng Wu1,2,3, Xinyu Zheng1,2,3

  • 1College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou, China.

Frontiers in Plant Science
|September 4, 2023
PubMed
Summary

A new TBC-YOLOv7 algorithm improves tea bud grading for automated picking. This machine vision system enhances accuracy in complex backgrounds, aiding tea harvesting and quality assessment.

Keywords:
BiFPNCASIoUYOLOv7contextual transformertea bud grading detection

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Agricultural Technology

Background:

  • Automated tea picking relies on accurate tea bud grading.
  • Existing target detection algorithms struggle with complex backgrounds in tea fields.

Purpose of the Study:

  • To propose an improved YOLOv7 algorithm (TBC-YOLOv7) for enhanced tea bud grading detection.
  • To improve the accuracy and efficiency of machine vision systems in automated tea harvesting.

Main Methods:

  • Integrated a transformer module into YOLOv7 for improved self-attention and global feature learning.
  • Employed a bidirectional feature pyramid network for multi-scale feature fusion.
  • Incorporated coordinate attention and the SIOU loss function to refine detection.

Main Results:

  • TBC-YOLOv7 achieved a mean average precision of 87.5%, outperforming original YOLOv7 by 3.4%.
  • Demonstrated high precision (88.2%) and recall (81%) across various tea bud grades.
  • Showcased superior performance with fewer parameters and high correlation with manual annotations (r=0.89).

Conclusions:

  • The TBC-YOLOv7 model significantly enhances vision recognition for tea bud grading.
  • The improved model provides a viable solution for practical, automated tea bud collection and grade assessment.