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Classification of peanut pod rot based on improved YOLOv5s.

Yu Liu1, Xiukun Li1,2, Yiming Fan1

  • 1Hebei Agricultural University, Baoding, China.

Frontiers in Plant Science
|April 30, 2024
PubMed
Summary

This study introduces an improved YOLOv5s model for grading peanut pod rot, enhancing disease resistance breeding. The machine vision system accurately identifies and classifies rotten peanuts, aiding in selecting resistant cultivars.

Keywords:
Shuffle Attentiongrading classificationimproved YOLOv5smachine visionpeanut pod rot

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Area of Science:

  • Agricultural Science
  • Plant Pathology
  • Computer Vision

Background:

  • Peanut pod rot significantly impacts crop yield and quality, necessitating effective control and breeding strategies.
  • Manual grading of peanut pod rot is inefficient, labor-intensive, and prone to inaccuracies, hindering breeding programs.
  • Developing automated methods for disease assessment is crucial for advancing peanut breeding for disease resistance.

Purpose of the Study:

  • To develop and validate a machine vision-based grading system for peanut pod rot.
  • To enhance the accuracy and efficiency of peanut pod rot classification for breeding applications.
  • To provide technological support for screening peanut cultivars with high resistance to pod rot.

Main Methods:

  • An improved YOLOv5s (You Only Look Once version 5 small) model incorporating a Shuffle Attention module was utilized.
  • The Complete Intersection over Union (CIoU) loss function was replaced with Enhanced Intersection over Union (EIoU) to improve identification accuracy.
  • A grade classification module was integrated to process RGB images and output pod rot data.

Main Results:

  • The enhanced YOLOv5s model achieved a Precision of 93.8% and a mean Average Precision (mAP) of 92.4%.
  • Recognition accuracy for non-rotted and rotten peanut pods reached 95.7% and 90.8%, respectively.
  • The improved model demonstrated significant performance gains over baseline YOLOv5s and YOLOv8 models.

Conclusions:

  • The proposed machine vision system offers an accurate and efficient method for grading peanut pod rot.
  • This technology provides valuable support for breeding programs aimed at developing peanut varieties with enhanced pod rot resistance.
  • The study highlights the potential of advanced computer vision techniques in agricultural disease management and crop improvement.