Acta Agriculturae Zhejiangensis ›› 2023, Vol. 35 ›› Issue (2): 445-454.DOI: 10.3969/j.issn.1004-1524.2023.02.22

• Biosystems Engineering • Previous Articles     Next Articles

Study of YOLOv5-based germination detection method for wheat seeds

BAI Weiwei1,2(), ZHAO Xueni1, LUO Bin2, ZHAO Wei1,2, HUANG Shuo3, ZHANG Han2,*()   

  1. 1. Shaanxi University of Science and Technology, Xi’an 710016, China
    2. Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    3. Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
  • Received:2020-04-12 Online:2023-02-25 Published:2023-03-14
  • Contact: ZHANG Han

Abstract:

Seed germination test is an important part of testing the quality of crops. In order to improve the efficiency of seed germination detection and realize the automation of seed germination detection, taking wheat as the research object, a model based on YOLOv5 seed germination discrimination was constructed by machine vision technology combined with deep learning methods. Based on this, a set of improved discriminative methods for seed germination detection based on YOLOv5 was designed through the combined analysis of wheat germination test images in 7 days. The rapid detection of wheat seed germination rate, germination potential, germination index and average germination days was realized and the detection experiment was carried out. The results showed that the YOLOv5 model had 92.5% accuracy for wheat seed germination. By using the improved discrimination method of seed germination detection based on YOLOv5, the accuracy of seed germination discrimination was 98.5%, and the errors of germination potential, germination index, and average germination days were 0.5%, 2.39, and 0.1 d compared with manual detection. The improved discrimination method based on YOLOv5 seed germination detection proposed in this study could realize the rapid detection of seed germination rate, germination potential,germination index and average germination days, and provide a reference for the rapid detection of crop seed germination.

Key words: wheat seed, germination detection, deep learning, YOLOv5

CLC Number: