Rapid grading detection on hybrid rice bud seeds based on improved YOLOv5 model

ZHONG Haimin; MA Xu; LI Zehua; WANG Xicheng; LIU Saisai; LIU Weiwen; WANG Cheng’en; LIN Yongda

doi:10.7671/j.issn.1001-411X.202209015

Journal of South China Agricultural University > 2023 > 44(6): 960-967. > DOI: 10.7671/j.issn.1001-411X.202209015

ZHONG Haimin, MA Xu, LI Zehua, et al. Rapid grading detection on hybrid rice bud seeds based on improved YOLOv5 model[J]. Journal of South China Agricultural University, 2023, 44(6): 960-967. DOI: 10.7671/j.issn.1001-411X.202209015

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Rapid grading detection on hybrid rice bud seeds based on improved YOLOv5 model

1.
College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
2.
College of Engineering, South China Agricultural University, Guangzhou 510642, China
3.
Key Laboratory of Smart Agricultural Technology in Tropical South China, Ministry of Agriculture and Rural Affairs, P. R. China, Guangzhou 510642, China
4.
College of Electronic Engineering, South China Agricultural University, Guangzhou 510642, China

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Received Date: September 11, 2022
Available Online: November 12, 2023
Published Date: June 14, 2023

Graphical Abstract

Abstract

Abstract

Objective
In order to improve the grading detection accuracy and speed of hybrid rice seed vigor.
Method
A rapid grading detection method for hybrid rice bud seeds named YOLOv5-I model, which was an improved model based on YOLOv5, was proposed. The feature extraction ability of the target channel of YOLOv5-I model was improved by introducing the SE (Squeeze-and-excitation) attention mechanism module, and a CIoU loss function strategy was adopted to improve the convergence speed of this model.
Result
The YOLOv5-I algorithm effectively achieved the rapid grading detection of hybrid rice bud seeds, with high detection accuracy and speed. In the test set, the average accuracy of the YOLOv5-I model was 97.52%, the average detection time of each image was 3.745 ms, and the memory space occupied by the YOLOv5-I model was small with 13.7 MB. The detection accuracy and speed of YOLOv5-I model was better than those of YOLOv5s, Faster-RCNN, YOLOv4 and SSD models.
Conclusion
The YOLOv5-I algorithm is better than existing algorithms, improves detection accuracy and speed, and can meet the practical requirement for grading detection of hybrid rice bud seeds.
- Deep learning,
- YOLOv5s,
- Grading detection,
- Attention mechanism,
- Hybrid rice,
- Seed vigor

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References

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