Lightweight One-Stage Maize Leaf Disease Detection Model with Knowledge Distillation

Yanxin Hu, Gang Liu, Zhiyu Chen, Jiaqi Liu and Jianwei Guo ()
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Yanxin Hu: School of Computer Science and Engineering, Changchun University of Technology, Changchun 130102, China
Gang Liu: School of Computer Science and Engineering, Changchun University of Technology, Changchun 130102, China
Zhiyu Chen: School of Computer Science and Engineering, Changchun University of Technology, Changchun 130102, China
Jiaqi Liu: School of Computer Science and Engineering, Changchun University of Technology, Changchun 130102, China
Jianwei Guo: School of Computer Science and Engineering, Changchun University of Technology, Changchun 130102, China

Agriculture, 2023, vol. 13, issue 9, 1-22

Abstract: Maize is one of the world’s most important crops, and maize leaf diseases can have a direct impact on maize yields. Although deep learning-based detection methods have been applied to maize leaf disease detection, it is difficult to guarantee detection accuracy when using a lightweight detection model. Considering the above problems, we propose a lightweight detection algorithm based on improved YOLOv5s. First, the Faster-C3 module is proposed to replace the original CSP module in YOLOv5s, to significantly reduce the number of parameters in the feature extraction process. Second, CoordConv and improved CARAFE are introduced into the neck network, to improve the refinement of location information during feature fusion and to refine richer semantic information in the downsampling process. Finally, the channel-wise knowledge distillation method is used in model training to improve the detection accuracy without increasing the number of model parameters. In a maize leaf disease detection dataset (containing five leaf diseases and a total of 12,957 images), our proposed algorithm had 15.5% less parameters than YOLOv5s, while the mAP(0.5) and mAP(0.5:0.95) were 3.8% and 1.5% higher, respectively. The experiments demonstrated the effectiveness of the method proposed in this study and provided theoretical and technical support for the automated detection of maize leaf diseases.

Keywords: maize disease detection; knowledge distillation; object detection; YOLOv5s (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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