Plant Disease Detection and Classification Method Based on the Optimized Lightweight YOLOv5 Model

Haiqing Wang, Shuqi Shang, Dongwei Wang, Xiaoning He, Kai Feng and Hao Zhu
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Haiqing Wang: College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China
Shuqi Shang: College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China
Dongwei Wang: College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China
Xiaoning He: College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China
Kai Feng: College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China
Hao Zhu: College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China

Agriculture, 2022, vol. 12, issue 7, 1-23

Abstract: Traditional plant disease diagnosis methods are mostly based on expert diagnosis, which easily leads to the backwardness of crop disease control and field management. In this paper, to improve the speed and accuracy of disease classification, a plant disease detection and classification method based on the optimized lightweight YOLOv5 model is proposed. We propose an IASM mechanism to improve the accuracy and efficiency of the model, to achieve model weight reduction through Ghostnet and WBF structure, and to combine BiFPN and fast normalization fusion for weighted feature fusion to speed up the learning efficiency of each feature layer. To verify the effect of the optimized model, we conducted a performance comparison test and ablation test between the optimized model and other mainstream models. The results show that the operation time and accuracy of the optimized model are 11.8% and 3.98% higher than the original model, respectively, while F1 score reaches 92.65%, which highlight statistical metrics better than the current mainstream models. Moreover, the classification accuracy rate on the self-made dataset reaches 92.57%, indicating the effectiveness of the plant disease classification model proposed in this paper, and the transfer learning ability of the model can be used to expand the application scope in the future.

Keywords: disease classification; YOLOv5 model; attention mechanism; transfer learning (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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