MobileNet-CA-YOLO: An Improved YOLOv7 Based on the MobileNetV3 and Attention Mechanism for Rice Pests and Diseases Detection

Liangquan Jia, Tao Wang, Yi Chen, Ying Zang, Xiangge Li, Haojie Shi and Lu Gao ()
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Liangquan Jia: School of Information Engineering, Huzhou University, Huzhou 313000, China
Tao Wang: School of Information Engineering, Huzhou University, Huzhou 313000, China
Yi Chen: School of Arts and Science, Fujian Medical University, Fuzhou 350122, China
Ying Zang: School of Information Engineering, Huzhou University, Huzhou 313000, China
Xiangge Li: School of Information Engineering, Huzhou University, Huzhou 313000, China
Haojie Shi: College of Modern Agriculture, Zhejiang A&F University, Hangzhou 311300, China
Lu Gao: School of Information Engineering, Huzhou University, Huzhou 313000, China

Agriculture, 2023, vol. 13, issue 7, 1-18

Abstract: The efficient identification of rice pests and diseases is crucial for preventing crop damage. To address the limitations of traditional manual detection methods and machine learning-based approaches, a new rice pest and disease recognition model based on an improved YOLOv7 algorithm has been developed. The model utilizes the lightweight network MobileNetV3 for feature extraction, reducing parameterization, and incorporates the coordinate attention mechanism (CA) and the SIoU loss function for enhanced accuracy. The model has been tested on a dataset of 3773 rice pest and disease images, achieving an accuracy of 92.3% and an mAP@.5 of 93.7%. The proposed MobileNet-CA-YOLO model is a high-performance and lightweight solution for rice pest and disease detection, providing accurate and timely results for farmers and researchers.

Keywords: MobileNetV3; rice pests and diseases; YOLOv7; coordinate attention mechanism; SIoU (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 complete reference list from CitEc
Citations: View citations in EconPapers (1)

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