Research on the Agricultural Pest Identification Mechanism Based on an Intelligent Algorithm

Qixun Xiao, Wenying Zheng, Yifan He, Zijie Chen, Fanxin Meng and Liyan Wu ()
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Qixun Xiao: School of Electronic Information Engineering, Zhuhai College of Science and Technology, No. 8, Anji East Road, Jinwan District, Zhuhai 519041, China
Wenying Zheng: College of Pharmacy and Food Science, Zhuhai College of Science and Technology, No. 8, Anji East Road, Jinwan District, Zhuhai 519041, China
Yifan He: College of Pharmacy and Food Science, Zhuhai College of Science and Technology, No. 8, Anji East Road, Jinwan District, Zhuhai 519041, China
Zijie Chen: School of Mechanical Engineering, Zhuhai College of Science and Technology, No. 8, Anji East Road, Jinwan District, Zhuhai 519041, China
Fanxin Meng: College of Pharmacy and Food Science, Zhuhai College of Science and Technology, No. 8, Anji East Road, Jinwan District, Zhuhai 519041, China
Liyan Wu: College of Pharmacy and Food Science, Zhuhai College of Science and Technology, No. 8, Anji East Road, Jinwan District, Zhuhai 519041, China

Agriculture, 2023, vol. 13, issue 10, 1-17

Abstract: The use of Internet of Things (IoT) technology for real-time monitoring of agricultural pests is an unavoidable trend in the future of intelligent agriculture. This paper aims to address the difficulties in deploying models at the edge of the pest monitoring visual system and the low recognition accuracy. In order to achieve that, a lightweight GCSS-YOLOv5s algorithm is proposed. Firstly, we introduce the lightweight network GhostNet, use the Ghostconv module to replace the traditional convolution, and construct the C3Ghost module based on the CSP structure, drastically reducing the number of model parameters. Secondly, during the feature fusion process, we introduce the content-aware reassembly of features (CARAFE) lightweight up-sampling operator to enhance the feature integration capability of the pests by reducing the impact of redundant features after fusion. Then, we adopt SIoU as the bounding box regression loss function, which enhances the convergence speed and detection accuracy of the model. Finally, the traditional non-maximum suppression (NMS) was improved to Soft-NMS to improve the model’s ability to recognize overlapping pests. According to the experimental results, the mean average precision (mAP) of the GCSS-YOLOv5s model reaches 90.5%. This is achieved with a 44% reduction in the number of parameters and a 7.4 G reduction in computation volume compared to the original model. The method significantly reduces the model’s resource requirements while maintaining accuracy, which offers a specific theoretical foundation and technological reference for the future field of intelligent monitoring.

Keywords: pest monitoring; GCSS-YOLOv5s; GhostNet; content-aware reassembly of features; SIoU; soft-non-maximum suppression (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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