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Efficient Tobacco Pest Detection in Complex Environments Using an Enhanced YOLOv8 Model

Daozong Sun, Kai Zhang, Hongsheng Zhong, Jiaxing Xie, Xiuyun Xue, Mali Yan, Weibin Wu and Jiehao Li ()
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Daozong Sun: College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China
Kai Zhang: College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China
Hongsheng Zhong: College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China
Jiaxing Xie: College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China
Xiuyun Xue: College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China
Mali Yan: College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China
Weibin Wu: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Jiehao Li: College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China

Agriculture, 2024, vol. 14, issue 3, 1-21

Abstract: Due to the challenges of pest detection in complex environments, this research introduces a lightweight network for tobacco pest identification leveraging enhancements in YOLOv8 technology. Using YOLOv8 large (YOLOv8l) as the base, the neck layer of the original network is replaced with an asymptotic feature pyramid network (AFPN) network to reduce model parameters. A SimAM attention mechanism, which does not require additional parameters, is incorporated to improve the model’s ability to extract features. The backbone network’s C2f model is replaced with the VoV-GSCSP module to reduce the model’s computational requirements. Experiments show the improved YOLOv8 model achieves high overall performance. Compared to the original model, model parameters and GFLOPs are reduced by 52.66% and 19.9%, respectively, while mAP@0.5 is improved by 1%, recall by 2.7%, and precision by 2.4%. Further comparison with popular detection models YOLOv5 medium (YOLOv5m), YOLOv6 medium (YOLOv6m), and YOLOv8 medium (YOLOv8m) shows the improved model has the highest detection accuracy and lightest parameters for detecting four common tobacco pests, with optimal overall performance. The improved YOLOv8 detection model proposed facilitates precise, instantaneous pest detection and recognition for tobacco and other crops, securing high-accuracy, comprehensive pest identification.

Keywords: pest recognition; object detection; YOLOv8; lightweight network; attention mechanism (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: 2024
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