Improving the Accuracy of Agricultural Pest Identification: Application of AEC-YOLOv8n to Large-Scale Pest Datasets

Abstract

Crop diseases and pests are important factors restricting agricultural production. Traditional pest detection methods are mainly targeted at a single pest species, which is difficult to meet the needs of multi-target identification and rapid response in real scenes. Therefore, this paper improves the YOLOv8n model for efficient multi-target pest detection. Two feature enhancement modules, EMSFEM and AFEM_SIE, are proposed in this paper. The EMSFEM module enriches the model’s receptive field through the combination of multi-scale asymmetric convolution kernel and different expansion rates and can better extract the width, height, texture, and edge information of the target. The AFEM_SIE module captures the similarities and differences between upper and lower features through spatial information exchange and enhances feature representation through inter-feature information exchange. In addition, an improved feature fusion operation, Concat_Weighting, is proposed on the basis of Concat. The module uses the learned weights to carry out channel weighting and feature graph weighting for input features, which realizes more flexible and effective feature fusion. The results of experiments conducted on the publicly available large-scale crop pest and disease dataset IP102 show that the performance of the AEC-YOLOv8n model is significantly improved compared with the original YOLOv8n model, with mAP50 increased by 8.9%, accuracy increased by 6.8%, and recall rate increased by 6.3%. The AEC-YOLOv8n model proposed in this study can effectively identify and deal with a variety of crop pests and has achieved the best detection accuracy on the IP102 dataset, which has high application value.

Keywords

• IP102
• EMSFEM
• AFEM_SIE
• texture and edge information
• concat_weighting
• multiple target recognition