A lightweight rice pest detection algorithm based on improved YOLOv8

Timely and accurate detection of rice pests is highly important for pest control, as well as for improving rice yield and quality. However, owing to the high interclass similarity, significant intraclass age differences, and complex backgrounds among different pests, accurately and rapidly identifying a variety of rice pests via deep neural network models poses a significant challenge. To address this issue, this paper presents a fast and accurate method for rice pest detection and identification named Rice-YOLO (You Only Look Once). This model is based on YOLOv8-N and incorporates an efficient detection head designed for the complex characteristics of pests. Additionally, deep supervision layers were introduced into the network, along with the incorporation and improvement of the dynamic upsampling module. The experimental data included the large-scale pest public dataset IP102 and the sixteen-class rice pest dataset R2000. The experimental results demonstrated that Rice-YOLO outperformed previous object detection algorithms, with 78.1% mAP@0.5, 62.9% mAP@0.5:0.95, and 74.3% F1 scores.