MULTI-TARGET DETECTION METHOD FOR MAIZE PESTS BASED ON IMPROVED YOLOv8

When maize is afflicted by pests and diseases, it can lead to a drastic reduction in yield, causing significant economic losses to farmers. Therefore, accurate and efficient detection of maize pest species is crucial for targeted pest control during the management process. To achieve precise detection of maize pest species, this paper proposes a deep learning detection algorithm for maize pests based on an improved YOLOv8n model: Firstly, a maize pest dataset was constructed, comprising 2,756 images of maize pests, according to the types of pests and diseases. Secondly, a deformable attention mechanism (DAttention) was introduced into the backbone network to enhance the model's capability to extract features from images of maize pests. Thirdly, spatial and channel recombination convolution (SCConv) was incorporated into the feature fusion network to reduce the miss rate of small-scale pests. Lastly, the improved model was trained and tested using the newly constructed maize pest dataset. Experimental results demonstrate that the improved model achieved a detection average precision (mAP) of 94.8% at a speed of 171 frames per second (FPS), balancing accuracy and efficiency. The improved model can be deployed in low-computing-power mobile devices to achieve realtime detection, and in the future, more types of maize pests can be detected by adding multi-category datasets and training with new models with more computational power, which is important for the healthy development of maize agriculture