Detection of Rice Leaf Diseases Based on Improved YOLOv8n

Abstract

Rice plays an important role in human food chain, but it is easily affected by related diseases in its growth, which seriously affects rice yield. At present, there are many methods to detect rice leaf diseases, but there are still problems such as large model and poor detection effect. In order to solve the limitations of traditional manual and deep learning-based models, a lightweight model based on improved YOLOv8n is proposed. Firstly, SPPD (Spatial Pyramid Pooling and Dilated) structure with different void ratios composed of GELU activation function was added to the Backbone network to increase the receptive field of the network, and Coordinate attention (CA) was combined to help the model pay attention to the characteristics of rice diseases and improve the detection accuracy. Finally, XSEPConv (Extremely Separated Convolution) is used to reduce the parameters and improve the efficiency of the model. The model was trained and tested on the self-built rice leaf disease image data set, and its mAP @0.5 reached 89.3% and FPS reached 217. The proposed SCX-YOLOv8n model is a lightweight, efficient and high-performance rice leaf disease detection model, and compared with other mainstream models, it also has certain advantages in accuracy and recall rate, which can provide an accurate and accurate rice leaf disease detection and other related fields.