Deep learning model for rice disease detection

Common diseases of rice include blight, brown, dead heart, downy mildew, etc. These diseases can cause damage to rice leaves. If not recognized in time, it will lead to a decrease in rice yield and even the death of part or the entire crop in the land. Using deep learning technology to monitor rice crop diseases can provide early warning of rice crops, and then promptly specify prevention strategies when diseases occur, helping farmers and agricultural management departments to make scientific decisions to improve the efficiency and sustainability of rice production. Based on the core concept of ecological and environmental protection, we proposed an innovation to the You Only Look Once (YOLOv8) technical framework, focusing on optimizing the accurate identification technology of rice diseases and pests. This improvement aims to promote the sustainable development of agricultural production in a more environmentally friendly and efficient way. This article uses the improved You Only Look Once (YOLOv8) model to accurately detect the disease types in the collected data set. This article uses multi-scale convolution kernels and Attention to improve the C2f module of the yolov8 model. The improved model is in the data set An accuracy rate of 96.4% was achieved. This research helps to gain a deeper understanding of the applicability of computer vision in crop disease detection and further explore its application potential in agricultural production.