Research and Design of a Crop Leaf Disease Diagnosis System Based on the YOLOv8 Model

In the context of global food security and agricultural output, the early detection and accurate diagnosis of crop leaf diseases are particularly crucial. However, traditional manual diagnostic methods, due to their inefficiency and limited precision, struggle to meet the demands of modern agriculture. This study introduces a crop leaf disease diagnostic system based on the YOLOv8 model, utilizing computer vision and deep learning technologies to automatically identify and classify leaf diseases. Trained on a dataset comprising 21,406 images, the system demonstrates high-efficiency recognition capabilities, particularly excelling in handling diverse disease types. The findings not only enhance the speed and accuracy of disease diagnosis but also provide a cost-effective and efficient technological solution for agricultural disease management. The primary value of this research lies in its application of cutting-edge computer vision technology to real agricultural problems, offering new perspectives and methodologies for future agricultural disease diagnosis and management.