Optimization Study on Field Deployment of Lightweight YOLO Model for Sugarcane Pest and Disease Recognition

This paper addresses the practical issues encountered during the field deployment of sugarcane pest and disease recognition models. It elaborates on the basic principles of the YOLO model and the characteristics of the lightweight YOLO model. Furthermore, it analyzes the drawbacks of existing deployment methods and proposes targeted field deployment optimization strategies, which encompass two major aspects: optimal allocation of hardware resources and optimization adjustments of model algorithms. The research findings indicate that the optimized lightweight YOLO model demonstrates high recognition accuracy and real-time performance in sugarcane pest and disease recognition.