基于YOLOv8的水稻病虫害识别系统

Rice is one of the most important food crops in the world, however, the infestation of pests and diseases seriously affects the yield and quality of rice. Traditional pest and disease identification methods are subject to many limitations, such as low accuracy, low efficiency and other shortcomings, therefore, this paper addresses the difficulties and challenges in rice pest and disease control, and explores the rice pest and disease identification technology from the level of deep learning technology, and experimentally introduces the YOLOv8 target detection technology, and the result greatly improves the accuracy and stability of the identification. Firstly, the technical characteristics and advantages of YOLOv8 target detection model are introduced; secondly, the development environment and design ideas of the system are introduced; finally, the specific operation content of the rice pest recognition system based on YOLOv8 deep learning is introduced in detail, which is not only capable of recognizing 14 types of rice pests, but also can carry out real-time monitoring through the data of images and videos, which provides a new way for the monitoring and control of rice pests.