基于深度学习及阿里云百炼平台的水稻病虫害识别

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, so 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 traditional deep learning technology - firstly, it introduces the technological features and advantages of the YOLOv8 target detection model; secondly, it describes the development environment and the design of the system; finally, it details the YOLOv8 deep learning-based rice pest and disease identification technology; and lastly, it describes in detail the YOLOv8 target detection model. ideas; finally, the specific operation content of the rice pest recognition system based on YOLOv8 deep learning is described in detail, which can not only recognize 14 types of rice pests, but also conduct real-time monitoring through the data of images and videos, providing a new model and solution for the monitoring and control of rice pests. In addition, this paper also carries out the development of an intelligent body application of a rice pest identification intelligent robot on the enterprise large model production platform - AliCloud Hundred Refinement Platform, and then explores a rice pest identification method that is different from the traditional and concise and convenient.