Abstract: Chinese medicine plays an important role in today’s medical field, and the demand for Chinese herbal medicine is also increasing year by year. However, Chinese herbal medicine disease is one of the important reasons for the reduction of its production. At present, the method of artificial identification of Chinese herbal diseases is still used, which is subjective and has low accuracy. In order to identify Chinese herbal diseases more scientifically and accurately, this paper develops a Chinese herbal disease identification management system. The Chinese herbal medicine disease identification and management system adopts the front-back separation mode, the front-end uses Vue and Element-Ui framework, the back-end uses Springboot and Mybatis technical framework, the database uses MySQL, and the deep learning prediction model is integrated to scientifically identify the types of Chinese herbal medicine diseases. In this paper, the deep learning YOLOv5 model is improved on the basis of the existing recognition system, and the recognition accuracy is increased from the original 95.3% to 98.2%. The system has added the function of Chinese herbal medicine disease identification and monitoring of Chinese herbal medicine growth environment data display, which has the effect of monitoring the growth environment of Chinese herbal medicine and high-precision identification of disease types. The system has a strong practicality and service, can reduce the labor force of growers, increase economic benefits, and promote the development of smart agriculture.

Key words: Vue framework, Springboot framework, MySQL database, Chinese herbal diseases, deep learning