The application of machine learning and artificial intelligence technology in the production quality management of traditional Chinese medicine decoction pieces

The new generation of information technology represented by Big data has developed rapidly. The gradual integration of related technologies with modern manufacturing, service industry, and industrial production has achieved technological innovation and improved production service efficiency. Applying intelligent Big data to the production of traditional Chinese medicine has become an important technical means to improve the market competitiveness of the traditional Chinese medicine industry. The healthy China strategy is constantly advancing, the application of traditional Chinese medicine decoction pieces is gradually widespread. However, there are many factors affecting the quality of decoction pieces of traditional Chinese medicine. The decoction pieces quality of traditional Chinese medicine mainly depends on the experience of relevant personnel, and the identification effect is limited. How to ensure the quality of decoction pieces directly affects the establishment of the value system of traditional Chinese medicine. Under the background of developing Big-data and artificial intelligence, aiming at the current problem of quality variation in the production process of Chinese herbal medicines, the color, spots, texture, and geometric features of traditional Chinese medicine slices images are extracted. Then, based on machine learning methods, a corresponding quality evaluation model for traditional Chinese medicine slices is constructed. The quality of the acquired feature images is classified adopting a support vector machine. This shows that the accuracy of the quality identification method based on computer vision technique and support vector machine for traditional Chinese medicine decoction pieces proposed by the study reaches 95.59%, which can effectively distinguish the quality of Chinese herbal pieces and provide support for developing Chinese medicine.