Intelligent Identification of Fritillariae Cirrhosae Bulbus，Crataegi Fructus and Pinelliae Rhizoma Based on Deep Learning Algorithms / 中国实验方剂学杂志

ABSTRACT

Objective:To propose a new method for detecting and evaluating traditional Chinese medicine （TCM） by artificial intelligence and machine vision technology. Method:Taking Fritillariae Cirrhosae Bulbus， Crataegi Fructus and Pinelliae Rhizoma as the research objects， big data of pictures was collected by machine vision and the image database was established. Through the intelligent analysis of the external characteristics of TCM， the deep convolutional neural network model was established to realize the functions of location detection and variety identification by means of deep learning， so as to significantly improve the accuracy of rapid identification of TCM. Result:The classification accuracy of 11 kinds of Chinese herbal pieces （raw， fried， parched and charred products of Crataegi Fructus， Pinelliae Rhizoma， Pinelliae Rhizoma Praeparatum Cum Zingibere et Alumine， Pinelliae Rhizoma Praeparatum， Pinelliae Rhizoma Praeparatum Cum Alumine and three products of Fritillariae Cirrhosae Bulbus） could be more than 99%， and the average recognition accuracy of specific categories could reach more than 97%. Conclusion:The intelligent identification technology of TCM decoction pieces realized by deep learning algorithms has the advantages of simplicity， rapidity， high precision and quantifiable detection， which can provide technical support for the quality detection and evaluation of TCM， and enrich the research ideas of quality evaluation of TCM.