Automatic classification of medicinal materials based on three-dimensional point cloud and surface spectral information

Abstract

Chinese herbal medicine plays an irreplaceable role in modern medicine at the moment. The recognition of the species of Chinese medicine is the precondition for drug development. The classification and recognition of Chinese herbal medicine relied on manual work, and the highest rate is about 70%. Most automatic recognition studies were based on two-dimensional imagines of Chinese herbal medicine for classification, while other studies used spectral information. These methods for kinds of Chinese herbal medicine was relatively single, and the band range was narrow, which ignored the three-dimensional structure characteristics. The accuracy, variety and efficiency of their recognition need to be improved. This paper first extract the spatial structure characteristic and ground object spectrum information of Chinese herbal medicine by 3D point cloud data and electromagnetic objects spectrum data. The classical classification model of deep neural network PointNet is used to classify the 3D point cloud data of Chinese herbal medicine, the object spectrum data is used to match and classify the spectral feature, and then verified by correlation coefficient method. This paper conduct a classification experiment by using the self-built data set containing seven kinds of Chinese herbal medicine. The results show that the overall accuracy of the test using PointNet for the automatic classification of seven kinds of Chinese herbal medicine at the same time can reach 86.3%, while the classification of the object spectrum verified by correlation coefficient method can reach 98%. Experiments have proved that the accuracy and the types of Chinese herbal medicine processed are superior to previous methods. This method can be used to identify Chinese herbal medicine and improve the production efficiency.