Deep Learning Network of Amomum villosum Quality Classification and Origin Identification Based on X-ray Technology

Author:

Wu Zhouyou123,Xue Qilong123,Miao Peiqi14,Li Chenfei123,Liu Xinlong123,Cheng Yukang123,Miao Kunhong123,Yu Yang123,Li Zheng1235ORCID

Affiliation:

1. Xin-Huangpu Joint Innovation Institute of Chinese Medicine, Guangzhou 510715, China

2. State Key Laboratory of Component Traditional Chinese Medicine, Tianjin 301617, China

3. College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China

4. Tianjin Modern Innovative TCM Technology Co., Ltd., Tianjin 300380, China

5. Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China

Abstract

A machine vision system based on a convolutional neural network (CNN) was proposed to sort Amomum villosum using X-ray non-destructive testing technology in this study. The Amomum villosum fruit network (AFNet) algorithm was developed to identify the internal structure for quality classification and origin identification in this manuscript. This network model is composed of experimental features of Amomum villosum. In this study, we adopted a binary classification method twice consecutive to identify the origin and quality of Amomum villosum. The results show that the accuracy, precision, and specificity of the AFNet for quality classification were 96.33%, 96.27%, and 100.0%, respectively, achieving higher accuracy than traditional CNN under the condition of faster operation speed. In addition, the model can also achieve an accuracy of 90.60% for the identification of places of origin. The accuracy of multi-category classification performed later with the consistent network structure is lower than that of the cascaded CNNs solution. With this intelligent feature recognition model, the internal structure information of Amomum villosum can be determined based on X-ray technology. Its application will play a positive role to improve industrial production efficiency.

Funder

Joint Innovation Foundation of JIICM

Tianjin University Student Innovation and Entrepreneurship Training Program

Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine

National Natural Science Foundation of China

Publisher

MDPI AG

Subject

Plant Science,Health Professions (miscellaneous),Health (social science),Microbiology,Food Science

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