Origin Intelligent Identification of Angelica sinensis Using Machine Vision and Deep Learning

Zimei Zhang, Jianwei Xiao, Shanyu Wang, Min Wu, Wenjie Wang, Ziliang Liu () and Zhian Zheng ()
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Zimei Zhang: College of Engineering, China Agricultural University, Beijing 100083, China
Jianwei Xiao: Beijing Institute of Aerospace Testing Technology, Beijing 100074, China
Shanyu Wang: College of Engineering, China Agricultural University, Beijing 100083, China
Min Wu: College of Engineering, China Agricultural University, Beijing 100083, China
Wenjie Wang: Chinese Academy of Agricultural Mechanization Sciences, Beijing 100083, China
Ziliang Liu: College of Engineering, China Agricultural University, Beijing 100083, China
Zhian Zheng: College of Engineering, China Agricultural University, Beijing 100083, China

Agriculture, 2023, vol. 13, issue 9, 1-14

Abstract: The accurate identification of the origin of Chinese medicinal materials is crucial for the orderly management of the market and clinical drug usage. In this study, a deep learning-based algorithm combined with machine vision was developed to automatically identify the origin of Angelica sinensis ( A. sinensis ) from eight areas including 1859 samples. The effects of different datasets, learning rates, solver algorithms, training epochs and batch sizes on the performance of the deep learning model were evaluated. The optimized hyperparameters of the model were the dataset 4, learning rate of 0.001, solver algorithm of rmsprop, training epochs of 6, and batch sizes of 20, which showed the highest accuracy in the training process. Compared to support vector machine (SVM), K-nearest neighbors (KNN) and decision tree, the deep learning-based algorithm could significantly improve the prediction performance and show better robustness and generalization performance. The deep learning-based model achieved the highest accuracy, precision, recall rate and F1_Score values, which were 99.55%, 99.41%, 99.49% and 99.44%, respectively. These results showed that deep learning combined with machine vision can effectively identify the origin of A. sinensis .

Keywords: Angelica sinensis; origin identification; deep learning; machine vision (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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