Automated Grading of Angelica sinensis Using Computer Vision and Machine Learning Techniques

Zimei Zhang, Jianwei Xiao, Wenjie Wang, Magdalena Zielinska, Shanyu 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
Wenjie Wang: Chinese Academy of Agricultural Mechanization Sciences, Beijing 100083, China
Magdalena Zielinska: Department of Systems Engineering, University of Warmia and Mazury in Olsztyn, 10-726 Olsztyn, Poland
Shanyu Wang: College of Engineering, China Agricultural University, 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, 2024, vol. 14, issue 3, 1-21

Abstract: Angelica sinensis ( Oliv. ) Diels , a member of the Umbelliferae family, is commonly known as Danggui ( Angelica sinensis , AS). AS has the functions of blood tonic, menstrual pain relief, and laxatives. Accurate classification of AS grades is crucial for efficient market management and consumer health. The commonly used method to classify AS grades depends on the evaluator’s observation and experience. However, this method has issues such as unquantifiable parameters and inconsistent identification results among different evaluators, resulting in a relatively chaotic classification of AS in the market. To address these issues, this study introduced a computer vision-based approach to intelligently grade AS. Images of AS at five grades were acquired, denoised, and segmented, followed by extraction of shape, color, and texture features. Thirteen feature parameters were selected based on difference and correlation analysis, including tail area, whole body area, head diameter, G average, B average, R variances, G variances, B variances, R skewness, G skewness, B skewness, S average, and V average, which exhibited significant differences and correlated with grades. These parameters were then used to train and test both the traditional back propagation neural network (BPNN) and the BPNN model improved with a growing optimizer (GOBPNN). Results showed that the GOBPNN model achieved significantly higher average testing precision, recall, F-score, and accuracy (97.1%, 95.9%, 96.5%, and 95.0%, respectively) compared to the BPNN model. The method combining machine vision technology with GOBPNN enabled efficient, objective, rapid, non-destructive, and cost effective AS grading.

Keywords: Angelica sinensis; machine vision; classification; grade recognition; machine learning (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: 2024
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