Objective: To establish a deep-learning architecture based on faster region-based convolutional neural networks (Faster R-CNN) algorithm for detection and sorting of red ginseng (Ginseng Radix et Rhizoma Rubra) with internal defects automatically on an online X-ray machine vision system. Methods: A Faster R-CNN based classifier was trained with around 20 000 samples with mean average precision value (mAP) of 0.95. A traditional image processing method based on feedforward neural network (FNN) obtained a bad performance with the accuracy, recall and specificity of 69.0%, 68.0%, and 70.0%, respectively. Therefore, the Faster R-CNN model was saved to evaluate the model performance on the defective red ginseng online sorting system. Results: An independent set of 2000 red ginsengs were used to validate the performance of the Faster RCNN based online sorting system in three parallel tests, achieving accuracy of 95.8%, 95.2% and 96.2%, respectively. Conclusion: The overall results indicated that the proposed Faster R-CNN based classification model has great potential for non-destructive detection of red ginseng with internal defects.
This research was funded by National Natural Science Foundation of China (Grant No. 82074276), Projects of International Cooperation of Traditional Chinese Medicine (Grant No. 0610-2040NF020928), National S&T Major Project of China (Grant No. 2018ZX09201011), and Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine. (No. ZYYCXTD-D-202002).
Qilong Xuea, b, Peiqi Miaoc, Kunhong Miaoa, b, Yang Yua, b,*, Zheng Lia, b,*. An online automatic sorting system for defective Ginseng Radix et Rhizoma Rubra using deep learning[J]. Chinese Herbal Medicines (CHM),2023,15(3):447-456
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Online Published: July 21,2023
Published: July 18,2023
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