An online automatic sorting system for defective Ginseng Radix et Rhizoma Rubra using deep learning / 中草药·英文版
Chinese Herbal Medicines
; (4): 447-456, 2023.
Article
in En
| WPRIM
| ID: wpr-982523
Responsible library:
WPRO
ABSTRACT
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 2 000 red ginsengs were used to validate the performance of the Faster R-CNN 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.
Full text:
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Index:
WPRIM
Language:
En
Journal:
Chinese Herbal Medicines
Year:
2023
Type:
Article