ArticleChinese herbal medicines2023
An online automatic sorting system for defective
Article in Chinese herbal medicines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Bio-optimized complex valued spatiotemporal GNN for herbal species classification.Scientific reports · 2026Article
- Development and application of artificial intelligence in traditional Chinese medicine research and development.Chinese medicine · 2026Review
- Hematopoietic effects of Fufang E'jiao Jiang revealed by microbiome, metabolome and transcriptome analyses: a multi-omics strategy.Frontiers in immunology · 2025Article
- Application and research progress on artificial intelligence in the quality of Traditional Chinese Medicine.Frontiers in pharmacology · 2025Review
- Deep Learning-Based Defects Detection in Keyhole TIG Welding with Enhanced Vision.Materials (Basel, Switzerland) · 2024Article
- Dynamic and Static Regulation of Nicotinamide Adenine Dinucleotide Phosphate: Strategies, Challenges, and Future Directions in Metabolic Engineering.Molecules (Basel, Switzerland) · 2024Review
- AI-Assisted Rational Design and Activity Prediction of Biological Elements for Optimizing Transcription-Factor-Based Biosensors.Molecules (Basel, Switzerland) · 2024Review
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5 authors.
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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 ( 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.
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