An online automatic sorting system for defective Ginseng Radix et Rhizoma Rubra using deep learning.

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.

Deep Learning Network of Amomum villosum Quality Classification and Origin Identification Based on X-ray Technology.

A machine vision system based on a convolutional neural network (CNN) was proposed to sort Amomum villosum using X-ray non-destructive testing technology in this study. The Amomum villosum fruit network (AFNet) algorithm was developed to identify the internal structure for quality classification and origin identification in this manuscript. This network model is composed of experimental features of Amomum villosum. In this study, we adopted a binary classification method twice consecutive to identify the origin and quality of Amomum villosum. The results show that the accuracy, precision, and specificity of the AFNet for quality classification were 96.33%, 96.27%, and 100.0%, respectively, achieving higher accuracy than traditional CNN under the condition of faster operation speed. In addition, the model can also achieve an accuracy of 90.60% for the identification of places of origin. The accuracy of multi-category classification performed later with the consistent network structure is lower than that of the cascaded CNNs solution. With this intelligent feature recognition model, the internal structure information of Amomum villosum can be determined based on X-ray technology. Its application will play a positive role to improve industrial production efficiency.

[Methodology for adaptive decision--making research on manufacturing process of traditional Chinese medicine based on deep reinforcement learning].

The manufacturing process of traditional Chinese medicine is subject to material fluctuation and other uncertain factors which usually cause non-optimal state and inconsistent product quality. Therefore, it is necessary to design and collect the quality-rela-ted physical parameters, process parameters, and equipment parameters in the whole manufacturing process of traditional Chinese medicine for digitization and modeling of the process. In this paper, a method for non-optimal state identification and self-recovering regulation was developed for active quality control in the manufacturing process of traditional Chinese medicine. Moreover, taking vacuum belt drying process as an example, a DQN algorithm-based intelligent decision model was established and verified and the implementation process was also discussed and studied. Thus, the process parameters-based self-optimization strategy discovery and path planning of optimal process control were rea-lized in this study. The results showed that the deep reinforcement learning-based artificial intelligence technology was helpful to improve the product quality consistency, reduce production cost, and increase benefit.

X-ray-based machine vision technique for detection of internal defects of sterculia seeds.

An online machine learning system based on X-ray nondestructive quality evaluation technique was developed to detect internal defects of boat-fruited sterculia seed. The X-ray images of boat-fruited sterculia seed were first acquired by the detection system. Then, a boat-fruited sterculia seed net (BSSNet) was trained to identify the defective boat-fruited sterculia seeds based on the X-ray images. The BSSNet was evaluated with the accuracy, precision, specificity, and sensitivity as 94.64%, 93.51%, 92.37%, and 96.64%, respectively. Further, three classical CNN models including VGG16, Resnet, and Inception were trained on the same dataset with accuracy of 95.71%, 94.29%, and 94.64%, respectively. Compared with the classical CNN models, the BSSNet achieved similar or higher accuracy in X-ray images classification. Finally, an independent dataset containing 200 X-ray images was used to validate the performance of the BSSNet and obtained an accuracy of 96.5%. The results presented above demonstrated that this classification method has a great potential for industrial applications. PRACTICAL APPLICATION: An X-ray online detection system integrated with a machine vision model was used to evaluate the quality of boat-fruited sterculia seed. A low-power x-ray detection system can detect internal defects of the object and ensure safety in the production process. The developed machine vision can sort the boat-fruited sterculia seed with an accuracy of 96.5%. The proposed nondestructive detection system showed a good potential to be used in industrial applications.

[Construction of knowledge base of Chinese medicine manufacturing].

Chinese medicine pharmaceutical industry is in the process of digital and intelligent transformation. Intelligent methods are required for efficient analysis and mining of the valuable information in the history data including literature data, pharmaceutical big data, and expert knowledge. Therefore, it is urgent to establish a knowledge-driven intelligent system of pharmaceutical technologies of Chinese medicine for efficient supplying of high-quality Chinese medicinal products. The present study proposed the construction method of the knowledge base of Chinese medicine manufacturing, which was preliminarily established from literature mining, case-based reasoning, and real-time prediction based on vacuum belt drying process optimization. Integrating the technologies(such as deep learning, case-based reasoning, and simulation modeling), pharmaceutical mechanisms, and big data, the knowledge base of Chinese medicine manufacturing can realize knowledge automation and scientific decision-making. It provides an example for upgrading from experience-based manufacturing to intelligent Chinese medicine manufacturing.