No abstract

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.

No abstract

No abstract

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.