Quality grading of jujubes using composite convolutional neural networks in combination with RGB color space segmentation and deep convolutional generative adversarial networks. (6th December 2020)

• Record Type: Journal Article
• Authors: Guo, Zhongyuan; Zheng, Hong; Xu, Xiaohang; Ju, Jianping; Zheng, Zhaohui; You, Changhui; Gu, Yu
• Other Names: Kuila Arindam guestEditor.; Mukhopadhyay Mainak guestEditor.
• Is Part Of: Journal of food process engineering. Volume 44:Number 2(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-12-06
• Subjects: Food industry and trade -- Periodicals; Food -- Analysis -- Periodicals; 664.005
• DOI: 10.1111/jfpe.13620
• Languages: English
• ISSNs: 0145-8876

Abstract

Abstract: As an important link in the processing of jujube products, the qualities classification of jujubes have an important impact on improving the value of commodities. In this study, jujube target was extracted based on the RGB color space characteristics and then put into a black background through a mask. The data augmentation method combined deep convolutional generative adversarial networks and rigid transformation (RT) was used to improve the data richness of defective jujubes, effectively solve the imbalance problem between different types of jujube data. A composite convolutional neural network (CNN) method based on residual networks was designed to effectively solve the problem of misjudgment between jujubes with subtle defects and healthy jujubes. The overall results illustrated that the defect detection accuracy of the proposed scheme was 99.2%, which was superior to the widely used support vector machine and CNN methods. This work could be applied to the actual processing site and greatly improved the quality classification effect of jujubes. Practical Applications: Cracks, peeling, wrinkles, and other defects have seriously affected the quality and value of jujubes, and the quality classification of jujubes is imperative. This paper proposes a set of deep learning schemes from three aspects of improving data quality, enhancing data richness, and designing more accurate and effective classification models. Experimental results show that this scheme can significantly improve the accuracy of jujube quality grading. Abstract :