Quality grading of jujubes using composite convolutional neural networks in combination with RGB color space segmentation and deep convolutional generative adversarial networks

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 ApplicationsCracks, 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.

JujubeNet: A high-precision lightweight jujube surface defect classification network with an attention mechanism

“…In order to achieve better classification results on the 'Jujube2000' dataset, this paper refers to the algorithms used by different authors in related works (Geng et al, 2018;Sun et al, 2019;Wen et al, 2020;Guo et al, 2020;Ju et al, 2021;Yu et al, 2022). However, the relevant literature did not disclose the code in their papers, and the data sets used were not publicly available for download.…”

“…Finally, with the advantage of the residual module, the model achieved 96.1% classification accuracy on the self-built dataset of jujube. Guo et al. (2020) conducted a study on the impact of the jujube dataset on DL classification algorithms and used generative adversarial networks and rigid transformation to enhance the image data to solve the problem of an uneven sample of defective jujube.…”

A Survey of Defect Detection Applications Based on Generative Adversarial Networks

“…Even if the model is compressed to half the original size, the conditional GAN enhanced classification network can maintain the classification accuracy of 81.16%. Guo et al [123] adopted a data expansion method combining deep convolution generative adversarial network and rigid transformation (RT) to improve the data richness of defective dates and effectively solve the imbalance problem among different types of date data. The defect detection accuracy after data enhancement is up to 99.2%.…”

Detecting Starch-Head and Mildewed Fruit in Dried Hami Jujubes Using Visible/Near-Infrared Spectroscopy Combined with MRSA-SVM and Oversampling

“…Manual visual inspection and machine vision methods are commonly used in the defective fruit detection of dried jujubes [ 3 , 4 ]. Manual visual inspection is highly subjective and inefficient.…”