An automatic sorting system for unwashed eggs using deep learning. (October 2020)

• Record Type: Journal Article
• Authors: Nasiri, Amin; Omid, Mahmoud; Taheri-Garavand, Amin
• Is Part Of: Journal of food engineering. Volume 283(2020)
• Publication Date: 2020-10
• Subjects: Egg -- Intact -- Defect detection -- Deep learning -- VGG16; Food industry and trade -- Periodicals; Food -- Analysis -- Periodicals; Aliments -- Industrie et commerce -- Périodiques; Aliments -- Analyse -- Périodiques; Aliments -- Recherche -- Périodiques; 664.005
• DOI: 10.1016/j.jfoodeng.2020.110036
• Languages: English
• ISSNs: 0260-8774

Abstract

Abstract: Egg quality and safety are significant concerns of consumers and modern food industries. This study proposes a novel and precise assessment of egg sorting using a deep convolutional neural network (CNN), which is a state-of-the-art computer vision method to perform classification tasks. To classify unwashed egg images, VGG16 architecture was modified by a global average pooling layer, dense layers, a batch normalization layer, and a dropout layer. The modified model was trained based on intact, bloody, and broken (breakage, crack, or hole on the eggshell) eggs, which were combined with being dirty. Performance evaluation of the CNN model through 5-fold cross-validation showed that it outperforms traditional machine vision-based models. The accuracy, precision, sensitivity, specificity, and area under the curve were 96.55, 95.59, 94.92, 97.39, and 96.16%, respectively. The CNN model achieved an average overall accuracy of 94.84 by 5-fold cross-validation. Highlights: Convolution neural network (CNN) based-model was created for egg sorting. VGG-16 structure was employed to construct the deep model. Images captured by a candling system were directly used as input to the deep model. Average overall accuracy of 94.84% was achieved through 5-fold cross-validation.