Eggshell crack detection using deep convolutional neural networks

• Published in: Journal of food engineering Vol. 315; p. 110798
• Main Authors: Botta, Bhavya, Gattam, Sai Swaroop Reddy, Datta, Ashis Kumar
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-02-2022
• Subjects: Convolutional neural networks; Eggshell crack detection; Image patches; Support vector machines; Support vector machines; Image patches; Convolutional neural networks; Eggshell crack detection
• ISSN: 0260-8774; 1873-5770
• DOI: 10.1016/j.jfoodeng.2021.110798

The accurate detection of cracks on eggshells is quintessential to provide consumers with safe and quality eggs. This study proposes a novel approach to automatically detect these eggshell cracks using a convolutional neural network (CNN). The CNN model was trained on image patches extracted from the egg images with different batch sizes. Inferences upon testing showed that the model trained on a batch size of 64 gave the best results. The model also outperformed the support vector machine (SVM) classifiers trained on the histogram of oriented gradients (HOG) and local binary pattern (LBP) features with precision, recall, accuracy, false positive rate (FPR) and area under the curve (AUC) of 98.28%, 92.41%, 96.92%, 0.81% and 99.53%, respectively in classifying image patches. In addition, the proposed CNN model surpassed the SVM models in classifying images of eggs with an accuracy of 95.38%.
•Convolutional neural network (CNN) based model was developed for egg crack detection.•Image patches with high probability to contain cracks were extracted from egg images.•CNN model outperformed traditional machine learning classifiers in crack detection.•Accuracy of 95.38% was achieved in classifying the images of eggs.•The data consisted of 36.9% of egg images with generally disregarded micro-cracks.