Cengel Talha Alperen, Gencturk Bunyamin, Yasin Elham Tahsin, Yildiz Muslume Beyza, Cinar Ilkay, Koklu Murat
Department of Computer Engineering, Technology Faculty, Selcuk University, Konya, Turkey.
Graduate School of Natural and Applied Sciences, Selcuk University, Konya, Turkey.
J Food Sci. 2025 Jan;90(1):e17553. doi: 10.1111/1750-3841.17553.
The detection and classification of damage to eggs within the egg industry are of paramount importance for the production of healthy eggs. This study focuses on the automatic identification of cracks and surface damage in chicken eggs using deep learning algorithms. The goal is to enhance egg quality control in the food industry by accurately identifying eggs with physical damage, such as cracks, fractures, or other surface defects, which could compromise their quality. A total of 794 egg images were used in the study, comprising two different classes: damaged and not damaged (intact) eggs. Four different deep learning models based on convolutional neural networks were employed: GoogLeNet, Visual Geometry Group (VGG)-19, MobileNet-v2, and residual network (ResNet)-50. GoogLeNet achieved a classification accuracy of 98.73%, VGG-19 achieved 97.45%, MobileNet-v2 achieved 97.47%, and ResNet-50 achieved 96.84%. According to the results, the GoogLeNet model performed the damage detection with the highest accuracy rate (98.73%). This study encompasses artificial intelligence and deep learning techniques for the automatic detection of egg damage. The early detection of egg damage and accurate interventions highlights the significant importance of using these technologies in the food industry. This approach provides producers with the ability to detect damaged eggs more quickly and accurately, thereby minimizing product losses through timely intervention. Additionally, the use of these technologies offers a more efficient means of classifying and identifying damaged eggs compared to traditional methods.
在蛋品行业中，检测和分类鸡蛋的损伤对于生产健康鸡蛋至关重要。本研究聚焦于使用深度学习算法自动识别鸡蛋中的裂缝和表面损伤。目标是通过准确识别存在物理损伤（如裂缝、破裂或其他表面缺陷）的鸡蛋来加强食品行业的鸡蛋质量控制，这些损伤可能会影响鸡蛋质量。该研究共使用了794张鸡蛋图像，包括两个不同类别：受损鸡蛋和未受损（完好）鸡蛋。采用了四种基于卷积神经网络的不同深度学习模型：GoogLeNet、视觉几何组（VGG）-19、MobileNet-v2和残差网络（ResNet）-50。GoogLeNet的分类准确率达到98.73%，VGG-19为97.45%，MobileNet-v2为97.47%，ResNet-50为96.84%。根据结果，GoogLeNet模型在损伤检测方面准确率最高（98.73%）。本研究涵盖了用于自动检测鸡蛋损伤的人工智能和深度学习技术。鸡蛋损伤的早期检测和准确干预凸显了在食品行业使用这些技术的重大意义。这种方法使生产者能够更快、更准确地检测出受损鸡蛋，从而通过及时干预将产品损失降至最低。此外，与传统方法相比，使用这些技术为分类和识别受损鸡蛋提供了一种更有效的手段。
