Automating egg damage detection for improved quality control in the food industry using deep learning
Yazarlar (6)
Talha Alperen Cengel
Selçuk Üniversitesi, Türkiye
Selçuk Üniversitesi, Türkiye
Bunyamin Gencturk
Selçuk Üniversitesi, Türkiye
Selçuk Üniversitesi, Türkiye
Dr. Öğr. Üyesi Elham Tahsın Yasın YASIN Selçuk Üniversitesi, Türkiye
Muslume Beyza Yildiz
Selçuk Üniversitesi, Türkiye
Selçuk Üniversitesi, Türkiye
Dr. Öğr. Üyesi Ilkay Cinar Selçuk Üniversitesi, Türkiye
Murat Koklu Selçuk Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Journal of Food Science (Q2) | ||
| Dergi ISSN | 0022-1147 Dergi Bilgileri (2025) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 01-2025 |
| Kabul Tarihi | 01-11-2024 | Yayınlanma Tarihi | 01-01-2025 |
| Cilt / Sayı / Sayfa | 90 / 1 / 1–11 | DOI | 10.1111/1750-3841.17553 |
| Makale Linki | https://doi.org/10.1111/1750-3841.17553 | ||
| UAK Araştırma Alanları |
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| Özet |
| 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 … |
| Anahtar Kelimeler |
| automatic detection | deep learning | egg damage | egg quality | image classification |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 8 |
| Scopus | 13 |
| Google Scholar | 19 |