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Automating egg damage detection for improved quality control in the food industry using deep learning.
Talha Alperen Cengel1, Bunyamin Gencturk2, Elham Tahsin Yasin2
1Department of Computer Engineering, Technology Faculty, Selcuk University, Konya, Turkey.
Journal of Food Science
|January 22, 2025
Summary
Deep learning models accurately detect damaged eggs. GoogLeNet achieved the highest accuracy (98.73%) in identifying cracks and surface defects, improving quality control in the egg industry.
Area of Science:
- Agricultural Science
- Computer Science
- Food Science
Background:
- Egg quality control is crucial for the food industry.
- Traditional methods for detecting egg damage are often inefficient.
- Automated detection of physical damage is needed to ensure egg safety and quality.
Purpose of the Study:
- To develop an automated system for detecting and classifying damaged chicken eggs.
- To enhance egg quality control using deep learning algorithms.
- To compare the performance of different deep learning models for egg damage detection.
Main Methods:
- Utilized a dataset of 794 chicken egg images, categorized as damaged or intact.
- Employed four deep learning models: GoogLeNet, VGG-19, MobileNet-v2, and ResNet-50.
- Trained and evaluated the models for crack and surface damage identification.
Main Results:
- GoogLeNet achieved the highest classification accuracy at 98.73%.
- VGG-19, MobileNet-v2, and ResNet-50 showed accuracies of 97.45%, 97.47%, and 96.84%, respectively.
- All tested deep learning models demonstrated high performance in detecting egg damage.
Conclusions:
- Deep learning, particularly the GoogLeNet model, offers a highly accurate and efficient method for automatic egg damage detection.
- This technology can significantly improve quality control, reduce product loss, and enhance food safety in the egg industry.
- Automated detection systems provide a faster and more reliable alternative to traditional methods for identifying damaged eggs.
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