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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
PubMed
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.

Keywords:
automatic detectiondeep learningegg damageegg qualityimage classification

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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.