Data Augmentation Techniques on the Accuracy of Fertile and Infertile Egg Classification Using Convolutional Neural Networks

Bani Nurhakim; Dodi Solihudin; Dina Amalia; Irly Arelia

doi:10.52436/1.jutif.2025.6.5.5234

Authors

Bani Nurhakim Informatics Management, STMIK IKMI Cirebon, Indonesia
Dodi Solihudin Informatics Engineering, STMIK IKMI Cirebon, Indonesia
Dina Amalia Informatics Management, STMIK IKMI Cirebon, Indonesia
Irly Arelia Computerized Accounting, STMIK IKMI Cirebon, Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2025.6.5.5234

Keywords:

Candling Images, Convolutional Neural Network, Data Augmentation, Egg Classification, EfficientNetB4

Abstract

The classification of fertile and infertile chicken eggs is crucial in the poultry industry to ensure optimal incubation efficiency and hatchability. However, the visual similarity between both egg types under candling conditions poses a significant challenge for manual inspection. This study aims to develop a convolutional neural network (CNN) model using the EfficientNetB4 architecture to automatically classify egg fertility based on image data. The dataset comprises candling images of chicken eggs, which underwent preprocessing steps such as resizing, normalization, and histogram stretching to enhance contrast. To improve model generalization, aggressive data augmentation techniques were applied, including rotation, flipping, zooming, and brightness adjustment. The model was trained in two phases—feature extraction and fine-tuning—using transfer learning and class balancing strategies. Evaluation results demonstrated high performance with an F1-score of 0.95 and balanced classification across both classes. The model's interpretability was further enhanced using Grad-CAM visualization, showing relevant activation regions. These findings indicate that the proposed method is effective in automating egg fertility classification and has potential for broader application in agricultural image diagnostics.

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