<![CDATA[Chicken eggs are one of the staple food ingredients in Indonesia, playing a vital role in fulfilling the nutritional needs of the community. Therefore, an efficient, accurate, and reliable method for assessing egg quality is essential, especially to support the distribution process in the food industry. This study aims to develop a digital image-based classification system for assessing the quality of chicken eggs using deep learning methods with the EfficientNet-B7 architecture. EfficientNet-B7 was selected for its proven high accuracy in image classification tasks through the application of compound scaling, which simultaneously optimizes depth, width, and resolution. The dataset used in this study combines images collected from public sources and primary documentation, representing various conditions commonly found in chicken eggs. The preprocessing stage involved trimming techniques to focus on the egg object, followed by data augmentation using ImageDataGenerator, including rotation, shifting, zooming, and flipping to enhance dataset diversity. Model training was carried out with the early stopping technique to prevent overfitting. The experimental results showed that the model achieved an accuracy of 98.08% in classifying egg quality based on shell condition and other visual indicators. These findings demonstrate that the implementation of the EfficientNet-B7 model has great potential to support the automation of chicken egg quality assessment processes in a faster and more consistent manner. Thus, this research is expected to contribute to improving the efficiency of the food industry, particularly in the distribution process of chicken eggs in Indonesia.]]>
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