Deep-learning models were used to evaluate egg quality based on the surface condition of the eggs. Three different deep learning image classification models (EfficientNet, Swin-transformer, YOLO v5) were used for the training, and EfficientNet showed the highest accuracy with 99.7% for assessing egg quality based on the reference with 8 conditions, such as chicken manure, yolk, spot, sandy, calcium, swelling, deformed, and normal. The result demonstrates that deep learning image classification technique can be used for automated evaluation of egg quality with good accuracy.
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