Egg fertility assessment system using deep learning technology

In the poultry industry, the accurate distinction between fertilized and unfertilized eggs is one of the key factors for determining hatching efficiency. Therefore, accurately identifying and removing unfertilized and hatching failure eggs at an early stage is important for enhancing economic and operational efficiency. When looking at only fertilized eggs, all Convolutional neural networks(CNNs) showed accuracies of over 97%. In the case of unfertilized and hatching failure eggs, all networks showed lower accuracies compared to fertilized eggs, as in Case A. In Case B, the overall performance results of the CNNs showed that the more network training parameters there are, the higher the test performance. Case C used only fertilized and unfertilized eggs from the total data (2,536), and randomly selected them in an 8:2 ratio for training and testing data. The overall classification performance of the CNNs was excellent, with over 97.02%. Particularly, for fertilized eggs, all networks showed outstanding performance evaluation results of more than 99.34%. Case D determined the test images by randomly pre-selecting 20% of the eggs, similar to Case B, except hatching failure eggs were excluded from training. The distinction between fertilized and unfertilized eggs from images on the 5th to 6th day after incubation was found to be a highly effective and efficient method for all 12 CNN algorithms. Thus, the selection of CNNs should be based on available computational resources and the desired speed and accuracy. However, network training can be difficult due to the challenge of acquiring data for hatching failure eggs. The exact point of hatching failure is unclear, which makes the distinction between fertilized and unfertilized eggs ambiguous.