Grading the state of the egg is an essential task for assessing the quality of an egg. A broken egg can compromise the quality of the remaining eggs in the storage, leading to lower revenue. The poultry industry needs visual imaging methods and advanced techniques to analyze broken eggs from factory outlet. Nevertheless, egg quality also needs to be assessed in the actual production line. To support this scenario, this chapter recommends the Convolutional Layer Optimized AlexNet (CLO-AN) for assessing and classifying broken eggs with high efficiency. The broken egg dataset from Kaggle, consisting of 1500 images, was utilized for the execution. The recommended CLO-AN paradigm starts with data collection and then explores the dataset’s peculiarities. The dataset is fitted using current Convolutional Neural Network (CNN) models to choose the most efficient model with the highest projected accuracy for identifying cracked and broken eggs. According to the execution results, AlexNet’s accuracy rate was 91.32%. In order to build up the appropriate convolutional layers for the model to project high accuracy, the outperforming AlexNet was now fine-tuned to further increase the accuracy. According to the testing results, the recommended CLO-AN with 100 convolutional layers exhibits the highest accuracy of 98.41% when compared to alternative convolutional layer sizes.