  This study explored the application of CNN-Transfer Learning for
nondestructive chicken egg fertility detection for precision poultry hatchery
practices. Four models, VGG16, ResNet50, InceptionNet, and MobileNet, were
trained and evaluated on a dataset (200 single egg images) using augmented
images (rotation, flip, scale, translation, and reflection). Although the
training results demonstrated that all models achieved high accuracy,
indicating their ability to accurately learn and classify chicken eggs'
fertility state, when evaluated on the testing set, variations in accuracy and
performance were observed. InceptionNet exhibited the best overall performance,
accurately classifying fertile and non-fertile eggs. It demonstrated excellent
performance in both training and testing sets in all parameters of the
evaluation metrics. In testing set, it achieved an accuracy of 0.98, a
sensitivity of 1 for detecting fertile eggs, and a specificity of 0.96 for
identifying non-fertile eggs. The higher performance is attributed to its
unique architecture efficiently capturing features at different scales leading
to improved accuracy and robustness. Further optimization and fine-tuning of
the models might necessary to address the limitations in accurately detecting
fertile and non-fertile eggs in case of other models. This study highlighted
the potential of CNN-Transfer Learning for nondestructive fertility detection
and emphasizes the need for further research to enhance the models'
capabilities and ensure accurate classification.
