Abstract
Feather color plays a crucial role in distinguishing various poultry breeds. However, the reliability of conventional manual classification methods is debated due to the intricate nature of feather color traits. To address this issue, we applied Convolutional Neural Networks (CNN) to extract the feather texture features of 300 images each of Golden Meihua (GM) and Silver Meihua (SM) feathers. The nonlinear features were then learned through an MLP layer and activation functions to complete the classification of GM and SM. Finally, we successfully developed a method automating the identification of feather texture features, achieving a recognition model accuracy of 93.71 % after 5-fold cross-validation. This research improves the precision and effectiveness of feather color selection, offering valuable insights into the systematic classification of feather colors in diverse poultry species.