Image Classification of Chicken Breed and Gender Using Deep Learning

Identifying chicken breeds and genders accurately is essential for conserving local breeds and maintaining gender ratios on farms. This study developed a system based on the Swin Transformer that efficiently and accurately classifies chicken breeds and genders. The system incorporates a target detection module to eliminate background noise and employs data augmentation techniques to prevent overfitting. A high-quality dataset, consisting of 10,482 locally captured images representing 13 Chinese native chicken breeds, was created for training and testing the model. The system was evaluated using a custom dataset and compared against popular image classification models, such as ResNet and ViT. Results indicate that the target detection module and data augmentation effectively improved the model’s performance. Additionally, strategies such as increasing the input size appropriately and utilizing pre-trained weights significantly enhanced the model’s accuracy. Interpretability analysis reveals that the system successfully identifies specific chicken body parts across different breeds and genders, aligning with human visual attention and highlighting its effectiveness. This work provides a robust solution for poultry management, aiding in tasks such as breed selection, gender ratio control, and genetic conservation. Furthermore, the methodology and dataset presented in this research provide a foundation for future studies in agricultural computer vision applications.