<![CDATA[Poultry diseases have a significant impact on livestock productivity; therefore, early detection is crucial to prevent infection spread. Deep learning approaches have recently shown promising results in improving disease classification accuracy. Convolutional Neural Network (CNN) models can identify poultry diseases through fecal images using automatic feature extraction. This study proposes poultry disease classification using two CNN architectures, EfficientNetV2-L and MobileNetV2. Each model was trained under three scenarios: baseline, class weights, and Focal Loss, using the Poultry Diseases Detection dataset from Kaggle consisting of four classes of chicken fecal images. The experimental results show that applying Focal Loss improves model performance compared to other scenarios. The EfficientNetV2-L model with Focal Loss achieved the highest accuracy of 99.51%, precision of 99.57%, recall of 99.51%, and F1-score of 99.52%. Meanwhile, MobileNetV2 performed reasonably well with faster training time. These findings indicate that combining Focal Loss with efficient CNN architectures enhances the classification of imbalanced datasets and has the potential to be implemented in real-time poultry disease detection systems]]>
