<![CDATA[The timely identification of poultry diseases is essential for maintaining high livestock yields and curbing the transmission of infections, which directly supports global food security. The integration of multi-layered neural architectures has advanced this diagnostic process, offering an innovative approach to automated health monitoring through enhanced classification accuracy. By employing Convolutional Neural Networks (CNNs), the extraction of discriminative features from fecal images is automated, providing a scalable and robust approach to sustainable agriculture. This study proposes poultry disease classification using two CNN architectures, EfficientNetV2-L and MobileNetV2, trained under three scenarios: baseline, class weights, and Focal Loss. Using a dataset of 6,812 chicken fecal images, the experimental results demonstrate that applying Focal Loss significantly improves performance across all metrics. The EfficientNetV2-L model with Focal Loss achieved superior results, with 99.51% accuracy, 99.57% precision, 99.51% recall, and 99.52% F1-score. Meanwhile, MobileNetV2 performed reasonably well with a faster training time, making it suitable for resource-constrained environments. These findings indicate that combining Focal Loss with efficient CNN architectures enhances the classification of imbalanced datasets and provides a promising technological solution for real-time poultry disease detection systems.]]>
