EfficientNetB3 Deep Learning Model for Chicken Disease Detection Using Keras and TensorFlow

The early detection and classification of diseases in chickens are relevant for proper poultry management and control to reduce economic losses while enhancing animal welfare. The research herein investigates the deep learning approach for chicken disease classification using the EfficientNetB3 network, which is among the state-of-the-art architectures that have proven efficiency at high accuracy in image classification tasks. Our dataset will contain four classes: "Coccidiosis," "Healthy," "New Castle Disease," and "Salmonella." We trained the model on distinguishing these classes, and we got an overall accuracy of 98%. The evaluation metrics of the classes are mostly very high: "Coccidiosis" and "Salmonella" had a nearly perfect score in both precision and F1-score at 0.99 each, while "Healthy" and "New Castle Disease" both turned in scores over 0.96 in all the metrics. They are aligned at 0.98 for both the macro and weighted average, which supports the idea that the performance is well balanced across classes, independent of class distribution. These results therefore depict the reliability of this model in disease classification, even within imbalanced data. The successful performance of an EfficientNetB3 underlines its potential to provide a computationally efficient yet accurate tool toward disease monitoring at poultry farms. This approach can exemplify the bedrock toward developing an automated health assessment system in agriculture, hence improvement in biosecurity and reduction of dependency on traditional disease detection methods.