Advanced Chicken Disease Detection with Keras and TensorFlow Deep Learning Models

This study investigates the effectiveness of Convolutional Neural Networks in spotting diseases in images of chickens. The model, examining a set of chicken pictures, achieved an overall 98.27% accuracy. The CNN model perfected using TensorFlow Achieved Keras had excellent performance measures against types of diseases in terms of Precision, Recall, and F1-Score. In other words, the model is a resource that can assist a poultry farmer in determining diseases like coccidiosis, Newcastle Disease, and Salmonella. Early detection and management of chicken diseases are vital to maintaining healthy poultry. These old methods are cumbersome, time-inefficient, and require unique and valuable expertise. The research problem is conceived in the development of a CNN model that can automatically detect diseases with the best elevation in the accuracy and effectiveness of the disease identification process amid poultry. Diagnosing diseases using these methods in chickens can be highly difficult and precise, thereby challenging the farmers in this industry.