Automated Detection of Poultry Diseases Using Deep Learning and Computer Vision

Early, accurate detection of chicken diseases is necessary for promoting sustainable farming techniques, enhancing poultry health, and lowering of economic losses. This paper presents a deep learning-based method using transfer learning with the EfficientNetB5 architecture to classify chicken health issues into four categories: healthy, Disease A, Disease B, and Disease C using photo data. Using a powerful training pipeline and thorough evaluation, the model not only achieved remarkable overall accuracy of 97% by surpassing 93% across all classes but also high precision, recall, and F1-scores. Closely inspection hhhkof the confusion matrix and classification metrics confirmed that the method could effectively separate visually similar but complex disease kinds. Analyzing the deployment alternatives of this model also addressed concerns such hardware constraints, scalability, and the need of real-time inference to enable useful use in chicken farms. This work presents a noninvasive, efficient diagnostic method to help early intervention and disease control in chicken production as well as showing how well advanced convolutional neural networks address significant agricultural challenges. The findings of this study suggest to a transformational route towards adding AI-driven solutions in agriculture, therefore guaranteeing better animal welfare, more farm productivity, and a sustainable future for chicken health management.