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ABSTRACT
Newcastle Disease (ND) poses a significant threat to the poultry industry, causing substantial economic losses and endangering food security worldwide. Traditional detection methods, while effective, are often time-consuming, labor-intensive, and prone to delays. This paper presents an innovative approach leveraging artificial intelligence (AI) to enhance the accuracy and speed of Newcastle Disease detection. By utilizing machine learning algorithms, specifically convolutional neural networks (CNNs), and integrating data from diverse sources such as clinical signs, serological tests, and imaging, our AI model achieves high sensitivity and specificity in identifying ND. The proposed system not only streamlines the diagnostic process but also provides real-time monitoring capabilities, enabling prompt and effective disease management. Experimental results demonstrate that our AI-driven approach outperforms conventional diagnostic methods, offering a promising tool for veterinarians and poultry farmers to mitigate the impact of Newcastle Disease. This advancement underscores the potential of AI in transforming animal health diagnostics, ensuring sustainable and healthy poultry production.
