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Machine Learning Based Model for Detecting and Diagnosing Newcastle Disease in Poultry Farms

AHMED, Abdulhamid, Y. M. Malgwi

Abstract

Agriculture remains central to national economic growth, and with the global population now above 8.1 billion and projected to reach 10 billion by 2057, the demand for food production continues to intensify. In response to this challenge, this study presents a poultry disease diagnosis model based on a Convolutional Neural Network (CNN) using the VGG16 architecture to distinguish between healthy chickens and those infected with Newcastle disease through fecal image analysis. The model automatically extracts and interprets visual features, allowing early disease identification and supporting improved poultry health management. Experimental results demonstrate that the VGG16 model attained an accuracy of 99.00% and an F1-score of 0.99, indicating high precision and reliability in classifying healthy and infected samples. Visualization techniques further revealed clear separability between the diagnostic classes, confirming the robustness of the model. The overall findings highlight the potential of deep learning technologies in precision livestock farming, particularly for timely disease detection, reduction of economic losses, and enhancement of overall animal welfare. The study concludes that CNN-based diagnostic systems are effective and practical tools for automated poultry disease identification. It recommends future work involving larger and more diverse datasets, the application of transfer learning, and the development of hybrid deep learning models to further improve diagnostic performance. Additionally, collaboration among farmers, researchers, and industry stakeholders is encouraged to support the creation of real-time diagnostic tools and promote broader adoption of precision livestock technologies for sustainable poultry production.

Keywords

Convolutional Neural NetworkVGG16Newcastle DiseasePoultry HealthPrecision Livestock Farming.

References

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