Disease Prediction in Poultry Using Artificial Intelligence Tools

Abstract

Poultry farming has become a vital component of human nutrition worldwide, growing alongside industrial production. Despite its scale reaching millions of tons annually infectious diseases remain a major challenge. Each year, millions of chickens reportedly die due to these outbreaks, causing significant economic losses and disrupting flock health. Traditional methods often fall short in early detection and control, making timely intervention difficult. Recent technological advancements have opened new possibilities in poultry health management. Artificial Intelligence (AI), in particular, has emerged as a promising solution in a sector that is otherwise hard to monitor effectively. AI technologies, mainly categorized into Machine Learning (ML) and Deep Learning (DL), enable the analysis and interpretation of large, complex datasets. Using AI-based models, researchers can analyze both collective and individual behaviors, along with biological data, to identify early signs of disease often before visible symptoms occur. Studies have shown that ML algorithms such as Support Vector Machine (SVM) and Random Forest can achieve accuracy rates exceeding 90%. Meanwhile, DL models like Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks have demonstrated predictive accuracy as high as 94–98%. These systems help producers identify not just diseases but also potential pathogens at early stages, significantly minimizing animal losses and enhancing biosecurity. However, for widespread adoption, challenges like data quality, implementation cost, and farmer training need to be addressed. In conclusion, AI-powered disease prediction tools represent an innovative and impactful development in the poultry industry. By improving early detection, reducing economic losses, and enhancing operational efficiency, they are expected to play a crucial role in shaping the future of sustainable, technology-driven livestock production.