PS11-5. Artificial Intelligence-Powered Poultry Disease Prediction Model.

Abstract One of the most devastating incidents in Bangladesh is the outbreak of Avian Influenza. Due to this epidemic, a large number of poultry birds die every year. As a result, this country loses a valuable source of affordable poultry meat, which contributes significantly to meeting the growing demand for protein. Currently, most commercial poultry farms rely on monitoring through CCTV Cameras. While this allows observation from remote locations, it does not support effective analysis of poultry health data, nor does it ensure proper feed and water management, air quality improvement, and temperature regulation. To address these challenges, this research proposes the introduction of AI-powered sensors and real-time statistical data analysis. The system I envision will continuously monitor a multi-parameter environmental profile—temperature, humidity, atmospheric pressure, light intensity, and gas concentration, including ammonia (NH3) levels which correlate strongly with flock health deterioration—and cross-reference these streams against curated baseline health datasets. A predictive machine learning layer would identify statistical deviations and biological anomalies, generating a probabilistic outbreak forecast which would dispatch alerts to poultry researchers to enable immediate quarantine of the infected birds to ensure biosecurity. I aim to build a collaborative national poultry health intelligence network in Bangladesh, where collected data across thousands of farms continuously improves the underlying predictive model, making the system more accurate and adaptive with every deployment cycle. Therefore, it is essential to overcome key challenges in poultry farming, such as preventing avian influenza and implementing environmentally sustainable management systems. In conclusion, the application of artificial intelligence in poultry farming can reduce production costs and significantly improve profit margins.