AI-BASED EARLY POULTRY DISEASE RISK PREDICTION SYSTEM USING REAL-TIME TREND ANALYSIS

Abstract: Poultry farming plays a vital role in food production and the agricultural economy, but disease outbreaks in poultry farms can lead to significant economic losses, reduced productivity, and increased mortality rates. Early detection and prediction of diseases are essential to ensure the health and safety of poultry birds. This paper presents an AI-Based Early Poultry Disease Risk Prediction System using Real-Time Trend Analysis that aims to monitor environmental and health-related parameters continuously and predict possible disease risks at an early stage. The proposed system utilizes Artificial Intelligence and Ma chine Learning techniques to analyze real-time data collected from sensors and farm records, including temperature, humidity, ammonia levels, feed intake, water consumption, and bird activity. By applying predictive algorithms and trend analysis, the system identifies abnormal patterns and provides early warnings to farmers before disease outbreaks occur. The system also generates risk assessments and recommendations to improve farm management and reduce losses. The integration of IoT devices, cloud-based monitoring, and AI-driven analytics helps in improving disease prediction accuracy, reducing manual monitoring efforts, and supporting sustainable poultry farming practices. The proposed solution enables farmers to take preventive actions in advance, thereby improving poultry health, increasing productivity, and minimizing economic losses. Index Terms—Artificial Intelligence, Poultry Disease Prediction, Real-Time Trend Analysis, Machine Learning, Smart Poul try Farming, Internet of Things, Predictive Analytics, Early Disease Detection, Environmental Monitoring, Farm Automation.