Evaluation of the Kalman Filter Algorithm for IoT-Based Air and Light Quality Optimization

Abstract

Health and productivity of poultry, chicken farms require an environment with ideal air and lighting conditions. The urgency of this research requires a real-time IoT system equipped with a Kalman Filter data processing algorithm to reduce sensor noise and improve reading accuracy, as manual monitoring is often inaccurate and slow. Research objectives: 1) Design an IoT system based on multi-parameter sensors (gas and light) to monitor the environmental conditions of chicken farms; 2) Implement a Kalman Filter to filter sensor data noise and produce stable and accurate readings; 3) Evaluate system performance through field tests by comparing filtered data with actual data. The outcomes achieved are proof of submission to a Sinta-accredited journal and intellectual property rights for the monitoring system developed. The implications of this research provide appropriate technological solutions for chicken farms to prevent economic losses due to suboptimal environments.