Automated Livestock Monitoring: Real-Time Weighing and RFID Integration for Modern Farms

Accurate and stable livestock weighing is essential for monitoring animal health and growth, yet conventional systems are often costly and unstable due to animal movement, which causes fluctuating sensor readings. This study proposes an IoT-based automated weighing system integrated with an RFID module for animal identification and a Kalman filter for noise suppression. Owing to logistical constraints, preliminary validation was performed using dynamic human weights as a proxy for livestock, with the subject positioned at seven different locations on the weighing platform to replicate hoof stance variation. Results show that the Kalman filter reduced fluctuations from ±0.53 kg to ±0.11 kg, achieving stable and reliable readings under simulated dynamic conditions. These findings highlight the potential of the proposed system for practical livestock monitoring; however, the work is currently at the prototype stage with preliminary validation, and future efforts will include testing with live animals to further evaluate performance under real farm conditions.