Abstract

Precision agriculture leverages advanced technologies to enhance efficiency and productivity across various sectors of agriculture, including livestock management. However, integrated and interconnected livestock monitoring systems remain limited. This study proposes an internet of things (IoT)-based livestock monitoring system that utilizes sensor fusion to improve weight management and feeding efficiency. The system integrates radio-frequency identification (RFID) technology, load cells, and environmental sensors (AHT20) to enable real-time monitoring of livestock weight, feed intake, and ambient conditions. Data from these sensors are processed by an ESP32 microcontroller unit (MCU) and transmitted to a cloud server, supporting efficient data visualization and management through web and mobile applications. The sensor fusion technique combines data from multiple sensors to enhance measurement accuracy and reliability. The implementation of the Kalman filter algorithm effectively reduces noise in weight measurements, while RFID technology ensures accurate identification of individual animals. The system was evaluated in a controlled environment on a small-scale goat farm, achieving 100% accuracy in livestock identification and a ±0.5% margin of error in weight measurement. Furthermore, the AHT20 sensor reliably monitors temperature and humidity, helping maintain optimal environmental conditions for livestock welfare. By integrating sensor fusion and IoT technologies, the system significantly reduces the need for manual labour, improves livestock health monitoring, and supports data-driven decision-making. This research combines weight tracking, feeding control, and environmental sensing, thus advancing precision livestock farming and laying the groundwork for future innovations in smart agricultural systems.

Keywords

Precision agriculture, Livestock monitoring, Sensor fusion, Internet of things (IoT), RFID identification, Kalman filter.

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