A LoRa IoT Framework with Machine Learning for Remote Livestock Monitoring in Smart Agriculture

This work presents AgroTrack, a LoRa-based IoT framework for remote livestock monitoring in smart agriculture. The system is designed for low-power, long-range communication and supports real-time tracking and basic health assessment of free-range livestock through GPS, motion, and temperature sensors integrated into wearable collars. Data is collected and transmitted via LoRa to gateways and forwarded to a cloud platform for visualization, alerts, and analytics. To enhance its practical deployment, AgroTrack incorporates advanced analytics, including machine learning models for predictive health alerts and behavioral anomaly detection. This integration transforms the framework from a basic monitoring tool into an intelligent decision-support system, enabling farmers to improve livestock management, operational efficiency, and sustainability in rural environments.