A LoRa IoT Framework with Machine Learning for Remote Livestock Monitoring in Smart Agriculture
Hitesh Mohapatra
TL;DR
AgroTrack tackles the challenge of remote, low-power livestock monitoring in rural settings by integrating GPS, motion, and temperature sensing on wearable collars with LoRaWAN to gateways and cloud analytics. The framework delivers real-time tracking, health alerts, and intuitive dashboards, and demonstrates strong field performance with transmission up to 6.5 km, 28-day collar life, and 97.5% packet reliability. It extends the baseline system with ML-supported predictive health analytics and edge computing, improving scalability, robustness, and decision support, while addressing security and privacy through encryption and governance. The work provides practical guidance for deploying scalable, energy-efficient livestock monitoring in low-connectivity environments and outlines concrete paths for future enhancements, including edge processing, multi-hop networking, solar harvesting, and large-scale validation.
Abstract
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.