Design of an IoT-Based Sheep Health Monitoring System

This study aimed to develop and implement an IoT-based health monitoring system for sheep to accurately monitor vital physiological parameters, such as body temperature, pulse rate, and respiration rate. The system combined various IoT technologies, integrating the ESP32 microcontroller, ThingSpeak platform, and non-invasive sensors. Research and development methods, including system design, implementation, and testing, were utilized. Results showcased that the system effectively provided accurate and real-time monitoring of the targeted physiological parameters. Results demonstrate impressive sensor accuracy with over 90% precision in most instances. Body temperature measurements showed an average error of only 2.6%, while pulse rate measurements displayed an impressively low average error of just 1.2%. Respiration rate measurements exhibited a slightly higher average error at 7.55%. The implementation of this system allowed for early detection of potential health issues, leading to improved decision-making, livestock management, and animal welfare. The study's impact lies in the advancement of non-invasive health monitoring systems for livestock and prospects for AI-assisted predictive analysis, automated decision-making, and extension to other livestock species for overall improvement in animal welfare and farm productivity.