The Use of Wireless Sensor Networks for the Monitoring and Tracking of Cattle's Health

Abstract

In order to avoid diseases, increase output, and improve animal welfare, contemporary livestock management relies heavily on cattle health monitoring. Keeping tabs on the health of cattle the old-fashioned way is tedious, error-prone, and requires a lot of manual effort. In response to these issues, we provide an Internet of Things (IoT) system for monitoring and tracking the health of cattle that incorporates DHT11 humidity and temperature sensors, pulse sensors, GPS tracking, and a buzzer alarm system. In order to provide real-time health insights, the DHT11 sensor constantly analyzes the cattle's core temperature and relative humidity. To aid in the early diagnosis of stress, fever, and other abnormalities in health, a state-of-the-art pulse sensor detects the heart rate. The GPS gadget monitors the cattle's whereabouts in real time, which helps keep them secure and cuts down on instances of livestock theft. In addition, a buzzer warning system is built in to let farmers or vets know if there are any unusual health readings, so they can intervene quickly. The system's architecture is built on an Arduino-based microcontroller, which collects data from sensors and communicates with a cloud service to provide notifications. With the data acquired, farmers can track their cattle's whereabouts and health from anywhere using a smartphone app. The Internet of Things (IoT) and wireless sensor networks work together to provide real-time tracking, which cuts down on the need for human health checks and boosts productivity. To update cow farming, this technology offers a scalable and cost-effective option. The suggested method maximizes farm output, improves cow wellbeing, and helps with livestock management by allowing early illness identification, real-time monitoring, and automatic warning systems. A more proactive and efficient livestock farming industry may be possible with the addition of AI-based illness prediction, analytics powered by machine learning, and automated drug delivery in the future.