DESIGN AND DEVELOPMENT OF IOT BASED ANIMAL FARM MANAGEMENT SYSTEM USING WSN

Abstract

The increasing demand for sustainable and efficient agricultural practices has led to the adoption of smart technologies in livestock management. This research work focuses on the design and development of an IoT-based farm animal tracking system using Wireless Sensor Networks (WSN), aimed at improving the monitoring and management of farm animals. The system is developed in four distinct but integrated modules to cover multiple aspects of farm automation. The first module is a livestock management system that monitors the food and water levels for animals using sensors and enables remote control through a mobile application (Blynk). This ensures that the basic needs of the animals are met efficiently and consistently. The second module is a floor cleaning rover, which is an autonomous robot capable of following a predefined path to maintain cleanliness in animal enclosures. This contributes to better hygiene and reduces the chances of disease spread among livestock. The third module involves an animal collar system embedded with sensors to track the health status of individual animals, such as their body temperature and movement patterns. This helps in early detection of illness and ensures timely veterinary attention. The fourth module is a geo-fencing system designed to prevent animals from straying beyond designated boundaries. If an animal crosses the virtual fence, the system triggers a vibration alert on the collar and sends the location details to the administrator via cloud communication. The proposed system uses microcontrollers like the ESP32, along with various sensors such as DHT11, IR sensors, and ultrasonic sensors to collect and transmit real-time data. The data is stored and visualized through a cloud platform, enabling farmers to make data-driven decisions. The system demonstrates a cost-effective and scalable solution for precision livestock farming, with the potential to enhance productivity, animal welfare, and operational efficiency in agricultural settings.