Use of machine learning and IoT for monitoring and tracking of livestock

Abstract

Given the present era’s expanding population and rising necessity of dairy products, livestock supervision is one of the issues that is becoming progressively more of a priority. Moreover, periodic cattle health monitoring is crucial for extending the lifetime and maintaining the quality of livestock. Numerous ailments can be conveyed from animals to people, thus it is important to determine the condition and health status of livestock early on. This research analyzes the elements provided by various innovation systems and associated equipment, as well as their advantages and disadvantages. Additionally, we have suggested a real-time interval system for monitoring cattle health that is based on the Internet of Things (IoT). The suggested system would include a multi-sensor board that has been specially built to track various physiological indicators, such as skin temperature, heart rate, and the Temperature Humidity Index (THI) of the environment’s temperature and humidity. Wi-Fi technology will be used to transfer the observed data to the server, where data analytics will be carried out using machine learning (ML) models such as Decision Tree Classifier and Support Vector Machine (SVM) to identify sick animals and forecast cattle health over time so that prompt medical attention may be given.