LiveStock IQ: Design and Implementation of a Machine Learning-Based Smart Livestock Management System for Real-Time Health Monitoring, Behavior Analysis, and Automated Farm Decision Support

Traditional livestock farming in rural parts of India and developing countries still depends on human observation and experiential decision-making in the process of monitoring the health of animals, diagnosing diseases, and conducting various farm activities. The problem is that the aforementioned practice tends to cause delays in detecting illnesses among livestock, resulting in unnecessary deaths of cattle and economic losses for farmers without any access to a veterinarian at all times. However, existing applications and platforms used for farm management purposes are either prohibitively costly, lack stable internet connection, or cannot be employed to solve problems peculiar to resource-poor areas. Therefore, this paper describes a Machine Learning-Based Smart Livestock Management System, which is a sophisticated, yet inexpensive, solution developed specifically for low-resource regions. The presented product incorporates various IoT devices and can monitor key physiological parameters such as body temperature, heart rate, level of activity, and eating habits of individual animals. The trained machine learning model analyzes the live data from sensors to identify anomalies, predict early signs of diseases like Foot-and-Mouth Disease and Mastitis, and create automatic alerts about the health status of the animal to the farmers through an easy-to-use mobile application without requiring cloud storage or broadband connection. This system uses a Random Forest classifier model trained on a livestock health data set consisting of more than 12,000 labeled data samples, providing an accuracy of disease prediction as high as 94.3% with a maximum error rate of less than 3%. Classification of behavioral patterns with respect to normal grazing, resting, and distressed states with 91.7% accuracy is done by analyzing the data collected from accelerometers. An easy-to-use Android app helps farmers to monitor the health status, schedule feed, and get expert advice in English and Tamil languages.