Lumpy Diseases detection using Machine Learning

Abstract

Abstract— Machine learning algorithms for improving animal health monitoring have accelerated the creation of ML applications for behavioral and physiological monitoring systems, including ML-based animal health monitoring systems. Currently, farm animals are raised all over the world, and it is necessary to monitor their physiological processes. It is suggested in this article to use machine learning models to continuously monitor each animal's vital signs and look for biological changes. In this model, crucial data is gathered via IoT devices, and data analysis is carried out using machine learning techniques to identify potential dangers from changes in an animal's physiological state. The results of the experiments demonstrate that the suggested model is accurate and efficient enough to identify animal situations. For our purposes, the CNN and YOLO accuracy of more than 90% is a promising outcome.