Predictive Modelling of Livestock Health using Wearable Sensors

Abstract

Animal welfare, farm productivity, and food security are basically on animal health management. The conventional forms of monitoring make a lot of use of manual surveillance, which takes time and mostly identifies diseases when they are on advanced stages. This paper provides a predictive modelling tool of animal health care utilizing wearable sensors, Internet of Things (IoT), and machine learning tools that deliver real time data of physiological and behavioural aspects of animals like body temperature and activity levels, rumination patterns, and location. The resulting data are then sent via the wireless connections to the edge and cloud systems where the preprocessing and predictive analytics are implemented to detect abnormalities and any health risks. Different machine learning methods are used to predict health conditions and provide early warning to farmers and veterinarians. The experimental outcomes prove that multimodal sensor integration is a better way to predict health conditions and provide an opportunity to intervene before it is too late than traditional methods of monitoring. The suggested system promotes the active disease control, lessening the economic losses, and increasing the accuracy of the livestock farming based on the real-time and data-driven decision-making.