IoT-Based Livestock Monitoring System for Enhancing Animal Welfare and Environmental Sustainability

Abstract

The livestock production systems yield tremendous continuous flows of data regarding animal health, behavioural and environmental conditions, but traditional monitoring procedures are mostly manual, time-consuming, and cannot provide early warning regarding health- and welfare-related hazards. This paper describes a full-ahead Internet of Things (IoT)-based livestock monitoring system which comprises wearable sensor nodes, environment-sensing units and real time cloud analytics in order to improve animal welfare and foster environmental sustainability. The identified system tracks the main physiological and behavioural indicators (degree of physical activity, food consumption, laying down, and sleep) on a continuous basis and measures the crucial environmental parameters (air quality, ambient temperature and relative humidity). The obtained data is wirelessly promoted to a cloud-based system where it can be visually seen in real time, where intelligent analytics must be utilised, and automated early-warning notifications are given to facilitate predictive and data-driven farm management. To test the technical performance and practical effects of the system, the system was experimentally implemented in a medium-scale livestock farm. Findings showed that the proposed IoT framework allowed following health-related risks by 22-30% and reducing them earlier during the initial signs of diseases and heat-stress prediction and improving overall indicators of welfare, including regular feeding patterns, optimised resting periods, and decreased stress-abnormalities in activities. In addition, the ongoing monitoring of the environment made the control of ventilation more efficient, the use of water and feed more efficient, and the waste and emissions management were more effective, as a result, the overall carbon footprint of the farm was more subject to a quantifiable reduction in terms of the energy consumption. The results verify that IoT and cloud-based analytics can be successfully integrated to improve livestock production systems monitoring accuracy, operational efficiency, and sustainability significantly. This paper shows that intelligent livestock surveillance systems provide an option available in large scale, dependable, and cost-effective solution to precision animal production, contributing to the improvement of animal health, resource allocation efficiency, and sustainability in agricultural activities.