Real-Time Tracking of Animal Movements in Pasture-Based Livestock Systems Using Internet of Things and Artificial Intelligence

Abstract

Modern agriculture has witnessed a transformative shift in the management of pasture-based livestock systems through the incorporation of advanced technologies. This study introduces a real-time tracking system that utilizes Internet of Things (IoT) devices and Artificial Intelligence (AI) algorithms to comprehensively monitor animal movements. Demonstrating a reliability of 95.3%, the system ensures precise capture and recording of animal locations within pastures. With an average response time of 2.4 seconds, the system's high responsiveness enables timely interventions, enhancing overall livestock management. The analysis of spatial distribution provides valuable insights into grazing patterns, guiding targeted strategies like rotational grazing to optimize pasture utilization. AI-driven behavioral classification, with an accuracy of 93.8%, offers a nuanced understanding of animal behavior, extending beyond conventional tracking. Health monitoring sensors, contributing to a sensitivity of 89.5% and specificity of 92.1%, facilitate early detection of health issues, minimizing veterinary costs and promoting sustained productivity. Positioning the real-time tracking system as a vital tool for optimizing livestock management, it empowers farmers with real-time data for informed decision-making, improved grazing patterns, and enhanced herd management. The successful implementation of this system lays the foundation for future advancements in precision livestock farming, with the potential for integration with additional IoT devices and AI models, promising a comprehensive and automated approach to pasture-based livestock management. This research significantly adds to the dialogue on utilizing technology for sustainable and efficient livestock farming practices.