Virtual Booth

The "ANIMO – Animal Monitoring System Based on Internet of Things" project addresses the urgent need for improved animal health monitoring in livestock management. Traditional methods, primarily reliant on visual inspections, often overlook subtle early signs of disease, leading to delayed interventions that can result in significant animal suffering, economic losses for farmers, and potential public health risks from zoonotic diseases. This study aims to develop a prototype smart animal monitoring system that utilizes Internet of Things (IoT) technology and various sensors for real-time monitoring of livestock health. The primary objective is to identify essential features and capabilities for effective animal monitoring through collaboration with practitioners. Key parameters monitored include body temperature, environmental temperature, humidity, and activity levels. The research involves selecting and integrating relevant sensors, such as temperature sensors, environmental sensors, and accelerometers, to accurately measure these parameters. The prototype system is designed to provide timely alerts for potential health issues, enabling prompt action from farmers and veterinarians. Field trials are crucial for evaluating the prototype's effectiveness in real-world conditions. Feedback from practitioners helps refine the system, ensuring its practicality and usability. Findings indicate a significant relationship between environmental conditions and livestock health metrics, particularly in rabbits, where temperature and humidity levels impact their ability to regulate body temperature. The Animo system enhances monitoring capabilities beyond traditional methods, contributing to improved animal welfare and farm management. By providing real-time data and alerts, the system facilitates timely interventions, reducing the risks of economic losses and disease spread. The study concludes that the Animo system represents a significant advancement in livestock health monitoring, with potential applications across various animal species. Future research will focus on expanding the system's capabilities, integrating additional health parameters, and conducting long-term field trials to further validate its effectiveness and contribution to sustainable animal management practices.