While significant progress has been made in monitoring animal health and feeding procedures, less attention has been given to the continuous assessment of environmental parameters in veterinary contexts. However, factors such as temperature and humidity, air quality (e.g., ammonia, CO2, and particulate matter levels), ventilation efficiency, noise levels, and lighting conditions play a crucial role in animal welfare, disease prevention, and overall farm productivity. Inadequate environmental conditions can contribute to respiratory issues, stress, and reduced growth performance, highlighting the need for more advanced monitoring solutions. To address this gap, our research explores the potential of low-cost sensor networks, which we have already developed for civil, greenhouse, and agricultural environments 1-3. By adapting and optimizing these technologies for veterinary applications, we aim to provide a cost-effective and scalable solution for real-time environmental monitoring. This approach complements existing health and feeding management systems, offering a more comprehensive strategy for ensuring optimal living conditions in veterinary settings. IoT-based sensor networks offer real-time data acquisition and remote monitoring capabilities, allowing veterinarians and farm operators to track critical environmental factors that directly impact animal health, productivity, and biosecurity. Specifically, we investigate the feasibility of using cost-effective sensor platforms based on Arduino and other open-source technologies to measure key environmental parameters such as temperature, humidity, air quality, carbon dioxide and ammonia concentration in livestock facilities. These systems provide a scalable and accessible solution to enhance early warning mechanisms, optimize resource management, and improve overall animal welfare. This study highlights the benefits and challenges associated with integrating IoT-driven environmental monitoring into veterinary practice. By leveraging low-cost and programmable sensor technology, we demonstrate how real-time environmental data collection can bridge the gap between traditional veterinary care and advanced precision livestock farming. Our contribution is mainly technical, considering the refinement of sensor systems, which, while cost-effective, may not always achieve the same level of precision and accuracy as custombuilt devices; and serves as an illustrative example in addressing the challenges of integrating monitoring data into an IoT network. To this aim, we build upon the IoT system developed at the University of Pisa as a model for effectively managing and utilizing sensor data within a connected infrastructure. Based on this network, we analyze specific sensors that can be better suited to veterinary environments, ensuring optimal adaptation to their unique conditions and requirements.
Franco et al. (Wed,) studied this question.