TY - JOUR A1 - Parsiegel, Raphael A1 - Budag Becker, Miguel A1 - Try, Pieter A1 - Gebhard, Marion T1 - Virtual MOS Sensor Array Design for Ammonia Monitoring in Pig Barns JF - Sensors N2 - Abstract Animal welfare in barns is strongly influenced by air quality, with gaseous emissions like ammonia posing significant respiratory health risks. However, current state-of-the-art ammonia monitoring systems are labor-intensive and expensive. Metal Oxide Semiconductor (MOS) sensors offer a promising alternative due to their compatibility with sensor networks, enabling high-resolution ammonia monitoring across spatial and temporal scales. While MOS sensors exhibit high sensitivity to various volatile compounds, temperature-cycled operation is commonly employed to enhance selectivity, effectively creating virtual sensor arrays. This study aims to improve ammonia detection by designing a virtual sensor array through a cyclic data-driven approach, integrating machine learning with solid-state sensor modeling. The results of a two-week dataset with measurements of four different pig barns demonstrate ammonia sensing with a sampling rate of about 2/min and a range of 1–30 ppm. The method is robust and exhibits a 10% increase in normalized RMSE when comparing testing results of an unseen sensor module with results of the training dataset. A filter membrane boosts accuracy and prevents data loss due to contamination, such as flyspecks. Overall, the used MOS sensor BME688 is effective and economical for widespread continuous ammonia monitoring and localization of ammonia sources in pig barns. KW - livestock monitoring KW - MOS sensor KW - ammonia monitoring KW - electronic nose KW - temperature-cycled operation KW - virtual sensor array KW - smart sensor KW - smart farming KW - machine learning Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:1010-opus4-48109 VL - 2025 IS - 25(8), Special Issue AI, IoT and Smart Sensors for Precision Agriculture: 2nd Edition SP - 1 EP - 27 PB - MDPI ER -