Real-time monitoring of ammonia emissions from cereal crops using LoRaWAN-based sensing technology

Nyéki, Anikó and Alahmad, Tarek and Arshad, Sana and Gombkötő, Nóra and Neményi, Miklós and Mirzaei, Morad and Szabó, Szilárd and Harsányi, Endre and Al-Dalahmeh, Main and Mohammed, Safwan (2025) Real-time monitoring of ammonia emissions from cereal crops using LoRaWAN-based sensing technology. SCIENTIFIC REPORTS, 16. No. 1446. ISSN 2045-2322

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Abstract

This study presents a LoRaWAN-based IoT system developed for real-time monitoring of ammonia (NH₃) emissions in cereal crop fields. Sustainable agriculture increasingly demands on-farm greenhouse gas (GHG) tracking linked to environmental variables. IoT offers efficient real-time monitoring of soil NH₃ emissions and associated factors. Our research introduces a unique Field Monitoring Laboratory: a LoRaWAN-connected IoT system integrating soil, crop, and microclimate sensors to observe NH₃⁺, air temperature, rainfall, humidity, soil temperature, and moisture content. The system comprises a field lab, data server, and custom dashboard with analytics capabilities. NH₃ fluxes were measured in autumn-sown cereals across three growing seasons (2020–2023). Tukey’s Kramer test revealed significant (p<0.05, p<0.001) differences in NH₃ emissions and environmental variables between years. Highest NH₃ emissions (1.94 ppm in 2020, 1.71 ppm in 2021) coincided with elevated air (25–31 °C) and soil (21–23 °C) temperatures, and higher mean and peak rainfall (0.40–0.48 mm average; max 9–31.6 mm). Principal Component Analysis showed 65.8% variance explained by PC1 and PC2, with high loadings from temperature and soil moisture. Spearman’s correlation indicated moderate positive associations (r=0.38–0.4, p<0.05) of NH₃ with soil moisture at 20 cm and 40 cm of soil depth, and a weak negative correlation (r = -0.16 and −0.17) with soil temperature at 20 cm and 40 cm. The study underscores the potential of IoT technology using calibrated gas sensors and LoRaWAN for real-time NH₃ and environmental monitoring, enabling informed decision-making in smart agriculture.

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