Synchronous monitoring agricultural water qualities and greenhouse gas emissions based on low-cost Internet of Things and intelligent algorithms

Abstract

This study addressed the challenges of cost and portability in synchronous monitoring water quality and greenhouse gas emissions in paddy-dominated regions by developing a novel Internet of Things (IoT)-based monitoring system (WG-IoT-MS). The system, equipped with low-cost sensors and integrated intelligent algorithms, enabled real-time monitoring of dissolved N₂O concentrations. Combined with an air-water gas exchange model, the system achieved efficient monitoring and simulation of CO₂ and N₂O emissions from agricultural water bodies while reducing monitoring costs by approximately 60%. The proposed method was validated in paddy-dominated regions in Danyang, China. Results indicated the excellence of the dissolved N₂O concentration model based on support vector regression, demonstrating accurate predictions within a concentration range of 2.003 to 13.247 g/L. Notably, the model maintained acceptable predictive accuracy (R² > 0.70) even when some variables were partially absent (with the number of missing variables < 2 and the missing proportion (MP) ≤ 50%), making up for the data loss caused by sensor malfunctions. Furthermore, the model performed well (R² > 0.80) when testing data sourced from paddy fields and lakes. Finally, CO₂ and N₂O emissions were successfully monitored, with the results validated using a floating chamber method (R² > 0.70). The method provides crucial technical support for quantitative assessment of water quality and greenhouse gas emissions in paddy-dominated regions, laying a foundation for formulating effective emission reduction strategies.