Nitrous Oxide Emission Prediction Using IoT Soil and Weather Sensor Data

Nitrous oxide (N 2 O) is a powerful greenhouse gas (GHG) that has nearly 273 times more global warming potential than carbon dioxide over a 100-year period. By 2030, the Canadian government is requiring Canadian farmers to reduce their synthetic fertilizer-based GHG emissions by one third. Measuring N 2 O emissions is therefore important, but high frequency sampling requires expensive sensing equipment. Therefore, we propose replacing the expensive equipment with an affordable in-field Internet of Things (IoT) sensing device equipped with intelligence to make reasonably accurate N 2 O emission predictions by using only proximal sensor data. We gathered N 2 O emission, weather, and soil sensor data from a smart farm located in Ottawa, Ontario, Canada, during the 2021, 2022, and 2023 growing seasons. We built a soil sensing microprocessor-based prototype. We performed N 2 O emission prediction single-year interpolation (or gap-filling) and multi-year extrapolation experiments using data-driven models. Random forest and long short-term memory (LSTM) were the best performing models at interpolating, achieving 0.70–0.90 and 0.71–0.89 R 2 , respectively. When training models using 2021 data to predict 2022 emissions, reasonable accuracy (up to 0.62 R 2 ) was achieved by the multilayer perceptron model, which was one of the best performing models, alongside LSTM, in these experiments.