Nitrous Oxide Emission Prediction Using IoT Soil and Weather Sensor Data
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".