{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T09:04:08Z","timestamp":1773479048586,"version":"3.50.1"},"reference-count":63,"publisher":"Association for Computing Machinery (ACM)","issue":"1","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM J. Comput. Sustain. Soc."],"published-print":{"date-parts":[[2026,3,31]]},"abstract":"<jats:p>\n                    Nitrous oxide (N\n                    <jats:sub>2<\/jats:sub>\n                    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\n                    <jats:sub>2<\/jats:sub>\n                    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\n                    <jats:sub>2<\/jats:sub>\n                    O emission predictions by using only proximal sensor data. We gathered N\n                    <jats:sub>2<\/jats:sub>\n                    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\n                    <jats:sub>2<\/jats:sub>\n                    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\u20130.90 and 0.71\u20130.89 R\n                    <jats:sup>2<\/jats:sup>\n                    , respectively. When training models using 2021 data to predict 2022 emissions, reasonable accuracy (up to 0.62 R\n                    <jats:sup>2<\/jats:sup>\n                    ) was achieved by the multilayer perceptron model, which was one of the best performing models, alongside LSTM, in these experiments.\n                  <\/jats:p>","DOI":"10.1145\/3776747","type":"journal-article","created":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T11:07:45Z","timestamp":1763550465000},"page":"1-35","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Nitrous Oxide Emission Prediction Using IoT Soil and Weather Sensor Data"],"prefix":"10.1145","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8992-3431","authenticated-orcid":false,"given":"Patrick","family":"Killeen","sequence":"first","affiliation":[{"name":"School of Electrical Engineering and Computer Science, University of Ottawa","place":["Ottawa, Canada"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8831-6311","authenticated-orcid":false,"given":"Ci","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Computer Science, University of Ottawa","place":["Ottawa, Canada"]}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5665-7075","authenticated-orcid":false,"given":"Futong","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Computer Science, University of Ottawa","place":["Ottawa, Canada"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9119-9451","authenticated-orcid":false,"given":"Iluju","family":"Kiringa","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Computer Science, University of Ottawa","place":["Ottawa, Canada"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6039-6751","authenticated-orcid":false,"given":"Tet","family":"Yeap","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Computer Science, University of Ottawa","place":["Ottawa, Canada"]}]}],"member":"320","published-online":{"date-parts":[[2026,3,14]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMLCN.2023.3311749"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.3390\/agriculture11060475"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.array.2019.100009"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.geoderma.2018.12.031"},{"issue":"5","key":"e_1_3_3_6_2","doi-asserted-by":"crossref","first-page":"1437","DOI":"10.1002\/saj2.20292","article-title":"Untangling soil-weather drivers of daily N2O emissions and fertilizer management mitigation strategies in no-till corn","volume":"85","author":"Bastos Leonardo M.","year":"2021","unstructured":"Leonardo M. Bastos, Charles W. Rice, Peter J. Tomlinson, and David Mengel. 2021. Untangling soil-weather drivers of daily N2O emissions and fertilizer management mitigation strategies in no-till corn. Soil Science Society of America Journal 85, 5 (2021), 1437\u20131447.","journal-title":"Soil Science Society of America Journal"},{"issue":"2","key":"e_1_3_3_7_2","doi-asserted-by":"crossref","first-page":"283","DOI":"10.3390\/atmos14020283","article-title":"GHG Global emission prediction of synthetic N fertilizers using expectile regression techniques","volume":"14","author":"Benghzial Kaoutar","year":"2023","unstructured":"Kaoutar Benghzial, Hind Raki, Sami Bamansour, Mouad Elhamdi, Yahya Aalaila, and Diego H. Peluffo-Ord\u00f3\u00f1ez. 2023. GHG Global emission prediction of synthetic N fertilizers using expectile regression techniques. 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