{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T14:19:33Z","timestamp":1766067573148},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2022,6,14]],"date-time":"2022-06-14T00:00:00Z","timestamp":1655164800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,6,14]]},"abstract":"<jats:p>This article investigates the problem of agricultural yield prediction, including that of soybeans. Soya is a very nutritious legume and one of the species producing the most protein per hectare. It is used in particular as a source of protein in animal feed and as an oilseed. However, agricultural systems are dependent on climatic variability, and farmers must deal with this factor to optimize their activities. This article describes a method for predicting soybean yields based on machine learning. The comparative study shows that one can obtain forecasts with less than 2% margin of error using the Random Forest algorithm. In addition, the results obtained in this study can be extended to many other crops such as maize or rice.<\/jats:p>","DOI":"10.3233\/aise220028","type":"book-chapter","created":{"date-parts":[[2022,6,16]],"date-time":"2022-06-16T07:22:06Z","timestamp":1655364126000},"source":"Crossref","is-referenced-by-count":2,"title":["Machine Learning Forecast of Soybean Yields on South Brazil"],"prefix":"10.3233","author":[{"given":"Lilian","family":"Hollard","sequence":"first","affiliation":[{"name":"Universit\u00e9 de Reims Champagne Ardenne, LICIIS \u2013 LRC CEA DIGIT, 51687 Reims Cedex 2, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Angelica","family":"Durigon","sequence":"additional","affiliation":[{"name":"Universidade Federal de Santa Maria, Centre de Ci\u00eancias Rurais, 97105-900 Santa Maria \u2013 RS, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luiz Angelo","family":"Steffenel","sequence":"additional","affiliation":[{"name":"Universit\u00e9 de Reims Champagne Ardenne, LICIIS \u2013 LRC CEA DIGIT, 51687 Reims Cedex 2, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Ambient Intelligence and Smart Environments","Workshops at 18th International Conference on Intelligent Environments (IE2022)"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/AISE220028","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,16]],"date-time":"2022-06-16T07:22:07Z","timestamp":1655364127000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/AISE220028"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,14]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/aise220028","relation":{},"ISSN":["1875-4163","1875-4171"],"issn-type":[{"value":"1875-4163","type":"print"},{"value":"1875-4171","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,14]]}}}