{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T02:00:44Z","timestamp":1774317644301,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2022,8,10]],"date-time":"2022-08-10T00:00:00Z","timestamp":1660089600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Two modeling approaches for the estimation of durum wheat yield based on Sentinel-2 data are presented for 66 fields across three growing periods. In the first approach, a previously developed multiple linear regression model (VI-MLR) based on vegetation indices (EVI, NMDI) was used. In the second approach, the reflectance data of all Sentinel-2 bands for several dates during the growth periods were used as input parameters in three machine learning model algorithms, i.e., random forest (RF), k-nearest neighbors (KNN), and boosting regressions (BR). Modeling results were examined against yield data collected by a combine harvester equipped with a yield mapping system. VI-MLR showed a moderate performance with R2 = 0.532 and RMSE = 847 kg ha\u22121. All machine learning approaches enhanced model accuracy when all images during the growing periods were used, especially RF and KNN (R2 &gt; 0.91, RMSE &lt; 360 kg ha\u22121). Additionally, RF and KNN accuracy remained high (R2 &gt; 0.87, RMSE &lt; 455 kg ha\u22121) when images from the start of the growing period until March, i.e., three months before harvest, were used, indicating the high suitability of machine learning on Sentinel-2 data for early yield prediction of durum wheat, information considered essential for precision agriculture applications.<\/jats:p>","DOI":"10.3390\/rs14163880","type":"journal-article","created":{"date-parts":[[2022,8,10]],"date-time":"2022-08-10T09:42:56Z","timestamp":1660124576000},"page":"3880","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Assessing Durum Wheat Yield through Sentinel-2 Imagery: A Machine Learning Approach"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9623-5389","authenticated-orcid":false,"given":"Maria","family":"Bebie","sequence":"first","affiliation":[{"name":"Department of Agriculture Crop Production and Rural Environment, University of Thessaly, Fytokou Str., 38446 Volos, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0768-3230","authenticated-orcid":false,"given":"Chris","family":"Cavalaris","sequence":"additional","affiliation":[{"name":"Department of Agriculture Crop Production and Rural Environment, University of Thessaly, Fytokou Str., 38446 Volos, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3156-4963","authenticated-orcid":false,"given":"Aris","family":"Kyparissis","sequence":"additional","affiliation":[{"name":"Department of Agriculture Crop Production and Rural Environment, University of Thessaly, Fytokou Str., 38446 Volos, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,10]]},"reference":[{"key":"ref_1","first-page":"19","article-title":"Review of crop yield forecasting methods and early warning systems","volume":"Volume 18","author":"Basso","year":"2013","journal-title":"Proceedings of the First Meeting of the Scientific Advisory Committee of the Global Strategy to Improve Agricultural and Rural Statistics"},{"key":"ref_2","first-page":"159","article-title":"Description and performance of CERES-Wheat: A user-oriented wheat yield model","volume":"38","author":"Ritchie","year":"1985","journal-title":"USDA-ARS"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"325","DOI":"10.2134\/agronj1994.00021962008600020022x","article-title":"CropSyst: A collection of object-oriented simulation models of agricultural systems","volume":"86","author":"Campbell","year":"1994","journal-title":"Agron. 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