{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T20:15:00Z","timestamp":1775592900893,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,11,21]],"date-time":"2024-11-21T00:00:00Z","timestamp":1732147200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"North Central Soybean Research Program, (NCSRC)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Several studies have used aerial images to predict physiological maturity (R8 stage) in soybeans (Glycine max (L.) Merr.). However, information for making predictions in the current growing season using models fitted in previous years is still necessary. Using the Random Forest machine learning algorithm and time series of RGB (red, green, blue) and multispectral images taken from a drone, this work aimed to study, in three breeding experiments of plant rows, how maturity predictions are impacted by a number of factors. These include the type of camera used, the number and time between flights, and whether models fitted with data obtained in one or more environments can be used to make accurate predictions in an independent environment. Applying principal component analysis (PCA), it was found that compared to the full set of 8\u201310 flights (R2 = 0.91\u20130.94; RMSE = 1.8\u20131.3 days), using data from three to five fights before harvest had almost no effect on the prediction error (RMSE increase ~0.1 days). Similar prediction accuracy was achieved using either a multispectral or an affordable RGB camera, and the excess green index (ExG) was found to be the important feature in making predictions. Using a model trained with data from two previous years and using fielding notes from check cultivars planted in the test season, the R8 stage was predicted, in 2020, with an error of 2.1 days. Periodically adjusted models could help soybean breeding programs save time when characterizing the cycle length of thousands of plant rows each season.<\/jats:p>","DOI":"10.3390\/rs16234343","type":"journal-article","created":{"date-parts":[[2024,11,21]],"date-time":"2024-11-21T06:11:54Z","timestamp":1732169514000},"page":"4343","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Maturity Prediction in Soybean Breeding Using Aerial Images and the Random Forest Machine Learning Algorithm"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5164-6333","authenticated-orcid":false,"given":"Osvaldo","family":"P\u00e9rez","sequence":"first","affiliation":[{"name":"Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA"},{"name":"Instituto Nacional de Investigaci\u00f3n Agropecuaria (INIA), Estaci\u00f3n Experimental INIA La Estanzuela, Ruta 50 km 11, Colonia 70000, Uruguay"}]},{"given":"Brian","family":"Diers","sequence":"additional","affiliation":[{"name":"Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1587-665X","authenticated-orcid":false,"given":"Nicolas","family":"Martin","sequence":"additional","affiliation":[{"name":"Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3311","DOI":"10.1093\/jxb\/eru187","article-title":"Historical gains in soybean (Glycine max Merr.) seed yield are driven by linear increases in light interception, energy conversion, and partitioning efficiencies","volume":"65","author":"Koester","year":"2014","journal-title":"J. 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