02584naa a2200277 a 450000100080000000500110000800800410001902200140006002400280007410000150010224501370011726000090025450009030026352008220116665300160198865300320200465300210203665300270205765300190208465300380210365300080214165300230214970000140217270000150218677301050220110649202024-11-22       2024    bl uuuu     u00u1 u    #d  a2072-42927 a10.3390/rs162343432DOI1 aPÉREZ, O.  aMaturity prediction in soybean breeding using aerial images and the random forest machine learning algorithm.h[electronic resource]  c2024  aArticle history: Submission received 27 September 2024, Revised 24 October 2024, Accepted 5 November 2024, Published 21 November 2024. -- Academic editors: Wei Su, Quanjun Jiao, Bo Liu, Xing Li, Qiaoyun Xie. -- Funding: This research received funding from North Central Soybean Research Program, (NCSRC), "SOYGEN 3: Building capacity to increase soybean genetic gain for yield and composition through combining genomics-assisted breeding with characterization of future environments". --  This article belongs to the Special Issue Remote Sensing and Machine Learning in Vegetation Biophysical Parameters Estimation (Second Edition)(https://www.mdpi.com/journal/remotesensing/special_issues/G6CM96JWQY) . --  License: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).  aABSTRACT.- 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.  © 2024 by the authors. Licensee MDPI, Basel, Switzerland.  aAgriculture  aHigh-throughput phenotyping  aMachine learning  aPhysiological maturity  aPlant breeding  aSISTEMA AGRÍCOLA-GANADERO - INIA  aUAV  aVegetation indices1 aDIERS, B.1 aMARTIN, N.  tRemote Sensing, 2024, Volume 16, Issue 23, 4343; https://doi.org/10.3390/rs16234343  -- OPEN ACCESS.