Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
Fecha : |
22/11/2024 |
Actualizado : |
22/11/2024 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Autor : |
PÉREZ, O.; DIERS, B.; MARTIN, N. |
Afiliación : |
OSVALDO MARTIN PEREZ GONZALEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; BRIAN DIERS, Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; NICOLÁS MARTIN, Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA. |
Título : |
Maturity prediction in soybean breeding using aerial images and the random forest machine learning algorithm. |
Fecha de publicación : |
2024 |
Fuente / Imprenta : |
Remote Sensing, 2024, Volume 16, Issue 23, 4343; https://doi.org/10.3390/rs16234343 -- OPEN ACCESS. |
ISSN : |
2072-4292 |
DOI : |
10.3390/rs16234343 |
Idioma : |
Inglés |
Notas : |
Article 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/). |
Contenido : |
ABSTRACT.- 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. |
Palabras claves : |
Agriculture; High-throughput phenotyping; Machine learning; Physiological maturity; Plant breeding; SISTEMA AGRÍCOLA-GANADERO - INIA; UAV; Vegetation indices. |
Asunto categoría : |
F01 Cultivo |
URL : |
https://www.mdpi.com/2072-4292/16/23/4343/pdf
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Marc : |
LEADER 02584naa a2200277 a 4500 001 1064920 005 2024-11-22 008 2024 bl uuuu u00u1 u #d 022 $a2072-4292 024 7 $a10.3390/rs16234343$2DOI 100 1 $aPÉREZ, O. 245 $aMaturity prediction in soybean breeding using aerial images and the random forest machine learning algorithm.$h[electronic resource] 260 $c2024 500 $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/). 520 $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. 653 $aAgriculture 653 $aHigh-throughput phenotyping 653 $aMachine learning 653 $aPhysiological maturity 653 $aPlant breeding 653 $aSISTEMA AGRÍCOLA-GANADERO - INIA 653 $aUAV 653 $aVegetation indices 700 1 $aDIERS, B. 700 1 $aMARTIN, N. 773 $tRemote Sensing, 2024, Volume 16, Issue 23, 4343; https://doi.org/10.3390/rs16234343 -- OPEN ACCESS.
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Registro original : |
INIA Las Brujas (LB) |
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