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Registros recuperados : 9 | |
1. |  | PÉREZ, O.; DIERS, B.; MARTIN, N. Maturity prediction in soybean breeding using aerial images and the random forest machine learning algorithm. Remote Sensing, 2024, Volume 16, Issue 23, 4343; https://doi.org/10.3390/rs16234343 -- OPEN ACCESS. 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...Biblioteca(s): INIA Las Brujas. |
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3. |  | MARTÍN, N.; BEYHAUT, E.; ALTIER, N.; ABREO, E. Prospección y caracterización de Bacillus s.l. para mejorar la nutrición fosfatada de la soja. [p49]. Bloque 3: Manejo de insectos-plaga, malezas y enfermedades. In: Sociedad Uruguaya de Fitopatología Jornada Uruguaya de Fitopatología, 4., Jornada Uruguaya de Protección Vegetal, 2., 1° setiembre, 2017, Montevideo, Uruguay. Libro de resúmenes. Montevideo (UY): Sociedad Uruguay de Fitopatología (SUFIT), 2017. p. 74. Financiamiento: Proyecto Alianzas de la Agencia Nacional de Investigación e Innovación (ANII), conjuntamente con las empresas Calister S.A., Lafoner S.A., y Lage y Cía. S.A., el Instituto Pasteur Montevideo y el Instituto Nacional de...Biblioteca(s): INIA Las Brujas. |
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6. |  | TORRES, P.; BEYHAUT, E.; ALTIER, N.; FRESIA, P.; GARAYCOCHEA, S.; MARTÍN, N.; REGO, N.; CRISPO, N.; LAGE, M.; ARROSPIDE, G.; SUNDBERG, G.; CUITIÑO, M.J.; ABREO, E. Bacillus mineralizadores de fósforo en soja: efecto sobre la nutrición, el rendimiento y la comunidad de bacterias de la rizosfera. O2. Módulo 1: Promoción del crecimiento vegetal mediada por microorganismos. In: Abreo, E.; Beyhaut, E.; Rivas, F. (Eds.). Simposio Microorganismos para la Agricultura, 2. [Resúmenes y Posters]. Canelones (UY): INIA, 2022. p.4. (Serie Actividades de Difusión; 801) Financiamiento: proyecto ALI_1_2014_1_5056_ANII.Biblioteca(s): INIA Las Brujas. |
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7. |  | TORRES, P.; ABREO, E.; BEYHAUT, E.; GARAYCOCHEA, S.; MARTÍN, N.; REGO, N.; CRISPO, N.; LAGE, M.; ARROSPIDE, G.; SUNDBERG, G.; CUITIÑO, M.J.; ALTIER, N. Desarrollo de un inoculante para la movilización de fósforo como insumo en la producción agrícola. [resumen]. Resúmenes Sesión 7: Del laboratorio al campo. In: Reunión Latinoamericana de Rizobiología, XXX; Conferencia Latinoamericana de microorganismos promotores del crecimiento vegetal, V., Montevideo (Uruguay), 4-8 octubre 2021. Memoria RELAR-PGPR-2021. p.155. Expone: Torres, P. -- Contacto: ptorres@inia.org.uy -- Presentado en diálogos de investigación.Biblioteca(s): INIA Las Brujas. |
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8. |  | TORRES, P.; BEYHAUT, E.; ALTIER, N.; FRESIA, P.; GARAYCOCHEA, S.; MARTIN, N.; REGO, N.; CRISPO, N.; LAGE, M.; ARROSPIDE, G.; SUNDBERG, G.; CUITIÑO, M.J.; ABREO, E. Phosphorus mineralizing Bacillus co-inoculated with rhizobia interact with phosphorus fertilization to improve soybeans yield and affect bacterial rhizospheric community. [abstract. Theme 2 - Phosphorus acquisition by plants and microorganisms. Oral presentation. In: Michelini, D.; Garaycochea, S. (Eds.). 7th Phosphorus in Soils and Plants Symposium (PSP7). "Towards a sustainable phosphorus utilization in agroecosystems." Book of abstracts. PSP7, 3-7 October 2022, Montevideo, Uruguay. p.36. FUENTE DE FINANCIAMIENTO: Proyecto - ALI_1_2014_1_5046, ANII. -- Published By: The organizing committee of the 7th Symposium on Phosphorus in Soils and Plants (PSP7)- National Agricultural Research Institute and School of Agronomy,...Biblioteca(s): INIA Las Brujas. |
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9. |  | ALTIER, N.; ABREO, E.; BEYHAUT, E.; GARAYCOCHEA, S.; TORRES, P.; CERECETTO, V.; MARTÍN, N.; CUITIÑO, M.J.; CRISPO, M.; ARÉVALO, A.P.; REGO, N.; ARROSPIDE, G.; LAGE, M.; SUNDBERG, G. Desarrollo de un biofertilizante microbiano para aumentar la disponibilidad de fósforo en el cultivo de soja. * Sustentabilidad. Revista INIA Uruguay, 2020, no.62, p.95-100. (Revista INIA; 62) *Proyecto: Desarrollo de inoculantes para la movilización de fósforo como insumo en la producción agrícola. INIA/IP/EMPRESAS. Proyecto financiado
por la herramienta Alianzas para la Innovación de la Agencia Nacional de Investigación e...Biblioteca(s): INIA Las Brujas. |
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Registros recuperados : 9 | |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
Fecha actual : |
22/11/2024 |
Actualizado : |
22/11/2024 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Circulación / Nivel : |
Internacional - -- |
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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