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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 La Estanzuela. |
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Fecha actual : |
05/11/2020 |
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Actualizado : |
05/09/2022 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
Internacional - -- |
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Autor : |
TREVISAN, R.; PÉREZ, O.; SCHMITZ, N.; DIERS, B.; MARTIN, N |
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Afiliación : |
RODRIGO TREVISAN, Department of Crop Sciences, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA.; OSVALDO MARTIN PEREZ GONZALEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; NATHAN SCHMITZ, GDM Seeds Inc., Gibson City, IL 60936, USA.; BRIAN DIERS, Department of Crop Sciences, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA.; NICOLAS MARTIN, Department of Crop Sciences, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA. |
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Título : |
High-throughput phenotyping of soybean maturity using time Series UAV imagery and convolutional neural networks. |
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Fecha de publicación : |
2020 |
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Fuente / Imprenta : |
Remote Sensing, 2020, 12(21), 3617. OPEN ACCESS. DOI: https://doi.org/10.3390/rs12213617. |
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DOI : |
10.3390/rs12213617 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received: 18 September 2020 / Revised: 28 October 2020 / Accepted: 29 October 2020 / Published: 4 November 2020. |
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Contenido : |
Abstract: Soybean maturity is a trait of critical importance for the development of new soybean cultivars, nevertheless, its characterization based on visual ratings has many challenges.Unmanned aerial vehicles (UAVs) imagery-based high-throughput phenotyping methodologies
have been proposed as an alternative to the traditional visual ratings of pod senescence. However, the lack of scalable and accurate methods to extract the desired information from the images remains a significant bottleneck in breeding programs. The objective of this study was to develop an image-based high-throughput phenotyping system for evaluating soybean maturity in breeding programs. Images were acquired twice a week, starting when the earlier lines began maturation until the latest ones were mature. Two complementary convolutional neural networks (CNN) were
developed to predict the maturity date. The first using a single date and the second using the five best image dates identified by the first model. The proposed CNN architecture was validated using more than 15,000 ground truth observations from five trials, including data from three growing seasons and two countries. The trained model showed good generalization capability with a root mean squared error lower than two days in four out of five trials. Four methods of estimating prediction uncertainty showed potential at identifying different sources of errors in the maturity date predictions. The architecture developed solves limitations of previous research and can be used at scale in commercial breeding programs. MenosAbstract: Soybean maturity is a trait of critical importance for the development of new soybean cultivars, nevertheless, its characterization based on visual ratings has many challenges.Unmanned aerial vehicles (UAVs) imagery-based high-throughput phenotyping methodologies
have been proposed as an alternative to the traditional visual ratings of pod senescence. However, the lack of scalable and accurate methods to extract the desired information from the images remains a significant bottleneck in breeding programs. The objective of this study was to develop an image-based high-throughput phenotyping system for evaluating soybean maturity in breeding programs. Images were acquired twice a week, starting when the earlier lines began maturation until the latest ones were mature. Two complementary convolutional neural networks (CNN) were
developed to predict the maturity date. The first using a single date and the second using the five best image dates identified by the first model. The proposed CNN architecture was validated using more than 15,000 ground truth observations from five trials, including data from three growing seasons and two countries. The trained model showed good generalization capability with a root mean squared error lower than two days in four out of five trials. Four methods of estimating prediction uncertainty showed potential at identifying different sources of errors in the maturity date predictions. The architecture developed solves limitations of previ... Presentar Todo |
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Palabras claves : |
GLYCINE MAX (L.) MERR; MACHINE LEARNING; PHYSIOLOGICAL MATURITY; PLANT BREEDING; SOYBEAN PHENOLOGY. |
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Thesagro : |
MEJORAMIENTO GENETICO DE PLANTAS; SOJA. |
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Asunto categoría : |
F30 Genética vegetal y fitomejoramiento |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/14789/1/remotesensing-12-03617.pdf
https://www.mdpi.com/2072-4292/12/21/3617/htm#
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Marc : |
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024 7 $a10.3390/rs12213617$2DOI
100 1 $aTREVISAN, R.
245 $aHigh-throughput phenotyping of soybean maturity using time Series UAV imagery and convolutional neural networks.$h[electronic resource]
260 $c2020
500 $aArticle history: Received: 18 September 2020 / Revised: 28 October 2020 / Accepted: 29 October 2020 / Published: 4 November 2020.
520 $aAbstract: Soybean maturity is a trait of critical importance for the development of new soybean cultivars, nevertheless, its characterization based on visual ratings has many challenges.Unmanned aerial vehicles (UAVs) imagery-based high-throughput phenotyping methodologies have been proposed as an alternative to the traditional visual ratings of pod senescence. However, the lack of scalable and accurate methods to extract the desired information from the images remains a significant bottleneck in breeding programs. The objective of this study was to develop an image-based high-throughput phenotyping system for evaluating soybean maturity in breeding programs. Images were acquired twice a week, starting when the earlier lines began maturation until the latest ones were mature. Two complementary convolutional neural networks (CNN) were developed to predict the maturity date. The first using a single date and the second using the five best image dates identified by the first model. The proposed CNN architecture was validated using more than 15,000 ground truth observations from five trials, including data from three growing seasons and two countries. The trained model showed good generalization capability with a root mean squared error lower than two days in four out of five trials. Four methods of estimating prediction uncertainty showed potential at identifying different sources of errors in the maturity date predictions. The architecture developed solves limitations of previous research and can be used at scale in commercial breeding programs.
650 $aMEJORAMIENTO GENETICO DE PLANTAS
650 $aSOJA
653 $aGLYCINE MAX (L.) MERR
653 $aMACHINE LEARNING
653 $aPHYSIOLOGICAL MATURITY
653 $aPLANT BREEDING
653 $aSOYBEAN PHENOLOGY
700 1 $aPÉREZ, O.
700 1 $aSCHMITZ, N.
700 1 $aDIERS, B.
700 1 $aMARTIN, N
773 $tRemote Sensing, 2020, 12(21), 3617. OPEN ACCESS. DOI: https://doi.org/10.3390/rs12213617.
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