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| Registros recuperados : 34 | |
| 1. |  | PÉREZ, O. Efecto del déficit hídrico en cinco cultivares de trigo: tesis Mag. Sc. Montevideo (UY): UdelaR, Facultad de Agronomía, 2015. Tesis aprobada por el tribunal integrado por el Dr. Omar Borsani, Dr. Andrés Berger y Dr. Daniel Miralles el 1º de octubre de 2015. Autor: Osvaldo Pérez González. Directora Dra. Marina Castro y Co-director Ing. Agr. Luis Viega. Tesis de...| Biblioteca(s): INIA La Estanzuela. |
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| 4. | | BAO, L.; PÉREZ, O. (Ed.). El gorgojo acuático del arroz Montevideo (UY): INIA, 2012. 34 p. (Serie FPTA-INIA; 38) Proyecto FPTA 228: Estudios biológicos de Oryzophagus oryzae como base para la implementación de buenas prácticas de manejo del cultivo de arroz en diferentes zonas de Uruguay. Período de Ejecución: Dic. 2006-Mar. 2010.
EQUIPO DE TRABAJO:...| Biblioteca(s): INIA La Estanzuela; INIA Las Brujas; INIA Tacuarembó. |
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| 13. | | BAO, L.; PÉREZ, O.; CANTOU, G.; ROEL, A.; BENTANCOURT, C. Efecto del momento de inundación sobre la abundancia del gorgojo acuático del arroz Oryzophagus oryzae. Paso Farías, Artigas, 27 de agosto, 2009; Tacuarembó, 28 de agosto, 2009. ln: INIA TACUAREMBO. PROGRAMA NACIONAL DE INVESTIGACIÓN EN PRODUCCIÓN DE ARROZ. Presentación resultados experimentales de arroz Zafra 2008/2009. Tacuarembó (Uruguay): INIA, 2009. p. 69-70 (INIA Serie Actividades de Difusión ; 585)| Biblioteca(s): INIA Tacuarembó. |
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| 15. | | PÉREZ, O.; VIEGA, L.; GUTIÉRREZ, L.; CASTRO, M. Post-anthesis water deficit in spring wheat: effects on yield components and relative water content. In: SEMINARIO INTERNACIONAL DE TRIGO, 2014, La Estanzuela, Colonia, UY. GERMÁN, S., et al. (Org.). 1914-2014, un siglo de mejoramiento de trigo en La Estanzuela: un valioso legado para el futuro: resúmenes; posters. La Estanzuela, Colonia, UY: INIA, 2014. p. 41.| Biblioteca(s): INIA La Estanzuela. |
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| 16. | | PÉREZ, O.; VIEGA, L.; GUTIERREZ, L.; CASTRO, M. Post-anthesis water deficit in spring wheat: effects on yield components and relative water content. [Poster]. In: German, S.; Quincke, M.; Vázquez, D.; Castro, M.; Pereyra, S.; Silva, P.; García, A. (Eds.). Seminario Internacional "1914-2014: Un siglo de mejoramiento de trigo en La Estanzuela". Montevideo (UY): INIA, 2018. P.130. (INIA Serie Técnica; 241).| Biblioteca(s): INIA La Estanzuela. |
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| 19. | | CASTRO, M.; HOFFMAN, E.; PÉREZ, O.; VIEGA, L. Variabilidad en la respuesta al estrés hídrico de cultivares de trigo en Uruguay. In: German, S.; Quincke, M.; Vázquez, D.; Castro, M.; Pereyra, S.; Silva, P.; García, A. (Eds.). Seminario Internacional "1914-2014: Un siglo de mejoramiento de trigo en La Estanzuela". Montevideo (UY): INIA, 2018. p. 78-86. (INIA Serie Técnica; 241).| Biblioteca(s): INIA La Estanzuela. |
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| 20. | | CASTRO, M.; HOFFMAN, E.; PÉREZ, O.; VIEGA, L. Variabilidad en la respuesta al estrés hídrico de cultivares de trigo.[Presentación oral]. In: SEMINARIO INTERNACIONAL DE TRIGO, 2014, La Estanzuela, Colonia, UY. GERMÁN, S., et al. (Org.). 1914-2014, un siglo de mejoramiento de trigo en La Estanzuela: un valioso legado para el futuro. presentaciones; resúmenes. La Estanzuela, Colonia, UY: INIA, 2014.| Biblioteca(s): INIA La Estanzuela. |
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| Registros recuperados : 34 | |
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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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