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| 322. |  | ESTEVES, P.; HERNANDEZ, L.; CASTILLO, A.; DALLA RIZZA, M.; QUINCKE, M. Tecnologías para el desarrollo de líneas recombinantes de trigo. MV 20 - COMUNICACIONES LIBRES - MV. MEJORAMIENTO VEGETAL In: JOURNAL OF BASIC & APPLIED GENETICS, 2016, Vol.27, Iss. 1 (Supp.). XVI LATIN AMERICAN CONGRESS OF GENETICS, IV CONGRESS OF THE URUGUAYAN SOCIETY OF GENETICS, XLIX ANNUAL MEETING OF THE GENETICS SOCIETY OF CHILE, XLV ARGENTINE CONGRESS OF GENETICS, 9-12 October 2016. PROCEEDINGS. Montevideo (Uruguay): SAG, 2016. p. 290| Biblioteca(s): INIA Las Brujas. |
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| 323. | | 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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| 325. | | KOLMER, J.A.; ORDONEZ, M.E.; GERMAN, S.; MORGUNOV, A.; PRETORIUS,Z.A.; VISSER, B.; ANIKSTER, Y.; ACEVEDO, M. Multilocus genotypes of the wheat leaf rust fungus Puccinia triticina in worldwide regions indicate past and current long distance migration. Phytopathology, 2019,Apr 1: 10.1094/PHYTO-10-18-0411-R. [Epub ahead of print]. Article history: Published Online: 1 Apr 2019 .| Biblioteca(s): INIA La Estanzuela. |
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| 327. | | INIA (INSTITUTO NACIONAL DE INVESTIGACIÓN AGROPECUARIA).; INASE (INSTITUTO NACIONAL DE SEMILLAS). Resultados experimentales de la evaluación nacional de cultivares de trigos, cebadas , colzas y triticale doble propósito de los 3 últimos años: período 2006-2007-2008. La Estanzuela (UY): INIA ; INASE, 2009. 94 p. (Resultados experimentales; 9) Equipos de trabajo: INIA: S. Ceretta, M. Castro, M. Sastre, L. Benedetto, B. Castro, V. Ibáñez, M. Díaz, S. Germán, S. Pereyra, S. Stewart, A. Ríos, D. Vázquez, E. Restaino, A. Vergara. INASE: C. Gómez Etchebarne, M. Ibarra, J. Machado,...| Biblioteca(s): INIA Treinta y Tres. |
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| 329. | | SAINT PIERRE, C.; GHANEM, M.E.; BEN M'BAREK, S.; AZZIMONTI, G.; PEREYRA, S.; MARZA, F.; YAHYAOUI, A.; SINGH, P.; BAUM, M.; BRAUN, H. Global network for precision wheat phenotypin.[Resumen]. In: Latin-American Conference on Plant Phenotyping and Phenomics for Plant Breeding, 2, 2017, São Carlos, SP. Proceedings... São Carlos: Embrapa Instrumentação, 2017. p. 77.| Biblioteca(s): INIA La Estanzuela. |
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| 332. | | CONDÓN, F. Recursos genéticos. In: PROYECTO REGIONAL TRIGO; DÍAZ, R. (Coord.). Principales logros y avances: [países del Cono Sur]. Montevideo, UY: INIA/IICA/PROCISUR/INIA ES/CIMMYT, 2010. p. 45-47.| Biblioteca(s): INIA La Estanzuela. |
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| 339. | | CLAVIJO, F.; CURLAND, R.D.; ROMAN-REYNA, V.; DILL-MACKY, R.; PEREYRA, S.; SIRI, M.I. Caracterización de especies del género Xanthomonas patógenas de trigo en Uruguay. In: Sociedad Uruguaya de Fitopatología Jornada Uruguaya de Fitopatología, 6., Jornada Uruguaya de Protección Vegetal, 4., 21-22 octubre, 2021, Montevideo, Uruguay. Libro de resúmenes. Montevideo (UY): Sociedad Uruguay de Fitopatología (SUFIT), 2021. p. 31. Financiamiento: Proyecto de Investigación Fundamental Fondo Clemente Estable (ANII); Programa Grupos de Investigación I+D de CSIC.| Biblioteca(s): INIA Treinta y Tres. |
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Registro completo
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Biblioteca (s) : |
INIA La Estanzuela. |
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Fecha actual : |
06/06/2019 |
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Actualizado : |
14/08/2019 |
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Tipo de producción científica : |
Poster |
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Autor : |
PEZARD, J.; FERNANDEZ, P.; PEREYRA, S.; QUINCKE, M.; SAINT-PIERRE, C.; SINGH, P.K.; AZZIMONTI, G. |
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Afiliación : |
AgroParisTech, Paris, France.; PETER DENNIS FERNANDEZ GRAF, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; SILVIA ANTONIA PEREYRA CORREA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARTIN CONRADO QUINCKE WALDEN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; CAROLINA SAINT-PIERRE, International Maize and Wheat Improvement Center (CIMMYT), El Batán, México.; PAWAN K. SINGH, International Maize and Wheat Improvement Center (CIMMYT), El Batán, México.; GUSTAVO AZZIMONTI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Adapting automated image analysis to breeding programs constraints for the characterization of the resistance to leaf rust and other diseases. |
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Fecha de publicación : |
2018 |
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Fuente / Imprenta : |
In: Proceedings of the International Cereal Rusts and Powdery Mildew Conference (ICRPMC): Skukuza, South Africa, 23-26 september 2018. |
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Idioma : |
Inglés |
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Contenido : |
Description:
Disease phenotyping methods used in breeding programs to characterize the level of resistance of breeding materials usually consist on visual scores (VS) of disease symptoms determined in field trials. VS are considered as high time-consuming and rely on experienced operators. Nevertheless, up to date, it is the only method that has an efficient time/effort relationship considering breeding constrains. The objective was to develop a phenotyping methodology based on automated image analysis (AIA) for leaf diseases, adapted to the constraints of a breeding program. 410 wheat lines from 5 different breeding programs were sowed in three field trials, as part of the materials tested in 2017 at the multi-disease phenotyping platform INIA-CIMMYT, Uruguay. One trial was inoculated with Puccinia triticina isolates the second with Zymoseptoria tritici isolates and the third had natural infection of P. striiformis f. sp. tritici. Six flag leaves per genotype were cut and scanned with a flatbed scanner. A script was developed in the ImageJ software to autonomously recognize and measure the leaf diseased surface. Disease recognition and surface measurements were based on the different threshold color patterns of each disease. Host response was also determined for leaf and stripe rust, measuring the ratio of necrosis-chlorosis/sporulation area of lesions. AIA recognized the different diseases (error<5%). The diseased surfaces obtained by AIA correlated significantly and positively with the VS measured for the three diseases. Host responses estimated by AIA were the same as determined visually, (error<5%). AIA was fast, a mean of 214 leaves/hour analyzed, taking into account the adjustments of color thresholds and the validation of AIA. However, the time to prepare and scan the leaves was higher than the VS: a mean of 205 lines could be scanned per person/day while a mean of 402 lines per person/day could be visually scored. Adjustments to the scan methodology are being carried out to enhance the speed at this step. Nevertheless, AIA can be a performing alternative to VS in limited panels or mapping populations that undergo QTL analysis, where precise measurements of quantitative resistance variables are required to detect QTL with moderate effects and QTL interactions. MenosDescription:
Disease phenotyping methods used in breeding programs to characterize the level of resistance of breeding materials usually consist on visual scores (VS) of disease symptoms determined in field trials. VS are considered as high time-consuming and rely on experienced operators. Nevertheless, up to date, it is the only method that has an efficient time/effort relationship considering breeding constrains. The objective was to develop a phenotyping methodology based on automated image analysis (AIA) for leaf diseases, adapted to the constraints of a breeding program. 410 wheat lines from 5 different breeding programs were sowed in three field trials, as part of the materials tested in 2017 at the multi-disease phenotyping platform INIA-CIMMYT, Uruguay. One trial was inoculated with Puccinia triticina isolates the second with Zymoseptoria tritici isolates and the third had natural infection of P. striiformis f. sp. tritici. Six flag leaves per genotype were cut and scanned with a flatbed scanner. A script was developed in the ImageJ software to autonomously recognize and measure the leaf diseased surface. Disease recognition and surface measurements were based on the different threshold color patterns of each disease. Host response was also determined for leaf and stripe rust, measuring the ratio of necrosis-chlorosis/sporulation area of lesions. AIA recognized the different diseases (error<5%). The diseased surfaces obtained by AIA correlated significantly and posit... Presentar Todo |
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Palabras claves : |
INIA-CIMMYT; PLATAFORMA FENOTIPADO DE TRIGO; RUST DISEASE; WHEAT. |
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Thesagro : |
ENFERMEDADES DE LAS PLANTAS; Trigo. |
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Asunto categoría : |
H20 Enfermedades de las plantas |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/13100/1/PosterPezardetalICRPMC2018.pdf
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Marc : |
LEADER 03183nam a2200253 a 4500
001 1059824
005 2019-08-14
008 2018 bl uuuu u00u1 u #d
100 1 $aPEZARD, J.
245 $aAdapting automated image analysis to breeding programs constraints for the characterization of the resistance to leaf rust and other diseases.$h[electronic resource]
260 $aIn: Proceedings of the International Cereal Rusts and Powdery Mildew Conference (ICRPMC): Skukuza, South Africa, 23-26 september 2018.$c2018
520 $aDescription: Disease phenotyping methods used in breeding programs to characterize the level of resistance of breeding materials usually consist on visual scores (VS) of disease symptoms determined in field trials. VS are considered as high time-consuming and rely on experienced operators. Nevertheless, up to date, it is the only method that has an efficient time/effort relationship considering breeding constrains. The objective was to develop a phenotyping methodology based on automated image analysis (AIA) for leaf diseases, adapted to the constraints of a breeding program. 410 wheat lines from 5 different breeding programs were sowed in three field trials, as part of the materials tested in 2017 at the multi-disease phenotyping platform INIA-CIMMYT, Uruguay. One trial was inoculated with Puccinia triticina isolates the second with Zymoseptoria tritici isolates and the third had natural infection of P. striiformis f. sp. tritici. Six flag leaves per genotype were cut and scanned with a flatbed scanner. A script was developed in the ImageJ software to autonomously recognize and measure the leaf diseased surface. Disease recognition and surface measurements were based on the different threshold color patterns of each disease. Host response was also determined for leaf and stripe rust, measuring the ratio of necrosis-chlorosis/sporulation area of lesions. AIA recognized the different diseases (error<5%). The diseased surfaces obtained by AIA correlated significantly and positively with the VS measured for the three diseases. Host responses estimated by AIA were the same as determined visually, (error<5%). AIA was fast, a mean of 214 leaves/hour analyzed, taking into account the adjustments of color thresholds and the validation of AIA. However, the time to prepare and scan the leaves was higher than the VS: a mean of 205 lines could be scanned per person/day while a mean of 402 lines per person/day could be visually scored. Adjustments to the scan methodology are being carried out to enhance the speed at this step. Nevertheless, AIA can be a performing alternative to VS in limited panels or mapping populations that undergo QTL analysis, where precise measurements of quantitative resistance variables are required to detect QTL with moderate effects and QTL interactions.
650 $aENFERMEDADES DE LAS PLANTAS
650 $aTrigo
653 $aINIA-CIMMYT
653 $aPLATAFORMA FENOTIPADO DE TRIGO
653 $aRUST DISEASE
653 $aWHEAT
700 1 $aFERNANDEZ, P.
700 1 $aPEREYRA, S.
700 1 $aQUINCKE, M.
700 1 $aSAINT-PIERRE, C.
700 1 $aSINGH, P.K.
700 1 $aAZZIMONTI, G.
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