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| Registros recuperados : 11 | |
| 6. |  | CARDOZO, G.; REYNO, R.; AYALA, W.; CUADRO, R.; FERNANDEZ, P.; BERETTA, A.; JAURENA, M.; LATTANZI, F. Is phosphorus a limiting factor for the productivity of Campos grasslands? In: Symposium of the European Grassland Federeation, 19, 2017, Alghero, Italy. Porqueddu, C.; Franca, A.; Lombardi, G.; Molle, G.; Peratoner, G.; Hopkins, A., eds. Grassland resources for extensive farming systems in marginal lands: major drivers and future scenarios. Alghero, Italy : ISPAAM, 2017 p. 302-304. (Grassland Science in Europe; 22)| Biblioteca(s): INIA Tacuarembó; INIA Treinta y Tres. |
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| 9. | | Navarro, G.; Rovella, P.; Beltrami, M.; Silva, H.; Silingauskas, I.; Aguerre, R.; Mujica, H.; Moya, E.; Fernández, P. Selección de cultivares precoces de caña de azucar para el Uruguay ln: Reunión Técnica, 2 : 1979 Nov 27-29 : Montevideo Resúmenes. Montevideo (Uruguay): Facultad de Agronomía, 1979. pC11| Biblioteca(s): INIA La Estanzuela; INIA Las Brujas; INIA Tacuarembó. |
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| 10. | | BOGGIANO, P.; ZANONIANI, R.; CADENAZZI, M.; GIMÉNEZ, L.; FORMOSO, D.; AGUIRRE, S.; IRAZABAL, N.; OTEGUI, I.; ARCE, M.; FERNÁNDEZ, P.A; RICCETTO, S. Evaluación de la respuesta al riego suplementario de gramíneas perennes durante el período estival. En: Giménez, L. (ed.). Riego suplementario en cultivos y pasturas. Montevideo: INIA Uruguay. 2014. p. 29-43 (Serie FPTA-INIA; 55) Proyecto FPTA 261 - Respuesta física y económica al riego suplementario en cultivos y pasturas y desarrollo de la tecnología de riego por superficie en siembra directa| Biblioteca(s): INIA Treinta y Tres. |
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| 11. | | Heinz, R.; Paniego, N.; Guillin, E.; López Bilbao, M.; Fernández, P.; Fernández, L.; Talia, P.; Nishimakamasu, V.; Rivarola, M.; Parodi, B.; Gilli, J.; Rocco, M.; Poggio, L.; Hopp, H.E. Avances biotecnológicos en girasol y soja Idia XXI: revista de información sobre investigación y desarrollo agropecuario, 2002, v. 2, no. 3, p. 96-101| Biblioteca(s): INIA Tacuarembó. |
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| Registros recuperados : 11 | |
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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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