This title appears in the Scientific Report :
2014
Please use the identifier:
http://dx.doi.org/10.3390/agriculture4020147 in citations.
Please use the identifier: http://hdl.handle.net/2128/11521 in citations.
Non-Invasive Spectral Phenotyping Methods can Improve and Accelerate Cercospora Disease Scoring in Sugar Beet Breeding
Non-Invasive Spectral Phenotyping Methods can Improve and Accelerate Cercospora Disease Scoring in Sugar Beet Breeding
Breeding for Cercospora resistant sugar beet cultivars requires field experiments for testing resistance levels of candidate genotypes in conditions that are close to agricultural cultivation. Non-invasive spectral phenotyping methods can support and accelerate resistance rating and thereby speed up...
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Personal Name(s): | Jansen, Marcus (Corresponding Author) |
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Bergsträsser, Sergej / Schmittgen, Simone / Müller-Linow, Mark / Rascher, Uwe | |
Contributing Institute: |
Pflanzenwissenschaften; IBG-2 |
Published in: | Agriculture, 4 (2014) 2, S. 147 - 158 |
Imprint: |
Basel
MDPI AG
2014
|
DOI: |
10.3390/agriculture4020147 |
Document Type: |
Journal Article |
Research Program: |
Plant Science |
Link: |
OpenAccess OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://hdl.handle.net/2128/11521 in citations.
Breeding for Cercospora resistant sugar beet cultivars requires field experiments for testing resistance levels of candidate genotypes in conditions that are close to agricultural cultivation. Non-invasive spectral phenotyping methods can support and accelerate resistance rating and thereby speed up breeding process. In a case study, experimental field plots with strongly infected beet genotypes of different resistance levels were measured with two different spectrometers. Vegetation indices were calculated from measured wavelength signature to determine leaf physiological status, e.g., greenness with the Normalized Differenced Vegetation Index (NDVI), leaf water content with the Leaf Water Index (LWI) and Cercospora disease severity with the Cercospora Leaf Spot Index (CLSI). Indices values correlated significantly with visually scored disease severity, thus connecting the classical breeders’ scoring approach with advanced non-invasive technology. |