Non-Invasive Spectral Phenotyping Methods can Improve and Accelerate Cercospora Disease Scoring in Sugar Beet Breeding

  title={Non-Invasive Spectral Phenotyping Methods can Improve and Accelerate Cercospora Disease Scoring in Sugar Beet Breeding},
  author={M. Y. Jansen and Sergej Bergstr{\"a}sser and Simone Schmittgen and Mark M{\"u}ller-Linow and Uwe Rascher},
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… CONTINUE READING
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Publications referenced by this paper.
Showing 1-10 of 26 references

Development of spectral indices for detecting and identifying plant diseases

  • A. K. Mahlein, T. Rumpf, +4 authors E. C. Oerke
  • Remote Sens. Environ
  • 2013
Highly Influential
11 Excerpts

Spectral indices for estimating photosynthetic pigment concentrations: A test using senescent tree leaves

  • G. A. Blackburn
  • Int. J. Remote Sens
  • 1998
Highly Influential
3 Excerpts

Non-invasive phenotyping methodologies enable the accurate characterization of growth and performance of shoots and roots

  • M. Jansen, F. Pinto, +6 authors U. Schurr
  • In Genomics of Plant Genetic Resources;
  • 2014
2 Excerpts

Non-invasive approaches for phenotyping of enhanced performance traits in bean

  • U. Rascher, S. Blossfeld, +7 authors R Metzner
  • Funct. Plant Biol
  • 2011
1 Excerpt

Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance

  • T. Rumpf, A. K. Mahlein, U. Steiner, E. C. Oerke, H. W. Dehne, L. Pluemer
  • Comput. Electron. Agric
  • 2010
1 Excerpt

Relations of remote sensing leaf water indices to leaf water thickness in cowpea, bean, and sugarbeet plants

  • H.-D. Seelig, A. Hoehn, +5 authors III
  • Remote Sens. Environ
  • 2008
1 Excerpt

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