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Hyperspectral phenotyping on the microscopic scale: towards automated characterization of plant-pathogen interactions

Overview of attention for article published in Plant Methods, April 2015
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Title
Hyperspectral phenotyping on the microscopic scale: towards automated characterization of plant-pathogen interactions
Published in
Plant Methods, April 2015
DOI 10.1186/s13007-015-0073-7
Pubmed ID
Authors

Matheus Kuska, Mirwaes Wahabzada, Marlene Leucker, Heinz-Wilhelm Dehne, Kristian Kersting, Erich-Christian Oerke, Ulrike Steiner, Anne-Katrin Mahlein

Abstract

The detection and characterization of resistance reactions of crop plants against fungal pathogens are essential to select resistant genotypes. In breeding practice phenotyping of plant genotypes is realized by time consuming and expensive visual rating. In this context hyperspectral imaging (HSI) is a promising non-invasive sensor technique in order to accelerate and to automate classical phenotyping methods. A hyperspectral microscope was established to determine spectral changes on the leaf and cellular level of barley (Hordeum vulgare) during resistance reactions against powdery mildew (Blumeria graminis f.sp. hordei, isolate K1). Experiments were conducted with near isogenic barley lines of cv. Ingrid, including the susceptible wild type (WT), mildew locus a 12 (Mla12 based resistance) and the resistant mildew locus o 3 (mlo3 based resistance), respectively. The reflection of inoculated and non-inoculated leaves was recorded daily with a hyperspectral linescanner in the visual (400 - 700 nm) and near infrared (700 - 1000 nm) range 3 to 14 days after inoculation. Data analysis showed no significant differences in spectral signatures between non-inoculated genotypes. Barley leaves of the near-isogenic genotypes, inoculated with B. graminis f.sp. hordei differed in the spectral reflectance over time, respectively. The susceptible genotypes (WT, Mla12) showed an increase in reflectance in the visible range according to symptom development. However, the spectral signature of the resistant mlo-genotype did not show significant changes over the experimental period. In addition, a recent data driven approach for automated discovery of disease specific signatures, which is based on a new representation of the data using Simplex Volume Maximization (SiVM) was applied. The automated approach - evaluated in only a fraction of time revealed results similar to the time and labor intensive manually assessed hyperspectral signatures. The new representation determined by SiVM was also used to generate intuitive and easy to interpretable summaries, e.g. fingerprints or traces of hyperspectral dynamics of the different genotypes. With this HSI based and data driven phenotyping approach an evaluation of host-pathogen interactions over time and a discrimination of barley genotypes differing in susceptibility to powdery mildew is possible.

Mendeley readers

The data shown below were compiled from readership statistics for 163 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 1%
Australia 1 <1%
Brazil 1 <1%
Unknown 159 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 40 25%
Researcher 30 18%
Student > Master 25 15%
Student > Doctoral Student 11 7%
Student > Bachelor 8 5%
Other 23 14%
Unknown 26 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 74 45%
Environmental Science 13 8%
Computer Science 12 7%
Engineering 11 7%
Biochemistry, Genetics and Molecular Biology 6 4%
Other 11 7%
Unknown 36 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 May 2015.
All research outputs
#11,373,045
of 12,786,839 outputs
Outputs from Plant Methods
#500
of 517 outputs
Outputs of similar age
#188,204
of 227,389 outputs
Outputs of similar age from Plant Methods
#2
of 2 outputs
Altmetric has tracked 12,786,839 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 517 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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