Hyperspectral imaging reveals the effect of sugar beet quantitative trait loci on Cercospora leaf spot resistance
Marlene Leucker A D , Mirwaes Wahabzada A , Kristian Kersting B , Madlaina Peter C , Werner Beyer C , Ulrike Steiner A , Anne-Katrin Mahlein A and Erich-Christian Oerke A
+ Author Affiliations
- Author Affiliations
A Institute for Crop Science and Resource Conservation (INRES) – Phytomedicine, University of Bonn, Meckenheimer Allee 166a, 53115 Bonn, Germany.
B Department of Computer Science, TU Dortmund University, Otto-Hahn-Str. 14, 44227 Dortmund, Germany.
C KWS SAAT SE, Grimsehlstrasse 31, 37555 Einbeck, Germany.
D Corresponding author. Email: mleucker@uni-bonn.de
Functional Plant Biology 44(1) 1-9 https://doi.org/10.1071/FP16121
Submitted: 31 March 2016 Accepted: 2 August 2016 Published: 14 September 2016
Abstract
The quantitative resistance of sugar beet (Beta vulgaris L.) against Cercospora leaf spot (CLS) caused by Cercospora beticola (Sacc.) was characterised by hyperspectral imaging. Two closely related inbred lines, differing in two quantitative trait loci (QTL), which made a difference in disease severity of 1.1–1.7 on the standard scoring scale (1–9), were investigated under controlled conditions. The temporal and spatial development of CLS lesions on the two genotypes were monitored using a hyperspectral microscope. The lesion development on the QTL-carrying, resistant genotype was characterised by a fast and abrupt change in spectral reflectance, whereas it was slower and ultimately more severe on the genotype lacking the QTL. An efficient approach for clustering of hyperspectral signatures was adapted in order to reveal resistance characteristics automatically. The presented method allowed a fast and reliable differentiation of CLS dynamics and lesion composition providing a promising tool to improve resistance breeding by objective and precise plant phenotyping.
Additional keywords: hyperspectral reflectance, proximal sensing, QTL, quantitative disease resistance, spherical k-means clustering, spectral phenotyping, spectral vegetation indices.
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