Literature DB >> 32480541

Hyperspectral imaging reveals the effect of sugar beet quantitative trait loci on Cercospora leaf spot resistance.

Marlene Leucker1, Mirwaes Wahabzada1, Kristian Kersting2, Madlaina Peter3, Werner Beyer3, Ulrike Steiner1, Anne-Katrin Mahlein1, Erich-Christian Oerke1.   

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.

Entities:  

Year:  2016        PMID: 32480541     DOI: 10.1071/FP16121

Source DB:  PubMed          Journal:  Funct Plant Biol        ISSN: 1445-4416            Impact factor:   3.101


  3 in total

Review 1.  Tackling microbial threats in agriculture with integrative imaging and computational approaches.

Authors:  Nikhil Kumar Singh; Anik Dutta; Guido Puccetti; Daniel Croll
Journal:  Comput Struct Biotechnol J       Date:  2020-12-29       Impact factor: 7.271

Review 2.  Understanding the ramifications of quantitative ordinal scales on accuracy of estimates of disease severity and data analysis in plant pathology.

Authors:  Kuo-Szu Chiang; Clive H Bock
Journal:  Trop Plant Pathol       Date:  2021-07-13       Impact factor: 2.404

3.  Improved classification accuracy of powdery mildew infection levels of wine grapes by spatial-spectral analysis of hyperspectral images.

Authors:  Uwe Knauer; Andrea Matros; Tijana Petrovic; Timothy Zanker; Eileen S Scott; Udo Seiffert
Journal:  Plant Methods       Date:  2017-06-15       Impact factor: 4.993

  3 in total

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