Literature DB >> 26506458

Improvement of Lesion Phenotyping in Cercospora beticola-Sugar Beet Interaction by Hyperspectral Imaging.

Marlene Leucker1, Anne-Katrin Mahlein1, Ulrike Steiner1, Erich-Christian Oerke1.   

Abstract

Cercospora leaf spot (CLS) caused by Cercospora beticola is the most destructive leaf disease of sugar beet and may cause high losses in yield and quality. Breeding and cultivation of disease-resistant varieties is an important strategy to control this economically relevant plant disease. Reliable and robust resistance parameters are required to promote breeding progress. CLS lesions on five different sugar beet genotypes incubated under controlled conditions were analyzed for phenotypic differences related to field resistance to C. beticola. Lesions of CLS were rated by classical quantitative and qualitative methods in combination with noninvasive hyperspectral imaging. Calculating the ratio of lesion center to lesion margin, four CLS phenotypes were identified that vary in size and spatial composition. Lesions could be differentiated into subareas based on their spectral characteristics in the range of 400 to 900 nm. Sugar beet genotypes with lower disease severity typically had lesions with smaller centers compared with highly susceptible genotypes. Accordingly, the number of conidia per diseased leaf area on resistant plants was lower. The assessment of lesion phenotypes by hyperspectral imaging with regard to sporulation may be an appropriate method to identify subtle differences in disease resistance. The spectral and spatial analysis of the lesions has the potential to improve the screening process in breeding for CLS resistance.

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Year:  2015        PMID: 26506458     DOI: 10.1094/PHYTO-04-15-0100-R

Source DB:  PubMed          Journal:  Phytopathology        ISSN: 0031-949X            Impact factor:   4.025


  12 in total

1.  Digital Imaging Combined with Genome-Wide Association Mapping Links Loci to Plant-Pathogen Interaction Traits.

Authors:  Rachel F Fordyce; Nicole E Soltis; Celine Caseys; Raoni Gwinner; Jason A Corwin; Susana Atwell; Daniel Copeland; Julie Feusier; Anushriya Subedy; Robert Eshbaugh; Daniel J Kliebenstein
Journal:  Plant Physiol       Date:  2018-09-28       Impact factor: 8.340

2.  Quantitative, Image-Based Phenotyping Methods Provide Insight into Spatial and Temporal Dimensions of Plant Disease.

Authors:  Andrew M Mutka; Sarah J Fentress; Joel W Sher; Jeffrey C Berry; Chelsea Pretz; Dmitri A Nusinow; Rebecca Bart
Journal:  Plant Physiol       Date:  2016-07-21       Impact factor: 8.340

3.  Technical workflows for hyperspectral plant image assessment and processing on the greenhouse and laboratory scale.

Authors:  Stefan Paulus; Anne-Katrin Mahlein
Journal:  Gigascience       Date:  2020-08-01       Impact factor: 6.524

4.  Early Detection of Magnaporthe oryzae-Infected Barley Leaves and Lesion Visualization Based on Hyperspectral Imaging.

Authors:  Rui-Qing Zhou; Juan-Juan Jin; Qing-Mian Li; Zhen-Zhu Su; Xin-Jie Yu; Yu Tang; Shao-Ming Luo; Yong He; Xiao-Li Li
Journal:  Front Plant Sci       Date:  2019-01-15       Impact factor: 5.753

5.  Plant Phenotyping using Probabilistic Topic Models: Uncovering the Hyperspectral Language of Plants.

Authors:  Mirwaes Wahabzada; Anne-Katrin Mahlein; Christian Bauckhage; Ulrike Steiner; Erich-Christian Oerke; Kristian Kersting
Journal:  Sci Rep       Date:  2016-03-09       Impact factor: 4.379

6.  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

7.  Quantitative assessment of disease severity and rating of barley cultivars based on hyperspectral imaging in a non-invasive, automated phenotyping platform.

Authors:  Stefan Thomas; Jan Behmann; Angelina Steier; Thorsten Kraska; Onno Muller; Uwe Rascher; Anne-Katrin Mahlein
Journal:  Plant Methods       Date:  2018-06-08       Impact factor: 4.993

8.  Sensory assessment of Cercospora beticola sporulation for phenotyping the partial disease resistance of sugar beet genotypes.

Authors:  Erich-Christian Oerke; Marlene Leucker; Ulrike Steiner
Journal:  Plant Methods       Date:  2019-11-16       Impact factor: 4.993

9.  Reflectance Spectroscopy for Non-Destructive Measurement and Genetic Analysis of Amounts and Types of Epicuticular Waxes on Onion Leaves.

Authors:  Eduardo D Munaiz; Philip A Townsend; Michael J Havey
Journal:  Molecules       Date:  2020-07-29       Impact factor: 4.927

Review 10.  Status and advances in mining for blackleg (Leptosphaeria maculans) quantitative resistance (QR) in oilseed rape (Brassica napus).

Authors:  Junrey Amas; Robyn Anderson; David Edwards; Wallace Cowling; Jacqueline Batley
Journal:  Theor Appl Genet       Date:  2021-06-09       Impact factor: 5.699

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