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  1. Article: Improving Common Bacterial Blight Phenotyping by Using Rub Inoculation and Machine Learning: Cheaper, Better, Faster, Stronger.

    Foucher, Justine / Ruh, Mylène / Briand, Martial / Préveaux, Anne / Barbazange, Florian / Boureau, Tristan / Jacques, Marie-Agnès / Chen, Nicolas W G

    Phytopathology

    2022  Volume 112, Issue 3, Page(s) 691–699

    Abstract: Accurate assessment of plant symptoms plays a key role for measuring the impact of pathogens during plant-pathogen interaction. Common bacterial blight caused ... ...

    Abstract Accurate assessment of plant symptoms plays a key role for measuring the impact of pathogens during plant-pathogen interaction. Common bacterial blight caused by
    MeSH term(s) Bacteria ; Fabaceae ; Machine Learning ; Plant Diseases/microbiology ; Virulence
    Language English
    Publishing date 2022-03-02
    Publishing country United States
    Document type Journal Article
    ZDB-ID 208889-7
    ISSN 1943-7684 ; 0031-949X
    ISSN (online) 1943-7684
    ISSN 0031-949X
    DOI 10.1094/PHYTO-04-21-0129-R
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: Improving Common Bacterial Blight Phenotyping by Using Rub Inoculation and Machine Learning: Cheaper, Better, Faster, Stronger

    Foucher, Justine / Ruh, Mylène / Briand, Martial / Préveaux, Anne / Barbazange, Florian / Boureau, Tristan / Jacques, Marie-Agnès / Chen, Nicolas W. G.

    Phytopathology. 2022 Mar., v. 112, no. 3

    2022  

    Abstract: Accurate assessment of plant symptoms plays a key role for measuring the impact of pathogens during plant−pathogen interaction. Common bacterial blight caused by Xanthomonas phaseoli pv. phaseoli and X. citri pv. fuscans is a major threat to common bean. ...

    Abstract Accurate assessment of plant symptoms plays a key role for measuring the impact of pathogens during plant−pathogen interaction. Common bacterial blight caused by Xanthomonas phaseoli pv. phaseoli and X. citri pv. fuscans is a major threat to common bean. The pathogenicity of these bacteria is variable among strains and depends mainly on a type III secretion system and associated type III effectors such as transcription activator-like effectors. Because the impact of a single gene is often small and difficult to detect, a discriminating methodology is required to distinguish the slight phenotype changes induced during the progression of the disease. Here, we compared two different inoculation and symptom assessment methods for their ability to distinguish two tal mutants from their corresponding wild-type strains. Interestingly, rub inoculation of the first leaves combined with symptom assessment by machine learning-based imaging allowed significant distinction between wild-type and mutant strains. By contrast, dip inoculation of first-trifoliate leaves combined with chlorophyll fluorescence imaging did not differentiate the strains. Furthermore, the new method developed here led to the miniaturization of pathogenicity tests and significant time savings.
    Keywords Xanthomonas campestris pv. phaseoli ; beans ; blight ; chlorophyll ; disease progression ; genes ; host-pathogen relationships ; mutants ; pathogenicity ; phenotype ; plant pathology
    Language English
    Dates of publication 2022-03
    Size p. 691-699.
    Publishing place The American Phytopathological Society
    Document type Article
    ZDB-ID 208889-7
    ISSN 1943-7684 ; 0031-949X
    ISSN (online) 1943-7684
    ISSN 0031-949X
    DOI 10.1094/PHYTO-04-21-0129-R
    Database NAL-Catalogue (AGRICOLA)

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