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Advanced Diagnostic Approaches for Necrotrophic Fungal Pathogens of Temperate Legumes With a Focus on Botrytis spp.

Plant pathogens reduce global crop productivity by up to 40% per annum, causing enormous economic loss and potential environmental effects from chemical management practices. Thus, early diagnosis and quantitation of the causal pathogen species for accurate and timely disease control is crucial. Bot...

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Detalles Bibliográficos
Autores principales: Bilkiss, Marzia, Shiddiky, Muhammad J. A., Ford, Rebecca
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6702891/
https://www.ncbi.nlm.nih.gov/pubmed/31474966
http://dx.doi.org/10.3389/fmicb.2019.01889
Descripción
Sumario:Plant pathogens reduce global crop productivity by up to 40% per annum, causing enormous economic loss and potential environmental effects from chemical management practices. Thus, early diagnosis and quantitation of the causal pathogen species for accurate and timely disease control is crucial. Botrytis Gray Mold (BGM), caused by Botrytis cinerea and B. fabae, can seriously impact production of temperate grain legumes separately or within a complex. Accordingly, several immunogenic and molecular probe-type protocols have been developed for their diagnosis, but these have varying levels of species-specificity, sensitivity and consequent usefulness within the paddock. To substantially improve speed, accuracy and sensitivity, advanced nanoparticle-based biosensor approaches have been developed. These novel methods have made enormous impact toward disease diagnosis in the medical sciences and offer potential for transformational change within the field of plant pathology and disease management, with early and accurate diagnosis at the point-of-care in the field. Here we review several recently developed diagnostic tools that build on traditional approaches and are available for pathogen diagnosis, specifically for Botrytis spp. diagnostic applications. We then identify the specific gaps in knowledge and current limitations to these existing tools.