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Image-based phenotyping of plant disease symptoms

Plant diseases cause significant reductions in agricultural productivity worldwide. Disease symptoms have deleterious effects on the growth and development of crop plants, limiting yields and making agricultural products unfit for consumption. For many plant–pathogen systems, we lack knowledge of th...

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Detalles Bibliográficos
Autores principales: Mutka, Andrew M., Bart, Rebecca S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4283508/
https://www.ncbi.nlm.nih.gov/pubmed/25601871
http://dx.doi.org/10.3389/fpls.2014.00734
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author Mutka, Andrew M.
Bart, Rebecca S.
author_facet Mutka, Andrew M.
Bart, Rebecca S.
author_sort Mutka, Andrew M.
collection PubMed
description Plant diseases cause significant reductions in agricultural productivity worldwide. Disease symptoms have deleterious effects on the growth and development of crop plants, limiting yields and making agricultural products unfit for consumption. For many plant–pathogen systems, we lack knowledge of the physiological mechanisms that link pathogen infection and the production of disease symptoms in the host. A variety of quantitative high-throughput image-based methods for phenotyping plant growth and development are currently being developed. These methods range from detailed analysis of a single plant over time to broad assessment of the crop canopy for thousands of plants in a field and employ a wide variety of imaging technologies. Application of these methods to the study of plant disease offers the ability to study quantitatively how host physiology is altered by pathogen infection. These approaches have the potential to provide insight into the physiological mechanisms underlying disease symptom development. Furthermore, imaging techniques that detect the electromagnetic spectrum outside of visible light allow us to quantify disease symptoms that are not visible by eye, increasing the range of symptoms we can observe and potentially allowing for earlier and more thorough symptom detection. In this review, we summarize current progress in plant disease phenotyping and suggest future directions that will accelerate the development of resistant crop varieties.
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spelling pubmed-42835082015-01-19 Image-based phenotyping of plant disease symptoms Mutka, Andrew M. Bart, Rebecca S. Front Plant Sci Plant Science Plant diseases cause significant reductions in agricultural productivity worldwide. Disease symptoms have deleterious effects on the growth and development of crop plants, limiting yields and making agricultural products unfit for consumption. For many plant–pathogen systems, we lack knowledge of the physiological mechanisms that link pathogen infection and the production of disease symptoms in the host. A variety of quantitative high-throughput image-based methods for phenotyping plant growth and development are currently being developed. These methods range from detailed analysis of a single plant over time to broad assessment of the crop canopy for thousands of plants in a field and employ a wide variety of imaging technologies. Application of these methods to the study of plant disease offers the ability to study quantitatively how host physiology is altered by pathogen infection. These approaches have the potential to provide insight into the physiological mechanisms underlying disease symptom development. Furthermore, imaging techniques that detect the electromagnetic spectrum outside of visible light allow us to quantify disease symptoms that are not visible by eye, increasing the range of symptoms we can observe and potentially allowing for earlier and more thorough symptom detection. In this review, we summarize current progress in plant disease phenotyping and suggest future directions that will accelerate the development of resistant crop varieties. Frontiers Media S.A. 2015-01-05 /pmc/articles/PMC4283508/ /pubmed/25601871 http://dx.doi.org/10.3389/fpls.2014.00734 Text en Copyright © 2015 Mutka and Bart. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Mutka, Andrew M.
Bart, Rebecca S.
Image-based phenotyping of plant disease symptoms
title Image-based phenotyping of plant disease symptoms
title_full Image-based phenotyping of plant disease symptoms
title_fullStr Image-based phenotyping of plant disease symptoms
title_full_unstemmed Image-based phenotyping of plant disease symptoms
title_short Image-based phenotyping of plant disease symptoms
title_sort image-based phenotyping of plant disease symptoms
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4283508/
https://www.ncbi.nlm.nih.gov/pubmed/25601871
http://dx.doi.org/10.3389/fpls.2014.00734
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