Literature DB >> 33489007

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

Nikhil Kumar Singh1, Anik Dutta1,2, Guido Puccetti1,3, Daniel Croll1.   

Abstract

Pathogens and pests are one of the major threats to agricultural productivity worldwide. For decades, targeted resistance breeding was used to create crop cultivars that resist pathogens and environmental stress while retaining yields. The often decade-long process of crossing, selection, and field trials to create a new cultivar is challenged by the rapid rise of pathogens overcoming resistance. Similarly, antimicrobial compounds can rapidly lose efficacy due to resistance evolution. Here, we review three major areas where computational, imaging and experimental approaches are revolutionizing the management of pathogen damage on crops. Recognizing and scoring plant diseases have dramatically improved through high-throughput imaging techniques applicable both under well-controlled greenhouse conditions and directly in the field. However, computer vision of complex disease phenotypes will require significant improvements. In parallel, experimental setups similar to high-throughput drug discovery screens make it possible to screen thousands of pathogen strains for variation in resistance and other relevant phenotypic traits. Confocal microscopy and fluorescence can capture rich phenotypic information across pathogen genotypes. Through genome-wide association mapping approaches, phenotypic data helps to unravel the genetic architecture of stress- and virulence-related traits accelerating resistance breeding. Finally, joint, large-scale screenings of trait variation in crops and pathogens can yield fundamental insights into how pathogens face trade-offs in the adaptation to resistant crop varieties. We discuss how future implementations of such innovative approaches in breeding and pathogen screening can lead to more durable disease control.
© 2020 The Author(s).

Entities:  

Keywords:  Agriculture; Genome-wide association mapping; High-throughput phenotyping; Image analysis; Plant pathogens; Sustainability

Year:  2020        PMID: 33489007      PMCID: PMC7787954          DOI: 10.1016/j.csbj.2020.12.018

Source DB:  PubMed          Journal:  Comput Struct Biotechnol J        ISSN: 2001-0370            Impact factor:   7.271


  142 in total

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5.  The complex genomic basis of rapid convergent adaptation to pesticides across continents in a fungal plant pathogen.

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Review 7.  Vector-Borne Bacterial Plant Pathogens: Interactions with Hemipteran Insects and Plants.

Authors:  Laura M Perilla-Henao; Clare L Casteel
Journal:  Front Plant Sci       Date:  2016-08-09       Impact factor: 5.753

8.  Using Sensors and Unmanned Aircraft Systems for High-Throughput Phenotyping of Biomass in Perennial Ryegrass Breeding Trials.

Authors:  Junping Wang; Pieter Badenhorst; Andrew Phelan; Luke Pembleton; Fan Shi; Noel Cogan; German Spangenberg; Kevin Smith
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9.  Multivariate simulation framework reveals performance of multi-trait GWAS methods.

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10.  High throughput procedure utilising chlorophyll fluorescence imaging to phenotype dynamic photosynthesis and photoprotection in leaves under controlled gaseous conditions.

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