Rapid, automated detection of stem canker symptoms in woody perennials using artificial neural network analysis
Author(s)
Type
Journal Article
Abstract
Background
Pseudomonas syringae can cause stem necrosis and canker in a wide range of woody species including cherry, plum, peach, horse chestnut and ash. The detection and quantification of lesion progression over time in woody tissues is a key trait for breeders to select upon for resistance.
Results
In this study a general, rapid and reliable approach to lesion quantification using image recognition and an artificial neural network model was developed. This was applied to screen both the virulence of a range of P. syringae pathovars and the resistance of a set of cherry and plum accessions to bacterial canker. The method developed was more objective than scoring by eye and allowed the detection of putatively resistant plant material for further study.
Conclusions
Automated image analysis will facilitate rapid screening of material for resistance to bacterial and other phytopathogens, allowing more efficient selection and quantification of resistance responses.
Pseudomonas syringae can cause stem necrosis and canker in a wide range of woody species including cherry, plum, peach, horse chestnut and ash. The detection and quantification of lesion progression over time in woody tissues is a key trait for breeders to select upon for resistance.
Results
In this study a general, rapid and reliable approach to lesion quantification using image recognition and an artificial neural network model was developed. This was applied to screen both the virulence of a range of P. syringae pathovars and the resistance of a set of cherry and plum accessions to bacterial canker. The method developed was more objective than scoring by eye and allowed the detection of putatively resistant plant material for further study.
Conclusions
Automated image analysis will facilitate rapid screening of material for resistance to bacterial and other phytopathogens, allowing more efficient selection and quantification of resistance responses.
Date Issued
2015-12-24
Date Acceptance
2015-12-09
Citation
Plant Methods, 2015, 11
ISSN
1746-4811
Publisher
BioMed Central
Journal / Book Title
Plant Methods
Volume
11
Copyright Statement
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
License URL
Subjects
Science & Technology
Life Sciences & Biomedicine
Biochemical Research Methods
Plant Sciences
Biochemistry & Molecular Biology
Stem canker
Artificial neural network
Image analysis
DIGITAL IMAGE-ANALYSIS
SYRINGAE PV SYRINGAE
CHERRY PRUNUS-AVIUM
PSEUDOMONAS-SYRINGAE
BACTERIAL CANKER
SWEET CHERRY
MACHINE VISION
RESISTANCE
SEVERITY
INFECTION
Plant Biology & Botany
0607 Plant Biology
1001 Agricultural Biotechnology
Publication Status
Published
Article Number
57