Abstract
BackgroundPseudomonas 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.ResultsIn 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.ConclusionsAutomated image analysis will facilitate rapid screening of material for resistance to bacterial and other phytopathogens, allowing more efficient selection and quantification of resistance responses.Electronic supplementary materialThe online version of this article (doi:10.1186/s13007-015-0100-8) contains supplementary material, which is available to authorized users.
Highlights
Pseudomonas syringae can cause stem necrosis and canker in a wide range of woody species including cherry, plum, peach, horse chestnut and ash
This paper reports the development of an automated image analysis software which utilises artificial neural network (ANN) to analyse images of cherry and plum shoots exhibiting necrosis due to bacterial canker, with the goal of improving the accuracy of disease resistance screening
Quantification based on automated image analysis A feed-forward artificial neural network (ANN), which is known as multi-layer perceptrons (MLP), was implemented for the classification of diseased and healthy shoot tissue
Summary
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
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