Rapid, automated detection of stem canker symptoms in woody perennials using artificial neural network analysis

Bo Li, Michelle T. Hulin, Philip Brain, John W. Mansfield, Robert W. Jackson, Richard J. Harrison

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)
22 Downloads (Pure)

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.

Original languageEnglish
Article number57
Number of pages9
JournalPlant Methods
Volume11
DOIs
Publication statusPublished - 24 Dec 2015

Keywords

  • Artificial neural network
  • Image analysis
  • Stem canker

ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Plant Science

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