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

Li, B., Hulin, M. T., Brain, P., Mansfield, J. W., Jackson, R. W. and Harrison, R. J. (2015) Rapid, automated detection of stem canker symptoms in woody perennials using artificial neural network analysis. Plant Methods, 11 (1). 57. ISSN 1746-4811 doi: 10.1186/s13007-015-0100-8

Abstract/Summary

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.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/51835
Identification Number/DOI	10.1186/s13007-015-0100-8
Refereed	Yes
Divisions	Life Sciences > School of Biological Sciences > Biomedical Sciences
Publisher	BioMed Central
Download/View statistics	View download statistics for this item