Image pattern classification for the identification of disease causing agents in plants

Authors:
A. Camargo;J. S. Smith
Affiliations:
University of East Anglia, School of Computing Sciences, Norwich NR4 7TJ, UK;Department of Electrical Engineering and Electronics, The University of Liverpool, Brownlow Hill, Liverpool L69 3GJ, UK
Venue:
Computers and Electronics in Agriculture
Year:
2009

Citing 1
Cited 7

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Abstract

This study reports a machine vision system for the identification of the visual symptoms of plant diseases, from coloured images. Diseased regions shown in digital pictures of cotton crops were enhanced, segmented, and a set of features were extracted from each of them. Features were then used as inputs to a Support Vector Machine (SVM) classifier and tests were performed to identify the best classification model. We hypothesised that given the characteristics of the images, there should be a subset of features more informative of the image domain. To test this hypothesis, several classification models were assessed via cross-validation. The results of this study suggested that: texture-related features might be used as discriminators when the target images do not follow a well defined colour or shape domain pattern; and that machine vision systems might lead to the successful discrimination of targets when fed with appropriate information.