Texture and shape attribute selection for plant disease monitoring in a mobile cloud-based environment

Punnarai Siricharoen, Scotney Bryan, Philip Morrow, Gerard Parr

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)
13 Downloads (Pure)

Abstract

We focus on feature extraction and selection to best represent texture and shape properties of plant diseases in an image- based leaf monitoring system implemented in a mobile-cloud environment. A number of textural and region-based features are aggregated from previous studies; also we introduce mean and peak indices of histogram-of-shape as disease property representations along with the proposed and enhanced shape features based on diseased regions. A total of 260 colour-based attributes and 163 shape attributes are searched to find the best potential features based on different aspects: probability of feature error, correlation, targeted-class relevancy and the separability quality of a feature. Experimental results show that the best selected feature set which combines colour-based and shape features yields high classification accuracy on wheat disease images captured by a smartphone camera and also provides insights into potential sets of features to be further implemented as a lightweight standalone mobile application.
Original languageEnglish
Title of host publicationUnknown Host Publication
PublisherIEEE
Pages489-493
Number of pages5
ISBN (Print)978-1-4673-9961-6
DOIs
Publication statusPublished - 19 Aug 2016
Event2016 IEEE International Conference on Image Processing (ICIP) - Phoenix, AZ, USA
Duration: 19 Aug 2016 → …

Conference

Conference2016 IEEE International Conference on Image Processing (ICIP)
Period19/08/16 → …

Keywords

  • Histogram of shape features
  • textural features
  • feature selection

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