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

Siricharoen, Punnarai, Bryan, Scotney, Morrow, Philip and Parr, Gerard (2016) Texture and shape attribute selection for plant disease monitoring in a mobile cloud-based environment. In: 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA. IEEE. 5 pp. [Conference contribution]

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URL: http://dx.doi.org/10.1109/ICIP.2016.7532405

DOI: 10.1109/ICIP.2016.7532405


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.

Item Type:Conference contribution (Paper)
Keywords:Histogram of shape features, textural features, feature selection
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Information Engineering
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Information and Communication Engineering
ID Code:37396
Deposited By: Professor Philip Morrow
Deposited On:07 Apr 2017 11:47
Last Modified:07 Apr 2017 11:47

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