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 language | English |
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Title of host publication | Unknown Host Publication |
Publisher | IEEE |
Pages | 489-493 |
Number of pages | 5 |
ISBN (Print) | 978-1-4673-9961-6 |
DOIs | |
Publication status | Published (in print/issue) - 19 Aug 2016 |
Event | 2016 IEEE International Conference on Image Processing (ICIP) - Phoenix, AZ, USA Duration: 19 Aug 2016 → … |
Conference
Conference | 2016 IEEE International Conference on Image Processing (ICIP) |
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Period | 19/08/16 → … |
Keywords
- Histogram of shape features
- textural features
- feature selection