Texture and shape attribute selection for plant disease monitoring in a mobile cloud-based environment
- Resource Type
- Conference
- Authors
- Siricharoen, Punnarai; Scotney, Bryan; Morrow, Philip; Parr, Gerard
- Source
- 2016 IEEE International Conference on Image Processing (ICIP) Image Processing (ICIP), 2016 IEEE International Conference on. :489-493 Sep, 2016
- Subject
- Signal Processing and Analysis
Diseases
Shape
Histograms
Error analysis
Monitoring
Mobile communication
Indexes
histogram of shape features
textural features
feature selection
pathological plant monitoring
- Language
- ISSN
- 2381-8549
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.