By Topic

Classification of rice kernels using wavelet packet transform and support vector machine

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Weifeng Zhong ; Coll. of Autom., Harbin Univ. of Sci. & Technol., Harbin, China ; Chengji Liu ; Yanli Zhang ; Liguo Wu

A classification algorithm was developed to differentiate individual infected (dead, chalky, cracked, and immature) and qualified rice kernels. The image was preprocessed by wavelet packet, and the feature regions of interest were extracted by edge detection. Ten statistical features (area, perimeter, compactness, etc.) were extracted from the image data of single kernels. The statistical features composed the pattern vector of a single kernel. The dimensionality of pattern vectors was reduced by principal component analysis. A multi-class support vector machine with kernel of radial basis function was used for classification. Using the statistical features, the rice kernels infected by dead, chalky, cracked, and immature and healthy rice kernels were classified with accuracies of 95.7%, 91.6%, 99.8%, 96.8% and 100%, respectively. Almost perfect classification was obtained under the infected vs. healthy model.

Published in:

Strategic Technology (IFOST), 2011 6th International Forum on  (Volume:2 )

Date of Conference:

22-24 Aug. 2011