Classification of Rice Kernels Using Wavelet Packet Transform and Support Vector Machine

摘要：

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.

关键词： Wavelet packets Rice Principal component analysis Statistical features Support vector machine.

作者: Weifeng Zhong Chengji Liu Yanli Zhang Liguo Wu

作者单位: College of Automation,Harbin University of Science and Technology,Harbin 150080,China

会议类型: 国际会议

会议名称: The 6th International Forum on Strategic Technology(IFOST 2011)(第六届国际战略技术论坛)

会议地点: 哈尔滨

会议语种:英文

页码: 1099-1103

在线出版日期: 2011-08-22（万方平台首次上网日期，不代表论文的发表时间）