Image analysis of broken rice grains of Khao Dawk Mali rice

Author

Ngampak, Dollawat ; Piamsa-nga, Punpiti

Author_Institution

Dept. of Comput. Eng., Kasetsart Univ., Bangkok, Thailand

fYear

2015

fDate

28-31 Jan. 2015

Firstpage

115

Lastpage

120

Abstract

In process of rice grain milling, rice grains are sorted by size into many categories for sale at different prices. By observation on “small broken”, which is a low-quality category of sorted result, we found that it composes of significant amounts of more expensive rice grains. In this research, we propose a method to evaluate broken rice grains in order to make higher profit from its higher quality portion by image analysis. Our algorithm is to categorize “small broken” into four types: small broken, broken, big broken and head rice, which are classes described by the Department of Rice, Thailand. Least-Square Support Vector Machine (LS-SVM) with Radius Basis Function (RBF) kernel is used as a classifier in the algorithm. The accuracy of the algorithm is 98.20%.

Keywords

crops; image processing; milling; production engineering computing; radial basis function networks; support vector machines; Khao Dawk Mali rice; LS-SVM; RBF kernel; broken rice grains; image analysis; least-square support vector machine; radius basis function kernel; rice grain milling; rice grain sorting; Accuracy; Feature extraction; Head; Image edge detection; Kernel; Milling; Support vector machines; Broken Rice Grain; Classification;

fLanguage

English

Publisher

ieee

Conference_Titel

Knowledge and Smart Technology (KST), 2015 7th International Conference on

Conference_Location

Chonburi

Print_ISBN

978-1-4799-6048-4

Type

conf

DOI

10.1109/KST.2015.7051471

Filename

7051471