Classification model of seed cotton grade based on least square support vector machine regression method

Author

Si Chen ; Ling Li-na ; Yuan Rong-chang ; Sun Long-qing

Author_Institution

Dept. of Electr. Eng., SUNY - Univ. at Buffalo, Buffalo, NY, USA

fYear

2012

fDate

27-29 Sept. 2012

Firstpage

198

Lastpage

202

Abstract

Grade classification of seed cotton is a major problem that has an significant impact on the agricultural economy. According to characteristics like impurities, yellowness and brightness that extract from images of seed cotton, constructing classification model of seed cotton base on the least square method. Using support vector machine regression to come up with a well improved algorithm. After full learning, seed cotton classification accuracy satisfy the actual application needs.

Keywords

agricultural engineering; cotton; image classification; least squares approximations; regression analysis; support vector machines; agricultural economy; brightness; impurities; least square support vector machine regression method; seed cotton grade classification model; seed cotton images; yellowness; Brightness; Cotton; Feature extraction; Impurities; Standards; Support vector machines; Training; fuzzy math; least square method; pattern recognition; seed cotton; support vector machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Information and Automation for Sustainability (ICIAfS), 2012 IEEE 6th International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4673-1976-8

Type

conf

DOI

10.1109/ICIAFS.2012.6419904

Filename

6419904