Classification Model of Seed Cotton Grade Based on Least Square Support Vector Machine Regression Method

被引：0

作者：

Si Chen ^{[1
]}

Ling Li-na ^{[2
]}

Yuan Rong-chang ^{[3
]}

Sun Long-qing ^{[2
]}

机构：

[1] SUNY Buffalo, Dept Elect Engn, Buffalo, NY 14260 USA

[2] China Agr Univ, Beijing, Peoples R China

[3] Power Automat Dept, Beijing, Peoples R China

来源：

2012 IEEE 6TH INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION FOR SUSTAINABILITY (ICIAFS2012) | 2012年

关键词：

seed cotton; fuzzy math; pattern recognition; least square method; support vector machine;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：194 / +

页数：5

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