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

Content Provider	IEEE Xplore Digital Library
Author	Si Chen Ling Li-na Yuan Rong-chang Sun Long-qing
Copyright Year	2012
Description	Author affiliation: China Agricultural University, China (Ling Li-na) \|\| Department of Electrical Enginnering, University at Buffalo, The State University of New York, US (Si Chen; Sun Long-qing) \|\| Power Automation Department, China (Yuan Rong-chang)
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.
Starting Page	198
Ending Page	202
File Size	206827
Page Count	5
File Format	PDF
ISBN	9781467319768
e-ISBN	9781467319751
DOI	10.1109/ICIAFS.2012.6419904
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-09-27
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Support vector machines support vector machine Impurities Brightness least square method Feature extraction Cotton Standards seed cotton fuzzy math pattern recognition
Content Type	Text
Resource Type	Article

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