Combining genetic algorithm and SVM for corn variety identification

Author

Min Zhao ; Wenfu Wu ; Ya qiu Zhang ; Xing Li

Author_Institution

Coll. of Biol. & Agric. Eng., Jilin Univ., Changchun, China

fYear

2011

fDate

19-22 Aug. 2011

Firstpage

990

Lastpage

993

Abstract

In this article, a real-time, accurate and objective identification of different varieties of corn seeds is proposed, which is a large number of original features, contained color, texture and shape features, were extracted from corn seed images. Then, genetic algorithm and support vector machine (SVM) were used to select important ones and determine species. The proposed methods have optimized varieties recognition algorithm based on machine vision, which also improved the accuracy and achieved the best performance percentage of 94.4%. Basically, the average consumption time for every seed is 0.141s.

Keywords

agriculture; computer vision; feature extraction; genetic algorithms; image classification; image colour analysis; image texture; shape recognition; support vector machines; SVM; corn seed image identification; corn variety identification; corn variety recognition algorithm; feature extraction; genetic algorithm; machine vision; support vector machine; time 0.141 s; Accuracy; Biological cells; Feature extraction; Genetic algorithms; Image color analysis; Support vector machine classification; feature extraction; genetic algorithm; support vector machine; varieties identification;

fLanguage

English

Publisher

ieee

Conference_Titel

Mechatronic Science, Electric Engineering and Computer (MEC), 2011 International Conference on

Conference_Location

Jilin

Print_ISBN

978-1-61284-719-1

Type

conf

DOI

10.1109/MEC.2011.6025631

Filename

6025631