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Received: 2005-07-11

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Journal of Zhejiang University SCIENCE B 2005 Vol.6 No.11 P.1095~1100

10.1631/jzus.2005.B1095


Identification of rice seed varieties using neural network


Author(s):  LIU Zhao-yan, CHENG Fang, YING Yi-bin, RAO Xiu-qin

Affiliation(s):  School of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China

Corresponding email(s):   fcheng@zju.edu.cn

Key Words:  Machine vision, Digital image processing, Neural network, Rice seeds, Classification


LIU Zhao-yan, CHENG Fang, YING Yi-bin, RAO Xiu-qin. Identification of rice seed varieties using neural network[J]. Journal of Zhejiang University Science B, 2005, 6(11): 1095~1100.

@article{title="Identification of rice seed varieties using neural network",
author="LIU Zhao-yan, CHENG Fang, YING Yi-bin, RAO Xiu-qin",
journal="Journal of Zhejiang University Science B",
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pages="1095~1100",
year="2005",
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doi="10.1631/jzus.2005.B1095"
}

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%T Identification of rice seed varieties using neural network
%A LIU Zhao-yan
%A CHENG Fang
%A YING Yi-bin
%A RAO Xiu-qin
%J Journal of Zhejiang University SCIENCE B
%V 6
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%@ 1673-1581
%D 2005
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2005.B1095

TY - JOUR
T1 - Identification of rice seed varieties using neural network
A1 - LIU Zhao-yan
A1 - CHENG Fang
A1 - YING Yi-bin
A1 - RAO Xiu-qin
J0 - Journal of Zhejiang University Science B
VL - 6
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SP - 1095
EP - 1100
%@ 1673-1581
Y1 - 2005
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2005.B1095


Abstract: 
A digital image analysis algorithm based color and morphological features was developed to identify the six varieties (ey7954, syz3, xs11, xy5968, xy9308, z903) rice seeds which are widely planted in Zhejiang Province. Seven color and fourteen morphological features were used for discriminant analysis. Two hundred and forty kernels used as the training data set and sixty kernels as the test data set in the neural network used to identify rice seed varieties. When the model was tested on the test data set, the identification accuracies were 90.00%, 88.00%, 95.00%, 82.00%, 74.00%, 80.00% for ey7954, syz3, xs11, xy5968, xy9308, z903 respectively.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

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