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CLC number: TP7; S43

On-line Access: 2010-01-01

Received: 2009-07-06

Revision Accepted: 2009-11-16

Crosschecked: 2009-11-19

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Journal of Zhejiang University SCIENCE B 2010 Vol.11 No.1 P.71-78

10.1631/jzus.B0900193


Discrimination of rice panicles by hyperspectral reflectance data based on principal component analysis and support vector classification


Author(s):  Zhan-yu LIU, Jing-jing SHI, Li-wen ZHANG, Jing-feng HUANG

Affiliation(s):  Institute of Agricultural Remote Sensing and Information System Application, Zhejiang University, Hangzhou 310029, China; more

Corresponding email(s):   zdrsbond@zju.edu.cn, hjf@zju.edu.cn

Key Words:  Rice panicle, Principal component analysis (PCA), Support vector classification (SVC), Hyperspectral reflectance, Derivative spectra


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Zhan-yu LIU, Jing-jing SHI, Li-wen ZHANG, Jing-feng HUANG. Discrimination of rice panicles by hyperspectral reflectance data based on principal component analysis and support vector classification[J]. Journal of Zhejiang University Science B, 2010, 11(1): 71-78.

@article{title="Discrimination of rice panicles by hyperspectral reflectance data based on principal component analysis and support vector classification",
author="Zhan-yu LIU, Jing-jing SHI, Li-wen ZHANG, Jing-feng HUANG",
journal="Journal of Zhejiang University Science B",
volume="11",
number="1",
pages="71-78",
year="2010",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.B0900193"
}

%0 Journal Article
%T Discrimination of rice panicles by hyperspectral reflectance data based on principal component analysis and support vector classification
%A Zhan-yu LIU
%A Jing-jing SHI
%A Li-wen ZHANG
%A Jing-feng HUANG
%J Journal of Zhejiang University SCIENCE B
%V 11
%N 1
%P 71-78
%@ 1673-1581
%D 2010
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.B0900193

TY - JOUR
T1 - Discrimination of rice panicles by hyperspectral reflectance data based on principal component analysis and support vector classification
A1 - Zhan-yu LIU
A1 - Jing-jing SHI
A1 - Li-wen ZHANG
A1 - Jing-feng HUANG
J0 - Journal of Zhejiang University Science B
VL - 11
IS - 1
SP - 71
EP - 78
%@ 1673-1581
Y1 - 2010
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.B0900193


Abstract: 
Detection of crop health conditions plays an important role in making control strategies of crop disease and insect damage and gaining high-quality production at late growth stages. In this study, hyperspectral reflectance of rice panicles was measured at the visible and near-infrared regions. The panicles were divided into three groups according to health conditions: healthy panicles, empty panicles caused by Nilaparvata lugens Stål, and panicles infected with Ustilaginoidea virens. Low order derivative spectra, namely, the first and second orders, were obtained using different techniques. principal component analysis (PCA) was performed to obtain the principal component spectra (PCS) of the foregoing derivative and raw spectra to reduce the reflectance spectral dimension. support vector classification (SVC) was employed to discriminate the healthy, empty, and infected panicles, with the front three PCS as the independent variables. The overall accuracy and kappa coefficient were used to assess the classification accuracy of SVC. The overall accuracies of SVC with PCS derived from the raw, first, and second reflectance spectra for the testing dataset were 96.55%, 99.14%, and 96.55%, and the kappa coefficients were 94.81%, 98.71%, and 94.82%, respectively. Our results demonstrated that it is feasible to use visible and near-infrared spectroscopy to discriminate health conditions of rice panicles.

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