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Journal of Zhejiang University SCIENCE B 2007 Vol.8 No.10 P.738-744

http://doi.org/10.1631/jzus.2007.B0738


Characterizing and estimating rice brown spot disease severity using stepwise regression, principal component regression and partial least-square regression


Author(s):  LIU Zhan-yu, HUANG Jing-feng, SHI Jing-jing, TAO Rong-xiang, ZHOU Wan, ZHANG Li-li

Affiliation(s):  Institute of Agriculture 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:  Hyperspectral reflectance, Rice brown spot, Partial least-square (PLS) regression, Stepwise regression, Principal component regression (PCR)


LIU Zhan-yu, HUANG Jing-feng, SHI Jing-jing, TAO Rong-xiang, ZHOU Wan, ZHANG Li-li. Characterizing and estimating rice brown spot disease severity using stepwise regression, principal component regression and partial least-square regression[J]. Journal of Zhejiang University Science B, 2007, 8(10): 738-744.

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author="LIU Zhan-yu, HUANG Jing-feng, SHI Jing-jing, TAO Rong-xiang, ZHOU Wan, ZHANG Li-li",
journal="Journal of Zhejiang University Science B",
volume="8",
number="10",
pages="738-744",
year="2007",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2007.B0738"
}

%0 Journal Article
%T Characterizing and estimating rice brown spot disease severity using stepwise regression, principal component regression and partial least-square regression
%A LIU Zhan-yu
%A HUANG Jing-feng
%A SHI Jing-jing
%A TAO Rong-xiang
%A ZHOU Wan
%A ZHANG Li-li
%J Journal of Zhejiang University SCIENCE B
%V 8
%N 10
%P 738-744
%@ 1673-1581
%D 2007
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2007.B0738

TY - JOUR
T1 - Characterizing and estimating rice brown spot disease severity using stepwise regression, principal component regression and partial least-square regression
A1 - LIU Zhan-yu
A1 - HUANG Jing-feng
A1 - SHI Jing-jing
A1 - TAO Rong-xiang
A1 - ZHOU Wan
A1 - ZHANG Li-li
J0 - Journal of Zhejiang University Science B
VL - 8
IS - 10
SP - 738
EP - 744
%@ 1673-1581
Y1 - 2007
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2007.B0738


Abstract: 
Detecting plant health conditions plays a key role in farm pest management and crop protection. In this study, measurement of hyperspectral leaf reflectance in rice crop (Oryzasativa L.) was conducted on groups of healthy and infected leaves by the fungus Bipolaris oryzae (Helminthosporium oryzae Breda. de Hann) through the wavelength range from 350 to 2 500 nm. The percentage of leaf surface lesions was estimated and defined as the disease severity. Statistical methods like multiple stepwise regression, principal component analysis and partial least-square regression were utilized to calculate and estimate the disease severity of rice brown spot at the leaf level. Our results revealed that multiple stepwise linear regressions could efficiently estimate disease severity with three wavebands in seven steps. The root mean square errors (RMSEs) for training (n=210) and testing (n=53) dataset were 6.5% and 5.8%, respectively. Principal component analysis showed that the first principal component could explain approximately 80% of the variance of the original hyperspectral reflectance. The regression model with the first two principal components predicted a disease severity with RMSEs of 16.3% and 13.9% for the training and testing dataset, respectively. Partial least-square regression with seven extracted factors could most effectively predict disease severity compared with other statistical methods with RMSEs of 4.1% and 2.0% for the training and testing dataset, respectively. Our research demonstrates that it is feasible to estimate the disease severity of rice brown spot using hyperspectral reflectance data at the leaf level.

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

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