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

查看全文 作　　者：LIU Zhan-yu1, HUANG Jing-feng1, SHI Jing-jing1, TAO Rong-xiang2, ZHOU Wan3, ZHANG Li-li3 (1Institute of Agriculture Remote Sensing and Information System Application, Zhejiang University, Hangzhou 310029, China) (2Institute of Plant Protection and Microbiology, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China) (3Plant Inspection Station of Hangzhou City, Hangzhou 310020, China) 高影响力作者 出　　处：《Journal of Zhejiang University-Science B(Biomedicine & Biotechnology)》索引2007年第8卷第10期,共7页高影响力期刊 基　　金：the Hi-Tech Research and Development Program (863) of China (No. 2006AA10Z203);the National Scienceand Technology Task Force Project (No. 2006BAD10A01), China 摘　　要：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, respec-tively. 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 demon-strates that it is feasible to estimate the disease severity of rice brown spot using hyperspectral reflectance data at the leaf level. 关 键 词：HYPERSPECTRAL reflectance Rice BROWN SPOT Partial least-square (PLS) regression STEPWISE regression Principal component regression (PCR)
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