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Hyperspectral identification of cotton verticillium disease severity

文献类型： 外文期刊

作者： Jin, Ning 1 ; Huang, Wenjiang 2 ; Ren, Yu 3 ; Luo, Juhua 2 ; Wu, Yongli 1 ; Jing, Yuanshu 4 ; Wang, Dayong 1 ;

作者机构： 1.Shanxi Climate Ctr, Taiyuan 030002, Peoples R China

2.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

3.Tianjin Climate Ctr, Tianjin 300074, Peoples R China

4.Nanjing Univ Informat Sci & Technol, Coll Appl Meteorol, Nanjing 210044, Jiangsu, Peoples R China

关键词： Wavelet transform; Cotton verticillium; Hyperspectral remote sensing; Disease severity identification

期刊名称：OPTIK （ 影响因子：2.443； 五年影响因子：1.955 ）

ISSN： 0030-4026

年卷期： 2013 年 124 卷 16 期

页码：

收录情况： SCI

摘要： Hyperspectral remote sensing provides fine spectral information for diagnosing crop disease severity, and in this paper the application of the hyperspectral remote sensing in identifying cotton verticillium disease severity was investigated. The wavelet transform was employed to extract the principal information and reduce the dimensions of the hyperspectral reflectance data, which were measured for cotton blades in different disease severity. Then, four identification models were built using discriminant analysis, back propagation (BP) neural network, genetic back propagation (GA-BP) neural network and support vector machine (SVM). The effects of the four models were examined and it was indicated that the SVM approach was the best. (C) 2012 Elsevier GmbH. All rights reserved.

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