Differentiation of Yellow Rust and Powdery Mildew in Winter Wheat and Retrieving of Disease Severity Based on Leaf Level Spectral Analysis

文献类型： 外文期刊

第一作者： Yuan Lin

作者： Yuan Lin;Zhang Jing-cheng;Zhao Jin-ling;Wang Ji-hua;Yuan Lin;Zhang Jing-cheng;Wang Ji-hua;Huang Wen-jiang

作者机构：

关键词： Hyperspectral;Yellow rust;Powdery mildew;Fisher linear discrimination analysis (FLDA);Partial least square regression (PLSR)

期刊名称：SPECTROSCOPY AND SPECTRAL ANALYSIS （ 影响因子：0.589； 五年影响因子：0.504 ）

ISSN： 1000-0593

年卷期： 2013 年 33 卷 6 期

页码：

收录情况： SCI

摘要： Yellow rust and powdery mildew are two important diseases of winter wheat in China. The coincidence of their occurrence in field poses a challenge in their management and prevention. In the present study, the leaf spectra of the two diseases were measured by a spectrometer. Based on these data, we assessed the feasibility of differentiating the two diseases and evaluating their severity degrees. The disease sensitive bands and spectral features were identified through correlation analysis and independent t-test, including band regions at 665 similar to 684, 718 similar to 726 nm etc. and spectral features of DEP550-770, SIWSI etc. Based on these bands and spectral features, the models for disease discrimination and severity retrieving were developed according to FL-DA and PLSR analysis, respectively. The results showed that the selected bands and spectral features can differentiate the yellow rust and powdery mildew explicitly, which yielded an OAA of 80%. It is noted that the discrimination model performed especially well (OAA=95%) in classifying those diseased leaves with damage proportion over 20%. The retrieving model of disease severity that were constructed by spectral features achieved reasonable estimates, with the RMSE for both diseases less than 15%. The leaf level models for discriminating powdery mildew and yellow rust and estimating their disease severity serve as a basis for further study in diseases differentiation and detection at canopy level.

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