1. Institute of Remote Sensing and Information Application, Zhejiang University, Hangzhou 310058, China 2. Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China 3. Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
Abstract：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 665～684, 718～726 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 FLDA 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.
Key words：Hyperspectral;Yellow rust;Powdery mildew;Fisher linear discrimination analysis (FLDA);Partial least square regression (PLSR)
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