Hyperspectral estimation of wheat stripe rust using fractional order differential equations and Gaussian process methods.

Wheat stripe rust is the main cause of yield loss in winter wheat. For nondestructive monitoring of wheat stripe rust by remote sensing, a high-precision stripe rust monitoring model can be constructed so that field man-agement can be performed rationally and environmental damage from large doses of pesticides and chemicals can be avoided. Fractional order differential (FOD) equations can be used to flexibly control the differential step size, which enhances the spectral information and reduces the background noise. In this study, the canopy hyperspectral data under the influence of wheat stripe rust were evaluated by FOD, and the disease severity level (SL) of stripe rust in the field was analyzed by evaluating the polar difference, coefficient of variation, and correlation coefficient. Subsequently, the spectral features associated with stripe rust were screened using sig-nificance tests and Gaussian process regression sigma (GPR sigma) analysis methods. Then, models for the various wheat stripe rust severity levels were established by Gaussian process regression (GPR) with different data inputs. The results showed that the 0.8-1.4 differential order could effectively improve the correlation between spectral bands and disease severity, and the optimal correlation between the 1.2 order differential spectra and wheat stripe rust severity improved by 15% compared with the original reflectance spectra. The GPR sigma band analysis method screens only 8 bands at order 1.2, the R2 between the model-predicted SL and the measured SL is improved by 13% compared to the original reflectance spectrum, and the RMSE and MAE are reduced by 34% and 39%, respectively. Compared with the significance test method, GPR sigma band analysis selected fewer bands, constructed models with higher accuracy and was more suitable for the construction of models for estimating the severity of wheat stripe rust disease.