Research On Wheat Powdery Mildew Monitoring Based On Multi-source Remote Sensing Data

Wheat powdery mildew is one of the major diseases in wheat production in China. The leaf and canopy scale were both showed some characteristic information when this disease occurred. As such information can be interpreted by the hyperspectral feature, this disease is very suitable for monitoring by using remote sensing. In this paper, the physiological and biochemical parameters of wheat powdery mildew had been qualitative or quantitative inverted by hyperspectral data and HJ-CCD image, which were acquired in2011and2012.(1) The correlation between physiological and biochemical characteristics and disease severity of wheat powdery mildew had been analyzed. The results showed that wheat powdery mildew disease severity and chlorophyll concentration had a significantly negative correlation (R2>0.8466), meanwhile the highly negative correlation (R2>0.9725) had also been found between disease severity and chlorophyll density. In addition, the disease severity and leaf total nitrogen, SPAD, Chi, NBI were showed negative correlation, the coefficient of determination were0.8655,0.9257,0.768and0.632, respectively. Finally, the disease severity and Flav had a significant positive correlation (RFiav=0.727*). Through the inversion and calibration for NBI index by hyperspectral technology, the R and RMSEP of LS-SVM had reached0.9475and1.0037, which is better than the BPNN.The variation between physiological state and fast light response curve had been measured by using chlorophyll fluorescence imaging system. The fluorescence imaging feature, heterogeneity between disease and health leaves had been compared under two imaging modes. The results showed that the fluorescence parameters had high heterogeneity when the leaves had been infected, especially some disease spot leaves. The dynamic change of light response curves had been obtained by the slope equation of curves which had been fitted by first-order derivative. These results indicated the rationality of the detection of powdery mildew of wheat leaf based on fluorescence imaging system.(2) The response between hyperspectral reflectance and different disease severity had been analyzed, the best sensitive waveband (630-680nm) had also been identified for detecting the wheat powdery mildew. The disease severity based on the leave level was inverted by using vegetation index, the continuum transformation and wavelet transformation method. Sixteen hyperspectral vegetation index had been used to invert the disease severity, the correlation coefficient all passed the significance test (P-value<0.05) except RVSI. PSRI. GNDVI、NDVI、SR、PRI、OSAVI、SAVI、SIPI、VARI、IWB、 RVSI and PSRI had showed negative correlation, whereas the other indexes had positive correlation. The regression model built by AAI has a better inversion result (R2=0.9195, F=0.9195). The original spectrum dimension had been reduced by db6wavelet. Seven wavelet energy coefficients passed the significance test (P-value<0.01). The DI and48spectral features had been analyzed, and the model precision had also been evaluated. The results show that41spectral feature parameters has significantly correlation with DI.(3) The spectral response between disease severity and hyperspectral reflectance based on the canopy scale had also been analyzed, the inversion model of wheat powdery mildew had been built through the correlation analysis between vegetation index and the disease index.25vegetation indexes which were selected had showed significant correlation with the DI, except the PSRI and WI. The reduction of dimension of hyperspectral data were based on the FastICA algorithm, then the monitoring model of wheat powdery mildew disease index was built by PLS. This model can separate the data in visible and near infrared waveband, thus another three dimensional space distribution model of disease stress characteristics can be built. RIC1-NIRIC1-λrep simulation equation had a high correlation coefficient and low mean square error through the discrimination of these two statistical parameters, and a good accuracy can be obtained by this equation.(4) The remote sensing monitoring model of wheat powdery mildew had been built by the change vector analysis, the method used the response relationship between wheat infected area and GNDVI, and redefined the vector space. Several vegetation indexes had been employed for the change vector analysis, the GNDVI was more sensitive to the wheat powdery mildew disease by calculating the variation range of6vegetation indexes. By means of analyzing the disease of three region, the northern region of JinZhou city had a severe disease, the central region of NingJin county had moderate disease, and the large area disease had been found in the central of GaoCheng city. These results showed that the pesticide should be applied in these area for lowing the the loss of wheat.