Based on the field hyperspectral data from the analytical spectral devices (ASD) spectrometer, we characterized the spectral properties of rice canopies infested with brown spot disease and selected spectral regions and bands sensitive to four severity degrees (severe, moderate, light, and healthy). The results show that the curves' variation on the original and the first-and second-order derivative curves are greatly different, but the spectral difference in the near-infrared region is the most obvious for each level. Specifically, the peaks are located at 822, 738, and 793 nm, while the valleys are located at 402, 570, and 753 nm, respectively. The sensitive regions are between 430-520, 530-550, and 650-710 nm, and the bands are 498, 539, and 673 nm in the sensitivity analysis, while they are in the ranges of 401-530, 550-730 as well as at 498 nm and 678 nm in the continuum removal.
Powdery mildew (Blumeria graminis) is one of the most destructive crop diseases infecting winter wheat plants, and has devastated millions of hectares of farmlands in China. The objective of this study is to detect the disease damage of powdery mildew on leaf level by means of the hyperspectral measurements, particularly using the continuous wavelet analysis. In May 2010, the reflectance spectra and the biochemical properties were measured for 114 leaf samples with various disease severity degrees. A hyperspectral imaging system was also employed for obtaining detailed hyperspectral information of the normal and the pustule areas within one diseased leaf. Based on these spectra data, a continuous wavelet analysis (CWA) was carried out in conjunction with a correlation analysis, which generated a so-called correlation scalogram that summarizes the correlations between disease severity and the wavelet power at different wavelengths and decomposition scales. By using a thresholding approach, seven wavelet features were isolated for developing models in determining disease severity. In addition, 22 conventional spectral features (SFs) were also tested and compared with wavelet features for their efficiency in estimating disease severity. The multivariate linear regression (MLR) analysis and the partial least square regression (PLSR) analysis were adopted as training methods in model development. The spectral characteristics of the powdery mildew on leaf level were found to be closely related with the spectral characteristics of the pustule area and the content of chlorophyll. The wavelet features performed better than the conventional SFs in capturing this spectral change. Moreover, the regression model composed by seven wavelet features outperformed (R2=0.77, relative root mean square error RRMSE=0.28) the model composed by 14 optimal conventional SFs (R2=0.69, RRMSE=0.32) in estimating the disease severity. The PLSR method yielded a higher accuracy than the MLR method. A combination of CWA and PLSR was found to be
Winter wheat freeze injury is one of the main agro-meteorological disasters affecting wheat production. In order to evaluate the severity of freeze injury on winter wheat systematically, we proposed a grey-system model (GSM) to monitor the degree and the distribution of the winter wheat freeze injury. The model combines remote sensing (RS) and geographic information system (GIS) technology. It gave examples of wheat freeze injury monitoring applications in Gaocheng and Jinzhou of Hebei Province, China. We carried out a quantitative evaluation method study on the severity of winter wheat freeze injury. First, a grey relational analysis (GRA) was conducted. At the same time, the weights of the stressful factors were determined. Then a wheat freezing injury stress multiple factor spatial matrix was constructed using spatial interpolation technology. Finally, a winter wheat freeze damage evaluation model was established through grey clustering algorithm (GCA), and classifying the study area into three sub-areas, affected by severe, medium or light disasters. The evaluation model were verified by the Kappa model, the overall accuracy reached 78.82% and the Kappa coefficient was 0.6754. Therefore, through integration of GSM with RS images as well as GIS analysis, quantitative evaluation and study of winter wheat freeze disasters can be conducted objectively and accurately, making the evaluation model more scientific.
WANG Hui-fangGUO weiWANG Ji-huaHUANG Wen-jiangGU Xiao-heDONG Ying-yingXUXin-gang
