Hyperspectral Imaging for the Detection of Vitamin C Content in Potatoes Based on Fisher Discriminant Analysis Separable Information Fusion

In order to improve the accuracy and reliability of the prediction results of the vitamin C (VC) content in potatoes by hyperspectral imaging, a method for constructing input variables for predictive models based on Fisher discriminant analysis (FDA) separable data fusion was proposed. First, hyperspectral information of 200 potato samples was collected by hyperspectral imaging technology, and by comparing the modeling results obtained with the spectral data before and after preprocessing by 6 spectral preprocessing methods, multiplicative scatter correction (MSC) was determined as the optimal preprocessing method. Second, competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA) and CARS-SPA algorithm were used to extract the feature wavelengths, and 34 effective feature wavelengths were finally determined through comparative analysis. Third, the effective feature wavelengths were fused by FDA to achieve data separability, and the fused new variables were screened for their capacity to discriminate the differences among samples in order to determine the input variables for predictive models. Finally, predictive models using partial least squares (PLS) and back propagation neural network (BPNN) were established based on the variables selected before and after FDA fusion, and the results from these models were compared and analyzed. It was shown that the correlation coefficient of the BPNN model increased from 0.972 6 to 0.999 0, and the root mean square error (RMSE) reduced from 0.772 3 to 0.172 7 when the 34 effective feature wavelengths extracted by CARS were used for FDA fusion and the first three fused variables were used as the input variables, which not only greatly reduced data dimensionality, but also improved the accuracy of the detection results. Therefore, constructing input variables for the detection model based on FDA separable data fusion could improve the accuracy of the detection of potato VC content

Identification of Fusarium Head Blight in Winter Wheat Ears Using Continuous Wavelet Analysis

Fusarium head blight in winter wheat ears produces the highly toxic mycotoxin deoxynivalenol (DON), which is a serious problem affecting human and animal health. Disease identification directly on ears is important for selective harvesting. This study aimed to investigate the spectroscopic identification of Fusarium head blight by applying continuous wavelet analysis (CWA) to the reflectance spectra (350 to 2500 nm) of wheat ears. First, continuous wavelet transform was used on each of the reflectance spectra and a wavelet power scalogram as a function of wavelength location and the scale of decomposition was generated. The coefficient of determination R2 between wavelet powers and the disease infestation ratio were calculated by using linear regression. The intersections of the top 5% regions ranking in descending order based on the R2 values and the statistically significant (p-value of t-test < 0.001) wavelet regions were retained as the sensitive wavelet feature regions. The wavelet powers with the highest R2 values of each sensitive region were retained as the initial wavelet features. A threshold was set for selecting the optimal wavelet features based on the coefficient of correlation R obtained via the correlation analysis among the initial wavelet features. The results identified six wavelet features which include (471 nm, scale 4), (696 nm, scale 1), (841 nm, scale 4), (963 nm, scale 3), (1069 nm, scale 3), and (2272 nm, scale 4). A model for identifying Fusarium head blight based on the six wavelet features was then established using Fisher linear discriminant analysis. The model performed well, providing an overall accuracy of 88.7% and a kappa coefficient of 0.775, suggesting that the spectral features obtained using CWA can potentially reflect the infestation of Fusarium head blight in winter wheat ears

Identification of fusarium head blight in winter wheat ears using continuous wavelet analysis

Fusarium head blight in winter wheat ears produces the highly toxic mycotoxin deoxynivalenol (DON), which is a serious problem aecting human and animal health. Disease identification directly on ears is important for selective harvesting. This study aimed to investigate the spectroscopic identification of Fusarium head blight by applying continuous wavelet analysis (CWA) to the reflectance spectra (350 to 2500 nm) of wheat ears. First, continuous wavelet transform was used on each of the reflectance spectra and a wavelet power scalogram as a function of wavelength location and the scale of decomposition was generated. The coefficient of determination R2 between wavelet powers and the disease infestation ratio were calculated by using linear regression. The intersections of the top 5% regions ranking in descending order based on the R2 values and the statistically significant (p-value of t-test < 0.001) wavelet regions were retained as the sensitive wavelet feature regions. The wavelet powers with the highest R2 values of each sensitive region were retained as the initial wavelet features. A threshold was set for selecting the optimal wavelet features based on the coecient of correlation R obtained via the correlation analysis among the initial wavelet features. The results identified six wavelet features which include (471 nm, scale 4), (696 nm, scale 1), (841 nm, scale 4), (963 nm, scale 3), (1069 nm, scale 3), and (2272 nm, scale 4). A model for identifying Fusarium head blight based on the six wavelet features was then established using Fisher linear discriminant analysis. The model performed well, providing an overall accuracy of 88.7% and a kappa coefficient of 0.775, suggesting that the spectral features obtained using CWA can potentially reflect the infestation of Fusarium head blight in winter wheat ears