Analysis on spectral singularity and pollution assessment of corn leaves under copper stress
【Abstract】According to the data of the corn leaf spectra collected by a SVC hyper-spectrometer and the Cu contents measured synchronously in the corn leaves, the high frequency components of fifth layer wavelet decomposition (d5) were obtained by the “Db5” wavelet in Daubechies wavelets for the corn leaf spectra within the wavelength range from 350 to 2 500 nm. The fractal dimension of d5 could be calculated by the box dimension method, and the changing trend of fractal dimension of corn leaf spectrum under different Cu stress gradients was discussed based on a neighborhood change rate (α) of the fractal dimension. Therefore, the spectral singularity parameters of d5, such as the singular range and singular amplitude, might be quantitatively calculated and analyzed The experimental results showed that the d5 could precisely detect the weak spectral singularity information of corn under different Cu stress gradients, and realize the separation of hyperspectral signals of corn leaves at different pollution degrees; the d5 fractal dimensions reduced firstly, then rose slowly and finally reached the peak value with the increase in pollution degree, among which, the fractal dimension of Cu(100) was the minimum; the α values between CK(0) and Cu(100) were negative but positive in the other two stress gradient intervals, and the absolute value of α rates between Cu(100) and Cu(300) was the smallest, while the absolute value of α rates between Cu(300) and Cu(500) was the largest; it was validated that there was a strong correlation of the Cu content in corn leaves with the singular amplitude and fractal dimension through establishing the model on estimating Cu content in the leaf. The difference of Cu content in each leaf with different pollution degrees reached a significant level (is 0.05), and its determination coefficient R2 = 0.950 1. So the fractal dimension and singularity characteristics of spectral high frequency components could be used to diagnose effectively and analyze quantitatively the Cu pollution status of corn, and might provide some reference for monitoring heavy metal pollution of crops.
【Keywords】 hyperspectral; copper stress pollution; wavelet transform; fractal analysis; singular parameter; estimation model;
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