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Hyperspectral monitoring of maize leaves under copper stress at different growth stages

ABSTRACT Using hyperspectral remote sensing technology to identify spectral changes of crop leaves under heavy metal pollution is an important way to ensure food security. Discrete Fourier transform (DFT) and discrete wavelet transform (DWT) are used to rapid screen the spectral characteristics of maize growth status under copper (Cu) stress, and a zero-crossing rate (ZCR) was proposed to quantify the results. The experiment of maize under different Cu stress treatment was carried out, and the spectra of maize leaves at seedling, jointing and heading stages were measured. Analysis of frequency spectrum and amplitude spectrum of the maize leaves spectra under copper stress showed that the growth stages of maize could be effectively determined according to the phase of leaves spectra. The approximate coefficient (CA) and detail coefficient (CD) were obtainedat heading stage. It was found that the wavelength corresponding to the peak value of the acquired variable of CA (ca) in the range of 690–710 nm was moving in the direction of shortwave. In addition, the degree of copper stress in maize could be divided according to the peak deviation of ca. The characteristic wavebands could be detected according to the CD variable (cd) .