Spectroscopy and Spectral Analysis, Volume. 38, Issue 1, 212(2018)

Spectra Recognition of Corn Pollution Degree under Copper and Lead Ion Stress

GUO Hui1,2, YANG Ke-ming1, ZHANG Wen-wen1, LIU Cong1, and XIA Tian1
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
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    The effect of heavy metals copper ion (Cu2+) and lead ion (Pb2+) on corn leaf spectra is weak, hidden and difficult to be detected. The corn pot experiment of different concentrations of Cu2+, Pb2+ stress is set. Corn leaf spectra, leaf Cu2+, Pb2+ content, and chlorophyll relative content were measured. The corn leaf spectra characteristic due to Cu2+, Pb2+ pollution stress was also analyzed, and then 480~670 and 670~750 nm bands were selected to be studied. The index of spectra derivative difference entropy and the first three times harmonic amplitudes (c1, c2 and c3) was defined, and then leaf spectra faint change was detected by use of the index. In the end, it was concluded that in 480~670 and 670~750 nm bands, corn leaf had the greater concentration of heavy metal ions and the greater of its corresponding spectra derivative difference entropy. In 480~670 nm bands, the harmonic amplitudes c1 and c2 can be used to identify the Cu2+, Pb2+ pollution degree; In the 670~750 nm bands, the harmonic amplitudes c1, c2 and c3 can be used to identify Cu2+ pollution level, and c2 can identify the Pb2+ pollution degree. The larger amplitude value meaned more serious pollution stress in 480~670 and 670~750 nm bands. The index of spectra differential difference entropy and harmonic amplitudes (c1, c2 and c3) can be used as identification corn pollution degree under Cu2+, Pb2+ stress. The method based on spectra and frequencey domain to identify corn pollution degree under Cu2+, Pb2+ stress was feasible. The index of spectra differential difference entropy and harmonic amplitudes (c1, c2 and c3) can be more robust and reliable in the detection and identification spectra weak differences of corn leaf affected by Cu2+, Pb2+, and the result has certain practical application value in identifying vegetation pollution degree under heavy metal on base of hyperspectral data.

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    GUO Hui, YANG Ke-ming, ZHANG Wen-wen, LIU Cong, XIA Tian. Spectra Recognition of Corn Pollution Degree under Copper and Lead Ion Stress[J]. Spectroscopy and Spectral Analysis, 2018, 38(1): 212

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    Paper Information

    Received: Apr. 12, 2017

    Accepted: --

    Published Online: Jan. 30, 2018

    The Author Email:

    DOI:10.3964/j.issn.1000-0593(2018)01-0212-06

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