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| SM-DMFD model for detecting pollution information of corn leaves stressed by copper ions |
| Received:April 11, 2017 |
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| KeyWord:spectral analysis;heavy metal pollution;information screening model;fractal dimension;plant pollution monitoring |
| Author Name | Affiliation | E-mail | | ZHANG Wei | College of Geoscience and Surveying Engineering, China University of Mining & Technology(Beijing), Beijing 100083, China | | | YANG Ke-ming | College of Geoscience and Surveying Engineering, China University of Mining & Technology(Beijing), Beijing 100083, China | ykm69@163.com | | SUN Tong-tong | College of Geoscience and Surveying Engineering, China University of Mining & Technology(Beijing), Beijing 100083, China | | | WANG Xiao-feng | College of Geoscience and Surveying Engineering, China University of Mining & Technology(Beijing), Beijing 100083, China | | | CHENG Long | College of Geoscience and Surveying Engineering, China University of Mining & Technology(Beijing), Beijing 100083, China | |
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| Abstract: |
| Potted corn experiments were set up with different Cu2+ stress gradients in this study, based on the collected spectra of different types of corn leaves and the measured Cu2+ contents in the leaves. An SM-DMFD model for detecting the pollution information of corn leaf spectra was constructed by building a screening model(SM) of leaf spectral variant information and using some spectral analysis methods such as the harmonic analysis(HA) preprocessing, empirical mode decomposition(EMD), discrete wavelet multilayer decomposition, and divider method fractal dimension(DMFD) methods. The application result of the SM-DMFD model was analyzed and compared with the results obtained by some conventional methods such as the green-peak height(GH), red edge position(REP), maximum value of red edge(MR), first derivative area of red edge(FAR), and box dimension method(BDM) for monitoring plant heavy metal pollution information. The analyzed and compared results show that the correlation coefficient R of the DMFD values obtained using the SM-DMFD model and the Cu2+ contents in corn leaves and the fitting determination coefficient R2 reached 0.986 0 and 0.972 3, respectively. These coefficients indicated that the SM-DMFD model can effectively discriminate the variant information between the different spectra and can judge the pollution degree of corn leaves, thus verifying that the SM-DMFD model has better effectiveness and superiority in monitoring Cu2+ pollution information of the corn. Meanwhile, the model was also verified as having the ideal monitoring effect through visualizing the monitoring results of corn pollution information detected via the different methods. |
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