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| Detection of copper ion pollution in corn leaves based on continuous wavelet transform-Hilbert-Huang transform |
| Received:April 24, 2023 |
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| KeyWord:continuous wavelet transform;Hilbert-Huang transform;copper pollution stress;corn leave |
| Author Name | Affiliation | E-mail | | GUO Hui | School of Geomatics, Anhui University of Science and Technology, Huainan 232001, China
Coal Industry Engineering Research Center of Mining Area Environment and Disaster Cooperative Monitoring, Anhui University of Science and Technology, Huainan 232001, China | | | SHI Hai | School of Geomatics, Anhui University of Science and Technology, Huainan 232001, China
Coal Industry Engineering Research Center of Mining Area Environment and Disaster Cooperative Monitoring, Anhui University of Science and Technology, Huainan 232001, China | 1508069850@qq.com | | ZHANG Quanwang | School of Geomatics, Anhui University of Science and Technology, Huainan 232001, China
Coal Industry Engineering Research Center of Mining Area Environment and Disaster Cooperative Monitoring, Anhui University of Science and Technology, Huainan 232001, China | |
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| Abstract: |
| To accurately detect weak spectral distortion information for crops under different concentrations of heavy metal pollution, a corn pot experiment with different Cu2+ stress gradients was performed. The spectra of corn leaves under different gradients were collected and the Cu2+ content of the leaves was measuring at the same time. Continuous wavelet transform(CWT) combined with Hilbert-Huang transform(HHT)was used to construct a CWT-HHT algorithm to detect spectral copper pollution information from the corn leaves. This method was compared with other conventional vegetation index monitoring methods, such as the red edge position, the red edge normalization index, and the red edge vegetation stress index. The results showed that the instantaneous energy peak extracted using the CWT-HHT detection method had a trend of first increasing and then decreasing, which was consistent with the trend in the Cu2+ content of the corn leaves. Moreover, the CWT-HHT method was found to be better than the vegetation index monitoring method for detecting heavy metal pollution in crops, indicating that the CWT-HHT method is feasible for the detection of heavy metal copper pollution in corn leaves. |
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