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| Spectral singularity identification and pollution monitoring of corn under copper stress based on SD-SWT |
| Received:April 24, 2020 |
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| KeyWord:corn;copper pollution;spectral singularity analysis;discrete stationary wavelet transform;wavelet singularity indexes;stepwise multiple linear regression |
| Author Name | Affiliation | E-mail | | LI Yan-ru | College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China | | | YANG Ke-ming | College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China | ykm69@163.com | | HAN Qian-qian | College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China | | | GAO Wei | College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China | | | ZHANG Jian-hong | College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China | |
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| Abstract: |
| An initial pot experiment of a corn culture plant under the stress of multi-gradient Cu was set up in 2017 to monitor the degree of corn polluted Cu. The reflectance spectra and Cu content data of corn leaves were measured to identify the weak difference and singular information of corn spectra under the stress of the heavy metal Cu. Wavelet singularity indexes(WSI)were defined and extracted by combining first-order spectral derivative(SD)and discrete stationary wavelet transform(SWT)to identify spectral singularity compared to the conventional spectral characteristic parameters. The model of WSI-stepwise multiple linear regression(SMLR)to retrieve the Cu content in corn leaves was built by combining these algorithms. Meanwhile, the feasibility and stability of the retrieving model were verified using the reflectance spectra and Cu content data of corn leaves collected over the past year and compared with similar research results. The results showed that, compared with the conventional spectral characteristic parameters, WSI had a more significant correlation and linear relationship with the Cu content in corn leaves and could be used to monitor changes in the Cu content in corn leaves. Compared with similar research results, the WSI-SMLR model exhibited higher accuracy and stability in retrieving the Cu content in corn leaves. Therefore, wavelet singular indexes are effective and superior in monitoring the Cu pollution of corn, providing new spectral singular indexes and technical methods to monitor heavy metal pollution in crops. |
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