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| Discrimination of Pb and Cu pollution types in crops based on the discrimination feature of lead - copper pollution type |
| Received:May 03, 2022 |
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| KeyWord:hyperspectral;heavy metal pollution;pollution type discrimination;maize leave |
| Author Name | Affiliation | E-mail | | SHANG Xiangchun | Suntuan Coalmine, Huaibei Mining Co., Ltd., Huaibei 235000, China | | | JIN Qian | Key Laboratory of Mineral Resources and Ecological Environment Monitoring of Hebei Province, Baoding 071051, China | 13373121110@163.com | | YANG Keming | 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 | | | WU Bing | College of Geoscience and Surveying Engineering, China University of Mining and Technology(Beijing), Beijing 100083, China | |
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| Abstract: |
| To discriminate the heavy metal pollution types present in crops, a typical crop（maize）was cultivated under Pb and Cu stress, following which hyperspectral data containing Pb and Cu pollution information were obtained from the crop leaves. Fractional-order and integer - order derivative transformations were first applied to the spectra. Then, difference ratio spectral indexes（DRSIs）were used to construct feature covariates for forming the discrimination feature of lead - copper pollution type（DFLCPT）. Finally, random forest classification（RFC）, K-nearest neighbor classification（KNNC）, support vector machine classification（SVC）, and Gaussian process classification（GPC）models were constructed on the basis of the DFLCPT data for discrimination of the Pb and Cu pollution type in the crop. It was found that among the DRSIs constructed from a variety of derivative spectra, those [2 412, 1 223, 636] based on the 0.9 derivative spectrum had the largest absolute value of the correlation coefficient between the Pb and Cu pollution types of the sample, which is 0.764 1. Among the four classification models established on the basis of multidimensional DFLCPT（DFLCPTnD）data, the RFC model had a better effect than the SVC, KNNC, and GPC models. The highest accuracy of the RFC model for the training set and verification set was 100%, with good accuracy and strong stability. The results show that the DFLCPT-based model has achieved the expected effect in the discrimination of Pb and Cu pollution types and can provide technical support for the discrimination of heavy metal pollution types in crops grown on a large scale. |
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