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| Discrimination of copper and lead pollution and diagnosis of pollution degree in maize leaves based on SD-SVD-Burg |
| Received:July 23, 2020 |
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| KeyWord:maize leaf;heavy metal pollution;spectral characteristic interval;Burg algorithm;pollution element screening;pollution degree diagnosing |
| Author Name | Affiliation | E-mail | | HAN Qian-qian | 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 | | GAO Wei | College of Geoscience and Surveying Engineering, China University of Mining & Technology(Beijing), Beijing 100083, China | | | LI Yan-ru | College of Geoscience and Surveying Engineering, China University of Mining & Technology(Beijing), Beijing 100083, China | | | ZHANG Jian-hong | College of Geoscience and Surveying Engineering, China University of Mining & Technology(Beijing), Beijing 100083, China | |
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| Abstract: |
| In order to study a method for rapid screening of elements and degree of heavy metal contamination in crops, a potted plant experiment of maize under different gradient of copper and lead stress was set up in 2017. Hyperspectral data of the three spectral characteristic intervals of purple valley, green peak, and red edge of maize were processed by spectral first-order differential and singular value decomposition. Power spectral density curve was plotted by combining Burg algorithm, and the spectral data collected in 2014 were used as a validation group to test the stability of the model. Results showed that the peak number and slope of the spectral density curve of spectral signals of maize leaves were different between healthy maize leaf under different Cu and Pb concentrations. The average power of the power spectrum curve and content of Cu and Pb in maize leaf had the highest correlation coefficient of 0.995 8, and this proved that the method of differentiating and diagnosing the types and levels of maize pollution elements is feasible. The correlation coefficients between the average power of green peak power spectrum curve and content of Cu and Pb in maize leaves in different years under Cu and Pb stress were 0.921 3 and 0.991 5, respectively, further verifying the stability and universality of the algorithm in the diagnosis of Cu and Pb pollution in maize. |
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