The New Hyperspectral Analysis Method for Distinguishing the Types of Heavy Metal Copper and Lead Pollution Elements

Jianhong Zhang, Min Wang, Keming Yang, Yanru Li, Yaxing Li, Bing Wu and Qianqian Han
Additional contact information
Jianhong Zhang: College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
Min Wang: Youth League Committee, North China University of Science and Technology, Tangshan 063210, China
Keming Yang: College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
Yanru Li: College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
Yaxing Li: College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
Bing Wu: College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
Qianqian Han: College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China

IJERPH, 2022, vol. 19, issue 13, 1-26

Abstract: In recent years, the problem of heavy metal pollution in agriculture caused by industrial development has been particularly prominent, directly affecting food and ecological environmental safety. Hyperspectral remote sensing technology has the advantages of high spectral resolution and nondestructive monitoring. The physiological and biochemical parameters of crops undergo similar changes under different heavy metal stresses. Therefore, it is a great challenge to explore the use of hyperspectral technology to distinguish the types of the heavy metal copper (Cu) and lead (Pb) elements. This is also a hot topic in the current research. In this study, several models are proposed to distinguish copper and lead elements by combining multivariate empirical mode decomposition (MEMD) transformation and machine learning. First, MEMD is introduced to decompose the original spectrum, which effectively removes the noise and highlights and magnifies the weak information of the spectrum. The successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and iteratively retaining informative variables (IRIV) were used to screen the characteristic bands and were combined with extreme learning machine (ELM), support vector machine (SVM), and general regression neural network (GRNN) algorithms to build models to distinguish the types of Cu and Pb elements. The quality of the model was evaluated using accuracy ( A ), precision ( P ), recall ( R ), and F -score. The results showed that the MEMD-SPA-SVM, MEMD-CARS-SVM, MEMD-SPA-ELM, MEMD-CARS-ELM, and MEMD-IRIV-ELM models intuitively and effectively distinguished the types of Cu and Pb elements. Their accuracy and F -scores were all greater than 0.8. To verify the superiority of these models, the same model was constructed based on first derivative (FD) and second derivative (SD) transformations, and the obtained classification and recognition accuracy ( A ) and F -score were both lower than 0.8, which further confirmed the superiority of the model established after MEMD transformation. The model proposed in this study has great potential for applying hyperspectral technology to distinguish the types of elements contaminated by Cu and Pb in crops.

Keywords: spectral analysis; environmental heavy metal pollution; corn leaves; machine learning; multivariate empirical mode decomposition (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1660-4601/19/13/7755/pdf (application/pdf)
https://www.mdpi.com/1660-4601/19/13/7755/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:19:y:2022:i:13:p:7755-:d:846806

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().