University of Oulu

Yuan Wang, Hongbing Ma, Jingzhe Wang, Li Liu, Matti Pietikäinen, Zipeng Zhang, Xiangyue Chen, Hyperspectral monitor of soil chromium contaminant based on deep learning network model in the Eastern Junggar coalfield, Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, Volume 257, 2021, 119739, ISSN 1386-1425, https://doi.org/10.1016/j.saa.2021.119739

Hyperspectral monitor of soil chromium contaminant based on deep learning network model in the Eastern Junggar coalfield

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Author: Wang, Yuan1; Ma, Hongbing2; Wang, Jingzhe3;
Organizations: 1College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
2Electronic Engineering, Tsinghua University, Beijing 100084, China
3MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China
4College of System Engineering, National University of Defense Technology, Changsha 410073, China
5Center for Machine Vision and Signal Analysis, University of Oulu, Oulu 90570, Finland
6College of Resource and Environment Sciences, Xinjiang University, Urumqi 830046, China
7College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
Format: article
Version: accepted version
Access: embargoed
Persistent link: http://urn.fi/urn:nbn:fi-fe2022030121348
Language: English
Published: Elsevier, 2021
Publish Date: 2023-03-26
Description:

Abstract

In China, over 10% of cultivated land is polluted by heavy metals, which can affect crop growth, food safety and human health. Therefore, how to effectively and quickly detect soil heavy metal pollution has become a critical issue. This study provides a novel data preprocessing method that can extract vital information from soil hyperspectra and uses different classification algorithms to detect levels of heavy metal contamination in soil. In this experiment, 160 soil samples from the Eastern Junggar Coalfield in Xinjiang were employed for verification, including 143 noncontaminated samples and 17 contaminated soil samples. Because the concentration of chromium in the soil exists in trace amounts, combined with the fact that spectral characteristics are easily influenced by other types of impurity in the soil, the evaluation of chromium concentrations in the soil through hyperspectral analysis is not satisfactory. To avoid this phenomenon, the pretreatment method of this experiment includes a combination of second derivative and data enhancement (DA) approaches. Then, support vector machine (SVM), k-nearest neighbour (KNN) and deep neural network (DNN) algorithms are used to create the discriminant models. The accuracies of the DA-SVM, DA-KNN and DA-DNN models were 95.61%, 95.62% and 96.25%, respectively. The results of this experiment demonstrate that soil hyperspectral technology combined with deep learning can be used to instantly monitor soil chromium pollution levels on a large scale. This research can be used for the management of polluted areas and agricultural insurance applications.

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Series: Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
ISSN: 1386-1425
ISSN-E: 1873-3557
ISSN-L: 1386-1425
Volume: 257
Article number: 119739
DOI: 10.1016/j.saa.2021.119739
OADOI: https://oadoi.org/10.1016/j.saa.2021.119739
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Subjects:
Copyright information: © 2021 Elsevier Ltd. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/.
  https://creativecommons.org/licenses/by-nc-nd/4.0/