College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China.
Electronic Engineering, Tsinghua University, Beijing 100084, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2021 Aug 5;257:119739. doi: 10.1016/j.saa.2021.119739. Epub 2021 Mar 26.
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.
在中国，超过 10%的耕地受到重金属污染，这可能会影响作物生长、食品安全和人类健康。因此，如何有效、快速地检测土壤重金属污染已成为一个关键问题。本研究提供了一种新颖的数据预处理方法，可以从土壤高光谱中提取重要信息，并使用不同的分类算法来检测土壤中重金属污染的程度。在本实验中，使用了来自新疆东准噶尔煤田的 160 个土壤样本，其中包括 143 个无污染样本和 17 个污染土壤样本。由于土壤中铬的浓度存在于痕量水平，再加上光谱特征很容易受到土壤中其他类型杂质的影响，因此通过高光谱分析评估土壤中铬的浓度并不理想。为了避免这种现象，本实验的预处理方法包括二阶导数和数据增强（DA）方法的组合。然后，使用支持向量机（SVM）、k-最近邻（KNN）和深度神经网络（DNN）算法来创建判别模型。DA-SVM、DA-KNN 和 DA-DNN 模型的准确率分别为 95.61%、95.62%和 96.25%。实验结果表明，土壤高光谱技术与深度学习相结合可以用于大规模实时监测土壤铬污染水平。本研究可用于污染区域的管理和农业保险应用。
