A hyperspectral metal concentration inversion method using attention mechanism and graph neural network

Soil heavy metal contamination has emerged as a global environmental concern, posing significant risks to human health and ecosystem integrity. Hyperspectral technology, with its non-invasive, non-destructive, large-scale, and high spectral resolution capabilities, shows promising applications in monitoring soil heavy metal pollution. Traditional monitoring methods are often time-consuming, labor-intensive, and expensive, limiting their effectiveness for rapid, large-scale assessments. This study introduces a novel deep learning method, SpecMet, for estimating heavy metal concentrations in naturally contaminated agricultural soils using hyperspectral data. The SpecMet model extracts features from hyperspectral data using convolutional neural networks (CNNs) and achieves end-to-end prediction of soil heavy metal concentrations by integrating attention mechanisms and graph neural networks. Results demonstrate that the OR-SpecMet model, which utilizes raw spectral data, achieves optimal prediction performance, significantly surpassing traditional machine learning methods such as multiple linear regression, partial least squares regression, and support vector machine regression in estimating concentrations of lead (Pb), copper (Cu), cadmium (Cd), and mercury (Hg). Moreover, training specialized OR-SpecMet models for individual heavy metals better accommodates their unique spectral-concentration relationships, enhancing overall estimation accuracy while achieving a 20.3 % improvement in predicting low-concentration mercury. The OR-SpecMet method showcases the superior performance and extensive application potential of deep learning techniques in precise soil heavy metal pollution monitoring, offering new insights and reliable technical support for soil pollution prevention and agricultural ecosystem protection. The code and datasets used in this study are publicly available at: https://github.com/zhang2lei/metal.git.