Simultaneous prediction of multiple soil components using Mid-Infrared Spectroscopy and the GADF-Swin Transformer model

Abstract

Accurate characterization and monitoring of soil components are essential for optimizing agricultural practices and enhancing soil management strategies. Mid-infrared (MIR) spectroscopy has shown unique value in soil analysis due to its ability to provide rich molecular information. However, past research typically focuses on single-component prediction and struggles with the high dimensionality of MIR spectral data. This paper presents a novel approach for the simultaneous prediction of multiple soil components using MIR spectroscopy, leveraging Gramian Angular Difference Fields (GADF) and the Swin Transformer model. By transforming high-dimensional MIR spectral data into two-dimensional images and utilizing the Swin Transformer for multi-scale feature extraction and fusion, we achieve superior accuracy in simultaneous multi-component prediction. The experimental results indicate that the Swin Transformer model significantly improves overall predictive performance by effectively capturing intricate interdependencies among different soil components. This approach provides valuable insights into the application of advanced data transformation and deep learning techniques in soil analysis, particularly for simultaneous multi-component prediction, and supports more informed decisions in environmental management.