Accurate prediction of soil organic carbon using spectroscopic techniques and machine learning

ABSTRACT Soil organic carbon is a key indicator for assessing soil quality and condition. Its estimation can be conducted through hyperspectral imaging spectroscopy within the visible and near-infrared (VNIR) range. This study aimed to evaluate the potential of spectroscopic techniques to accurately predict soil organic carbon (SOC) content by comparing direct field measurements with laboratory-processed samples. The efficiency and reliability of this approach were assessed. A rigorous exploratory data analysis (EDA) was conducted to identify key spectral features and minimize noise. Support vector machine (SVM) consistently outperformed other machine learning algorithms, demonstrating high accuracy and reliability. It is concluded that the model has been calibrated very well in the field, a breakthrough for science; spectroscopy offers a rapid, cost-effective and non-destructive alternative to traditional laboratory methods for SOC assessment. This has significant implications for sustainable agriculture and soil health monitoring, allowing for timely and accurate assessment of soil carbon stocks.