X-LIBS: Interpretable Soil Classification Using Explainable AI and Laser-Induced Breakdown Spectroscopy

Machine learning (ML) has emerged as a powerful tool for soil analysis using Laser-Induced Breakdown Spectroscopy (LIBS). However, traditional black-box models often lack interpretability, limiting their effectiveness in decision-making processes. This study introduced explainable AI (XAI) techniques to enhance both the interpretability and classification performance of Partial Least Squares Discriminant Analysis (PLS-DA) models for soil classification with LIBS. Local Interpretable Model-agnostic Explanations (LIME) was employed to identify the local spectral features associated with soil elements, providing explanations for their contributions to classification outcomes. To improve classification performance on unlabeled spectra, prediction uncertainty was quantified by sequentially removing the top local spectral features identified by LIME as critical to the classification. Higher uncertainty was associated with a smaller number of feature removals needed to change the label. Spectra for which a large number of features had to be removed before the label changed were treated as high-confidence predictions and admitted to a co-training process. Thresholds on this flip count were defined separately for spectrally ambiguous and stable classes to manage uncertainty identification effectively. To address the sensitivity of PLS-DA to class imbalances, equal proportions of pseudo-labels from each class were included in co-training. The proposed method was evaluated on the publicly available EMSLIBS dataset, achieving a test accuracy of 92.69\%, competitive with the best-performing methods in the literature. The XAI results provided insights into the causes of misclassifications and identified the dominant spectral features critical for accurate predictions, advancing interpretable AI solutions for LIBS-based soil analysis.