Determination of soil texture using laser-induced breakdown spectroscopy and partial least squares regression

Soil texture is an important agricultural characteristic that drives effective crop production and field management. Laser-induced breakdown spectroscopy (LIBS) has been proposed to determine soil texture. LIBS has fewer sample preparation requirements compared with laboratory methods and has become a promising technology in soil analysis. However, the particle size of soil components affects LIBS signals, which leads to variations in LIBS emission lines. Current research indicates that LIBS can underperform in estimation uncertainties of soil texture proportions compared to some laboratory methods, such as the pipette method. To enhance LIBS effectiveness for soil texture analysis, we propose the implementation of the following strategies: 1) focusing on the collection of emissions lines from a shorter wavelength region [190 nm, 300 nm] with a high-resolution spectrometer (0.01 nm); 2) selection of the most important emission lines using partial least square coefficients. These strategies were found to reduce the spectral dimension and improve estimation performance. To highlight the efficiency and accuracy of our method, we use our experimental data to compare with literature methods that explore the full LIBS spectra and analyze spectral peaks separately. Employing repeated k-fold cross-validation to evaluate the root mean square error (RMSE) and coefficient of determination (${\mathrm {R}}^{2}$), we find that the literature methods using full spectrum and spectral area yield an average ${\mathrm {R}}^{2}$ of 0.76, RMSE $1.25 \%$, and ${\mathrm {R}} ^{\wedge} 20.79$, RMSE $1.16 \%$ respectively. In contrast, the proposed method achieves an average ${\mathrm {R}}^{2}$ of 0.97 and RMSE of $0.37 \%$ for soil textures. These results demonstrate that the soil texture can be accurately estimated with LIBS using the proposed method, thereby revealing the potential of LIBS in real-world applications for soil texture analysis.