Multi-Element Prediction of Soil Nutrients Using Laser-Induced Breakdown Spectroscopy and Interpretable Multi-Output Weight Network

Rapid and green detection of soil nutrients is essential for soil fertility and plant growth. However, traditional methods cannot meet the needs of rapid detection, and the reagents easily cause environmental pollution. Hence, we proposed a multivariable output weighting-network (MW-Net) combined with laser-induced breakdown spectroscopy (LIBS) to achieve rapid and green detection for three soil nutrients. For a better spectral signal-to-background ratio (SBR), the two important parameters of delay time and gate width were optimized. Then, the spectral noise was removed by the near-zero standard deviation method. Three common quantitative models were investigated for single-element prediction, which are usually applied in LIBS analysis. Also, multi-element prediction was investigated using MW-Net. The results showed that MW-Net outperformed other models generally with very good quantification for soil total N and K (the determination coefficients in the prediction set (Rp2) of 0.75 and 0.83 and the relative percent difference in the prediction sets (RPD) of 2.05 and 2.43) and excellent indirect determination for soil exchangeable Ca (Rp2 of 0.93 and RPD of 3.91). Finally, the interpretability was realized through feature extraction from MW-Net, indicating its design rationality. The preliminary results indicated that MW-Net combined with LIBS technology could quantify the three soil nutrients simultaneously, improving the detection efficiency, and it could possibly be deployed on a LIBS portable instrument in the future for precision agriculture.