Rapid fertilizer nutrient analysis enabled by handheld LIBS (hLIBS) and an intelligent Euclidean distance prediction...

Conventional methods for detecting fertilizer nutrient content are time-consuming and labor-intensive, which limits rapid on-site analysis. This study presents a rapid method for the rapid detection of nitrogen (N), phosphorus (P), and potassium (K) in compound fertilizers using laser-induced breakdown spectroscopy (LIBS). Within the 190-950 nm wavelength range, the LIBS spectra exhibited intense, characteristic emission lines for N, P, and K with high signal-to-background ratios. A sorted feature matrix was constructed from the spectral data using a Euclidean distance algorithm and subsequently used to build a partial least squares regression (PLSR) model. The optimized PLSR model achieved rapid and accurate predictions, and its performance was evaluated using the coefficient of determination (R²), root mean square error (RMSE), and residual predictive deviation (RPD). The results were as follows: N (R² = 0.9828, RMSE = 0.44 g kg^-1, RPD = 7.46), P (R² = 0.9541, RMSE = 1.02 g kg^-1, RPD = 4.64), and K (R² = 0.9610, RMSE = 0.90 g kg^-1, RPD = 5.06), with all prediction errors below 6 %. This method provides an efficient and practical solution for real-time, on-site nutrient detection, demonstrating significant potential for application by fertilizer producers, regulators, and agricultural agencies to enhance nutrient use efficiency and support sustainable agricultural practices.