Abstract
Precision agriculture (PA) strongly relies on spatially differentiated sensor information. Handheld instruments based on laser-induced breakdown spectroscopy (LIBS) are a promising sensor technique for the in-field determination of various soil parameters. In this work, the potential of handheld LIBS for the determination of the total mass fractions of the major nutrients Ca, K, Mg, N, P and the trace nutrients Mn, Fe was evaluated. Additionally, other soil parameters, such as humus content, soil pH value and plant available P content, were determined. Since the quantification of nutrients by LIBS depends strongly on the soil matrix, various multivariate regression methods were used for calibration and prediction. These include partial least squares regression (PLSR), least absolute shrinkage and selection operator regression (Lasso), and Gaussian process regression (GPR). The best prediction results were obtained for Ca, K, Mg and Fe. The coefficients of determination obtained for other nutrients were smaller. This is due to much lower concentrations in the case of Mn, while the low number of lines and very weak intensities are the reason for the deviation of N and P. Soil parameters that are not directly related to one element, such as pH, could also be predicted. Lasso and GPR yielded slightly better results than PLSR. Additionally, several methods of data pretreatment were investigated.
Highlights
Soils usually exhibit an inhomogeneous distribution of chemical, physical and biological soil properties
The focus of this work was the characterization of the potential of a handheld laser-induced breakdown spectroscopy (LIBS) instrument
The focus of this work was the characterization of the potential of a handheld LIBS instrument for for the determination of a broad range of major (Ca, Mg, K, P and N), and minor (Mn and Fe) nutrients the determination of a broad range of major (Ca, Mg, K, P and N), and minor (Mn and Fe) nutrients in in soils
Summary
Soils usually exhibit an inhomogeneous distribution of chemical, physical and biological soil properties. On agricultural land, this creates spatial variations of the qualities relevant for soil fertility and related management measures such as tillage, seeding, and fertilization. The usual, uniform fertilization of the fields can lead to partial over-or underdosing. LIBS spectra contain a large number of data points that may not all be relevant. Standard data pretreatment of real-world LIBS spectra currently consists of background correction and averaging in most cases, and normalization in many. The influence of background correction and normalization on the performance of the three multivariate methods was evaluated in relation to the averaged raw spectra. Corrected, Normalized and Averaged Spectra Lasso (0.68) (0.59) (0.82) (0.87) (0.85) (0.89).
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