Soil nutrient detection for precision agriculture with handheld LIBS

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Publication: Erler, A.; Riebe, D.; Beitz, T.; Löhmannsröben, H.-G.; Gebbers, R. Soil Nutrient Detection for Precision Agriculture Using Handheld Laser-Induced Breakdown Spectroscopy (LIBS) and Multivariate Regression Methods (PLSR, Lasso and GPR). Sensors 2020, 20, 418.

A new study using SciAps Z-300 LIBS shows handheld laser induced breakdown spectroscopy to be a promising sensor technique for the in-field determination of various soil parameters. The key to this new approach is moving LIBS out of the lab and to the work site. Read our interview with one of the German researchers advancing agricultural analysis.

Abstract: Precision agriculture 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.

Keywords: LIBS; lasso; PLS regression; gaussian processes; soil; precision agriculture; nutrients

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About this publication: Sensors (ISSN 1424-8220; CODEN: SENSC9) is the leading international peer-reviewed open access journal on the science and technology of sensors. Sensors is published semi-monthly online by MDPI.

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