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Soil Nutrient Detection for Precision Agriculture Using Handheld Laser-Induced Breakdown Spectroscopy (LIBS) and Multivariate Regression Methods (PLSR, Lasso and GPR)

  • 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 muchPrecision 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.show moreshow less

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Author details:Alexander ErlerORCiDGND, Daniel RiebeORCiDGND, Toralf BeitzORCiD, Hans-Gerd LöhmannsröbenORCiDGND, Robin GebbersORCiDGND
URN:urn:nbn:de:kobv:517-opus4-444183
DOI:https://doi.org/10.25932/publishup-44418
ISSN:1866-8372
Title of parent work (German):Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe
Publication series (Volume number):Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe (815)
Publication type:Postprint
Language:English
Date of first publication:2020/02/06
Publication year:2019
Publishing institution:Universität Potsdam
Release date:2020/02/06
Tag:LIBS; PLS regression; gaussian processes; lasso; nutrients; precision agriculture; soil
Issue:815
Number of pages:19
Source:Sensors 20 (2020) 2, 418 DOI: 10.3390/s20020418
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Chemie
DDC classification:6 Technik, Medizin, angewandte Wissenschaften / 62 Ingenieurwissenschaften / 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
Peer review:Referiert
Publishing method:Open Access
License (German):License LogoCC-BY - Namensnennung 4.0 International
External remark:Bibliographieeintrag der Originalveröffentlichung/Quelle
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