@article{ErlerRiebeBeitzetal.2020,
  author    = {Erler, Alexander and Riebe, Daniel and Beitz, Toralf and L{\"o}hmannsr{\"o}ben, Hans-Gerd and Gebbers, Robin},
  title     = {Soil Nutrient Detection for Precision Agriculture Using Handheld Laser-Induced Breakdown Spectroscopy (LIBS) and Multivariate Regression Methods (PLSR, Lasso and GPR)},
  series = {Sensors},
  volume    = {20},
  journal   = {Sensors},
  number    = {2},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {1424-8220},
  doi       = {10.3390/s20020418},
  pages     = {17},
  year      = {2020},
  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.},
  language  = {en}
}
@misc{ErlerRiebeBeitzetal.2019,
  author    = {Erler, Alexander and Riebe, Daniel and Beitz, Toralf and L{\"o}hmannsr{\"o}ben, Hans-Gerd and Gebbers, Robin},
  title     = {Soil Nutrient Detection for Precision Agriculture Using Handheld Laser-Induced Breakdown Spectroscopy (LIBS) and Multivariate Regression Methods (PLSR, Lasso and GPR)},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {815},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-44418},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-444183},
  pages     = {19},
  year      = {2019},
  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.},
  language  = {en}
}
@misc{LemkeFernandezTrujilloLoehmannsroeben2005,
  author    = {Lemke, Matthias and Fern{\´a}ndez-Trujillo, Rebeca and L{\"o}hmannsr{\"o}ben, Hans-Gerd},
  title     = {In-situ LIF analysis of biological and petroleum-based hydraulic oils on soil},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-12268},
  year      = {2005},
  abstract  = {Absorption and fluorescence properties of 4 hydraulic oils (3 biological and 1 petroleum-based) were investigated. In-situ LIF (laser-induced fluorescence) analysis of the oils on a brown sandy loam soil was performed. With calibration, quantitative detection was achieved. Estimated limits of detection were below ca. 500 mg/kg for the petroleum-based oil and ca. 2000 mg/kg for one biological oil. A semi-quantitative classification scheme is proposed for monitoring of the biological oils. This approach was applied to investigate the migration of a biological oil in soil-containing compartments, namely a soil column and a soil bed.},
  language  = {en}
}