TY  - JOUR
A1  - Erler, Alexander
A1  - Riebe, Daniel
A1  - Beitz, Toralf
A1  - Löhmannsröben, Hans-Gerd
A1  - Gebbers, Robin
T1  - Soil Nutrient Detection for Precision Agriculture Using Handheld Laser-Induced Breakdown Spectroscopy (LIBS) and Multivariate Regression Methods (PLSR, Lasso and GPR)
JF  - Sensors
N2  - 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.
KW  - LIBS
KW  - lasso
KW  - PLS regression
KW  - gaussian processes
KW  - soil
KW  - precision agriculture
KW  - nutrients
Y1  - 2020
U6  - https://doi.org/10.3390/s20020418
SN  - 1424-8220
VL  - 20
IS  - 2
PB  - MDPI
CY  - Basel
ER  - 
TY  - JOUR
A1  - Wojcik, Michal
A1  - Brinkmann, Pia
A1  - Zdunek, Rafał
A1  - Riebe, Daniel
A1  - Beitz, Toralf
A1  - Merk, Sven
A1  - Cieslik, Katarzyna
A1  - Mory, David
A1  - Antonczak, Arkadiusz
T1  - Classification of copper minerals by handheld laser-induced breakdown spectroscopy and nonnegative tensor factorisation
JF  - Sensors
N2  - Laser-induced breakdown spectroscopy (LIBS) analysers are becoming increasingly common for material classification purposes. However, to achieve good classification accuracy, mostly noncompact units are used based on their stability and reproducibility. In addition, computational algorithms that require significant hardware resources are commonly applied. For performing measurement campaigns in hard-to-access environments, such as mining sites, there is a need for compact, portable, or even handheld devices capable of reaching high measurement accuracy. The optics and hardware of small (i.e., handheld) devices are limited by space and power consumption and require a compromise of the achievable spectral quality. As long as the size of such a device is a major constraint, the software is the primary field for improvement. In this study, we propose a novel combination of handheld LIBS with non-negative tensor factorisation to investigate its classification capabilities of copper minerals. The proposed approach is based on the extraction of source spectra for each mineral (with the use of tensor methods) and their labelling based on the percentage contribution within the dataset. These latent spectra are then used in a regression model for validation purposes. The application of such an approach leads to an increase in the classification score by approximately 5% compared to that obtained using commonly used classifiers such as support vector machines, linear discriminant analysis, and the k-nearest neighbours algorithm.
KW  - LIBS
KW  - NTF
KW  - HALS
KW  - classification
KW  - copper minerals
Y1  - 2020
U6  - https://doi.org/10.3390/s20185152
SN  - 1424-8220
VL  - 20
IS  - 18
PB  - MDPI
CY  - Basel
ER  -