44417
2020
2020
eng
17
2
20
article
MDPI
Basel
1
2020-01-11
2020-01-11
--
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 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.
Sensors
10.3390/s20020418
1424-8220
Universität Potsdam
PA 2020_006
1243.51
418
<a href="https://doi.org/10.25932/publishup-44418">Zweitveröffentlichung in der Schriftenreihe Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe ; 815</a>
CC-BY - Namensnennung 4.0 International
Alexander Erler
Daniel Riebe
Toralf Beitz
Hans-Gerd Löhmannsröben
Robin Gebbers
eng
uncontrolled
LIBS
eng
uncontrolled
lasso
eng
uncontrolled
PLS regression
eng
uncontrolled
gaussian processes
eng
uncontrolled
soil
eng
uncontrolled
precision agriculture
eng
uncontrolled
nutrients
Ingenieurwissenschaften und zugeordnete Tätigkeiten
open_access
Institut für Chemie
Referiert
Publikationsfonds der Universität Potsdam
Open Access
44418
2019
2020
eng
19
815
postprint
1
2020-02-06
2020-02-06
--
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 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.
Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe
10.25932/publishup-44418
urn:nbn:de:kobv:517-opus4-444183
1866-8372
Sensors 20 (2020) 2, 418 DOI: 10.3390/s20020418
418
<a href="http://publishup.uni-potsdam.de/44417">Bibliographieeintrag der Originalveröffentlichung/Quelle</a>
false
false
CC-BY - Namensnennung 4.0 International
Alexander Erler
Daniel Riebe
Toralf Beitz
Hans-Gerd Löhmannsröben
Robin Gebbers
Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe
815
eng
uncontrolled
LIBS
eng
uncontrolled
lasso
eng
uncontrolled
PLS regression
eng
uncontrolled
gaussian processes
eng
uncontrolled
soil
eng
uncontrolled
precision agriculture
eng
uncontrolled
nutrients
Ingenieurwissenschaften und zugeordnete Tätigkeiten
open_access
Institut für Chemie
Referiert
Open Access
Universität Potsdam
https://publishup.uni-potsdam.de/files/44418/pmnr815.pdf