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
44007
2019
2019
eng
16
786
postprint
1
2019-12-05
2019-12-05
--
Comparison of Calibration Approaches in Laser-Induced Breakdown Spectroscopy for Proximal Soil Sensing in Precision Agriculture
The lack of soil data, which are relevant, reliable, affordable, immediately available, and sufficiently detailed, is still a significant challenge in precision agriculture. A promising technology for the spatial assessment of the distribution of chemical elements within fields, without sample preparation is laser-induced breakdown spectroscopy (LIBS). Its advantages are contrasted by a strong matrix dependence of the LIBS signal which necessitates careful data evaluation. In this work, different calibration approaches for soil LIBS data are presented. The data were obtained from 139 soil samples collected on two neighboring agricultural fields in a quaternary landscape of northeast Germany with very variable soils. Reference analysis was carried out by inductively coupled plasma optical emission spectroscopy after wet digestion. The major nutrients Ca and Mg and the minor nutrient Fe were investigated. Three calibration strategies were compared. The first method was based on univariate calibration by standard addition using just one soil sample and applying the derived calibration model to the LIBS data of both fields. The second univariate model derived the calibration from the reference analytics of all samples from one field. The prediction is validated by LIBS data of the second field. The third method is a multivariate calibration approach based on partial least squares regression (PLSR). The LIBS spectra of the first field are used for training. Validation was carried out by 20-fold cross-validation using the LIBS data of the first field and independently on the second field data. The second univariate method yielded better calibration and prediction results compared to the first method, since matrix effects were better accounted for. PLSR did not strongly improve the prediction in comparison to the second univariate method.
Postprints der Universität Potsdam Mathematisch-Naturwissenschaftliche Reihe
10.25932/publishup-44007
urn:nbn:de:kobv:517-opus4-440079
1866-8372
5244
Sensors 19 (2019) 23, Art. 5244 DOI: 10.3390/s19235244
<a href="http://publishup.uni-potsdam.de/44006">Bibliographieeintrag der Originalveröffentlichung/Quelle</a>
CC-BY - Namensnennung 4.0 International
Daniel Riebe
Alexander Erler
Pia Brinkmann
Toralf Beitz
Hans-Gerd Löhmannsröben
Robin Gebbers
Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe
786
eng
uncontrolled
laser-induced breakdown spectroscopy
eng
uncontrolled
LIBS
eng
uncontrolled
proximal soil sensing
eng
uncontrolled
soil nutrients
eng
uncontrolled
elemental composition
Ingenieurwissenschaften und zugeordnete Tätigkeiten
open_access
Institut für Chemie
Referiert
Open Access
Universität Potsdam
https://publishup.uni-potsdam.de/files/44007/pmnr786.pdf
44006
2019
2019
eng
16
23
19
article
MDPI
Basel
1
2019-11-28
2019-11-28
--
Comparison of Calibration Approaches in Laser-Induced Breakdown Spectroscopy for Proximal Soil Sensing in Precision Agriculture
The lack of soil data, which are relevant, reliable, affordable, immediately available, and sufficiently detailed, is still a significant challenge in precision agriculture. A promising technology for the spatial assessment of the distribution of chemical elements within fields, without sample preparation is laser-induced breakdown spectroscopy (LIBS). Its advantages are contrasted by a strong matrix dependence of the LIBS signal which necessitates careful data evaluation. In this work, different calibration approaches for soil LIBS data are presented. The data were obtained from 139 soil samples collected on two neighboring agricultural fields in a quaternary landscape of northeast Germany with very variable soils. Reference analysis was carried out by inductively coupled plasma optical emission spectroscopy after wet digestion. The major nutrients Ca and Mg and the minor nutrient Fe were investigated. Three calibration strategies were compared. The first method was based on univariate calibration by standard addition using just one soil sample and applying the derived calibration model to the LIBS data of both fields. The second univariate model derived the calibration from the reference analytics of all samples from one field. The prediction is validated by LIBS data of the second field. The third method is a multivariate calibration approach based on partial least squares regression (PLSR). The LIBS spectra of the first field are used for training. Validation was carried out by 20-fold cross-validation using the LIBS data of the first field and independently on the second field data. The second univariate method yielded better calibration and prediction results compared to the first method, since matrix effects were better accounted for. PLSR did not strongly improve the prediction in comparison to the second univariate method.
Sensors
10.3390/s19235244
1424-8220
5244
Universität Potsdam
PA 2019_128
1460.89
<a href="https://doi.org/10.25932/publishup-44007">Zweitveröffentlichung in der Schriftenreihe Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe ; 786</a>
false
false
CC-BY - Namensnennung 4.0 International
Daniel Riebe
Alexander Erler
Pia Brinkmann
Toralf Beitz
Hans-Gerd Löhmannsröben
Robin Gebbers
eng
uncontrolled
laser-induced breakdown spectroscopy
eng
uncontrolled
LIBS
eng
uncontrolled
proximal soil sensing
eng
uncontrolled
soil nutrients
eng
uncontrolled
elemental composition
Ingenieurwissenschaften und zugeordnete Tätigkeiten
open_access
Institut für Chemie
Referiert
Publikationsfonds der Universität Potsdam
Open Access