Mobile Laser-Induced Breakdown Spectroscopy for Future Application in Precision Agriculture—A Case Study
Abstract
:1. Introduction
2. Experimental Part and Data Analysis
2.1. Materials
2.2. Reference Analysis
2.3. LIBS Measurements
2.4. Data Analysis
3. Results
3.1. Determination of Soil Parameters
3.2. Matrix Influence
3.3. Different Aspects of Measurement and Data Analysis
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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SP | Method | Median | IQR | Range (90%) | R2 (CV) | R2 (Val) | RMSECV | RMSEV |
---|---|---|---|---|---|---|---|---|
Ca | GP | 0.59 | 0.21 | 0.50 | 0.86 | 0.86 | 0.0645 | 0.0775 |
Mg | GP | 0.65 | 0.16 | 0.41 | 0.80 | 0.79 | 0.0561 | 0.0602 |
K | GP | 1.71 | 0.43 | 1.02 | 0.92 | 0.92 | 0.0901 | 0.0655 |
P | GP | 0.09 | 0.04 | 0.10 | 0.77 | 0.82 | 0.0174 | 0.0184 |
N | GP | 0.13 | 0.02 | 0.06 | 0.62 | 0.71 | 0.0118 | 0.011 |
Fe | GP | 3.51 | 1.71 | 4.52 | 0.93 | 0.92 | 0.3489 | 0.431 |
Mn | PLS | 0.10 | 0.04 | 0.10 | 0.79 | 0.77 | 0.0189 | 0.0203 |
Zn | PLS | 0.01 | 0.01 | 0.02 | 0.87 | 0.84 | 0.0018 | 0.002 |
Cu | PLS | 0.003 | 0.002 | 0.006 | 0.87 | 0.85 | 6.62 × 10−4 | 7.11 × 10−4 |
SOM | GP | 2.39 | 0.57 | 1.43 | 0.67 | 0.71 | 0.2583 | 0.227 |
pH | GP | 6.07 | 0.27 | 1.15 | 0.54 | 0.57 | 0.2203 | 0.2343 |
Silt | GP | 58.57 | 12.34 | 28.43 | 0.83 | 0.89 | 3.4688 | 3.1757 |
Clay | GP | 26.51 | 11.86 | 28.84 | 0.91 | 0.91 | 2.6018 | 3.0933 |
Soil Parameter | HH | Lab/10 mJ | Lab/40 mJ | PF/25 mJ | PF/40 mJ |
---|---|---|---|---|---|
R2 (CV) | |||||
Ca | 0.85 | 0.74 | 0.86 | 0.91 | 0.95 |
K | 0.96 | 0.92 | 0.92 | 0.86 | 0.95 |
Mg | 0.88 | 0.60 | 0.79 | 0.89 | 0.84 |
P | 0.69 | 0.63 | 0.82 | 0.75 | 0.62 |
N | 0.59 | 0.64 | 0.71 | 0.30 | 0.59 |
Mn | 0.69 | 0.68 | 0.77 | 0.40 | 0.94 |
Fe | 0.94 | 0.87 | 0.92 | 0.87 | 0.88 |
SOM | 0.55 | 0.63 | 0.71 | 0.65 | 0.35 |
soil pH | −0.06 | 0.10 | 0.57 | 0.31 | 0.55 |
silt | 0.86 | 0.84 | 0.89 | 0.85 | 0.83 |
clay | 0.83 | 0.86 | 0.91 | 0.93 | 0.94 |
PLS | PCR | SVM | GP | |||||
---|---|---|---|---|---|---|---|---|
Soil Parameter | R2 (CV) | R2 (Val) | R2 (CV) | R2 (Val) | R2 (CV) | R2 (Val) | R2 (CV) | R2 (Val) |
Ca | 0.86 | 0.84 | 0.82 | 0.82 | 0.82 | 0.82 | 0.86 | 0.86 |
K | 0.92 | 0.91 | 0.92 | 0.93 | 0.91 | 0.93 | 0.92 | 0.92 |
Mg | 0.77 | 0.74 | 0.74 | 0.76 | 0.74 | 0.76 | 0.8 | 0.79 |
P | 0.72 | 0.69 | 0.71 | 0.71 | 0.73 | 0.73 | 0.77 | 0.82 |
N | 0.49 | 0.48 | 0.55 | 0.68 | 0.64 | 0.7 | 0.67 | 0.75 |
Cu | 0.87 | 0.87 | 0.87 | 0.86 | 0.65 | 0.56 | 0.81 | 0.76 |
Mn | 0.81 | 0.85 | 0.72 | 0.78 | 0.72 | 0.85 | 0.72 | 0.76 |
Zn | 0.85 | 0.76 | 0.82 | 0.69 | 0.58 | 0.59 | 0.82 | 0.66 |
Fe | 0.93 | 0.98 | 0.93 | 0.92 | 0.93 | 0.92 | 0.93 | 0.92 |
Humus | 0.64 | 0.62 | 0.63 | 0.69 | 0.62 | 0.74 | 0.67 | 0.71 |
pH | 0.54 | 0.39 | 0.48 | 0.52 | 0.52 | 0.49 | 0.54 | 0.57 |
Silt | 0.88 | 0.86 | 0.83 | 0.88 | 0.84 | 0.89 | 0.83 | 0.89 |
Clay | 0.93 | 0.9 | 0.92 | 0.89 | 0.92 | 0.9 | 0.91 | 0.91 |
Mean (K, Ca, Mg) | 0.85 | 0.83 | 0.83 | 0.84 | 0.82 | 0.84 | 0.86 | 0.86 |
Mean (MN) | 0.75 | 0.73 | 0.75 | 0.78 | 0.77 | 0.79 | 0.80 | 0.83 |
Mean (TN) | 0.87 | 0.87 | 0.84 | 0.81 | 0.72 | 0.73 | 0.82 | 0.78 |
Mean (SP) | 0.75 | 0.69 | 0.72 | 0.75 | 0.73 | 0.76 | 0.74 | 0.77 |
Mean | 0.79 | 0.76 | 0.76 | 0.78 | 0.74 | 0.76 | 0.79 | 0.79 |
SP | Method | Features | R2 (CV) | R2 (Val) | SP | Method | Features | R2 (CV) | R2 (Val) |
---|---|---|---|---|---|---|---|---|---|
Ca | PLS | 37,633 | 0.86 | 0.84 | N | PLS | 37,633 | 0.49 | 0.48 |
PCA | 242 | 0.87 | 0.85 | PCA | 242 | 0.58 | 0.56 | ||
PCA | 17 | 0.86 | 0.84 | PCA | 17 | 0.57 | 0.56 | ||
CARS | 1012/61 | 0.80 | 0.84 | CARS | 5/8 | 0.56 | 0.64 | ||
Lasso | 14 | 0.65 | Lasso | 36 | 0.44 | ||||
Mg | PLS | 37,633 | 0.77 | 0.74 | SOM | PLS | 37,633 | 0.64 | 0.62 |
PCA | 242 | 0.80 | 0.78 | PCA | 242 | 0.65 | 0.65 | ||
PCA | 17 | 0.79 | 0.76 | PCA | 17 | 0.65 | 0.65 | ||
CARS | 22/61 | 0.90 | 0.80 | CARS | 12/7 | 0.64 | 0.69 | ||
Lasso | 9 | 0.53 | Lasso | 12 | 0.46 | ||||
K | PLS | 37,633 | 0.92 | 0.91 | Silt | PLS | 37,633 | 0.88 | 0.86 |
PCA | 242 | 0.93 | 0.92 | PCA | 242 | 0.87 | 0.87 | ||
PCA | 17 | 0.92 | 0.91 | PCA | 17 | 0.87 | 0.85 | ||
CARS | 7/61 | 0.93 | 0.96 | CARS | 7/453 | 0.89 | 0.90 | ||
Lasso | 21 | 0.83 | Lasso | 13 | 0.73 | ||||
P | PLS | 37,633 | 0.72 | 0.69 | Clay | PLS | 37,633 | 0.93 | 0.90 |
PCA | 242 | 0.72 | 0.68 | PCA | 242 | 0.93 | 0.91 | ||
PCA | 17 | 0.72 | 0.61 | PCA | 17 | 0.92 | 0.90 | ||
CARS | 6172/4130 | 0.75 | 0.75 | CARS | 7545/22 | 0.92 | 0.91 | ||
Lasso | 11 | 0.40 | Lasso | 8 | 0.81 |
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Erler, A.; Riebe, D.; Beitz, T.; Löhmannsröben, H.-G.; Leenen, M.; Pätzold, S.; Ostermann, M.; Wójcik, M. Mobile Laser-Induced Breakdown Spectroscopy for Future Application in Precision Agriculture—A Case Study. Sensors 2023, 23, 7178. https://doi.org/10.3390/s23167178
Erler A, Riebe D, Beitz T, Löhmannsröben H-G, Leenen M, Pätzold S, Ostermann M, Wójcik M. Mobile Laser-Induced Breakdown Spectroscopy for Future Application in Precision Agriculture—A Case Study. Sensors. 2023; 23(16):7178. https://doi.org/10.3390/s23167178
Chicago/Turabian StyleErler, Alexander, Daniel Riebe, Toralf Beitz, Hans-Gerd Löhmannsröben, Mathias Leenen, Stefan Pätzold, Markus Ostermann, and Michal Wójcik. 2023. "Mobile Laser-Induced Breakdown Spectroscopy for Future Application in Precision Agriculture—A Case Study" Sensors 23, no. 16: 7178. https://doi.org/10.3390/s23167178