Abstract
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.
Highlights
Precision agriculture (PA) requires reliable, affordable, immediately available soil data with sufficient spatial and temporal resolution [1]
Agreement of the results is much better, in the low mass fraction region, a larger spread of the data points is observed. This kind of calibration is satisfactory for the major nutrients Ca and Mg but the predictive power is limited for the minor nutrient Fe
laser-induced breakdown spectroscopy (LIBS) is a promising method for efficient soil data collection at high spatial resolution, as required for precision agriculture
Summary
Precision agriculture (PA) requires reliable, affordable, immediately available soil data with sufficient spatial and temporal resolution [1]. Sensors 2019, 19, 5244 neither cover the whole range of soil fertility parameters nor do they directly measure them. The automated mobile proximal soil sensing systems frequently used in practical PA as well as research include geoelectrical, potentiometric pH, gamma-ray and spectral-optical sensors. PH electrodes most directly access a soil fertility parameter, namely active acidity [8]. Gamma-ray sensors detect radiation from K decay and from other isotopes This can be correlated with K in clay minerals and with plant available K+ [9,10]. Spectral-optical sensors include spectrometers and multi-wavelength sensors in the visible and near-infrared region. Visible and near-infrared spectrometers were used to map several fertility parameters at the same time [12,13]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
