Comparison of Calibration Approaches in Laser-Induced Breakdown Spectroscopy for Proximal Soil Sensing in Precision Agriculture

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.

Hyperspectral Imaging Tera Hertz System for Soil Analysis: Initial Results

“…The development of soil sensors is a challenging task due to the complexity of the soil and its many interfering parameters. This is also true for proximal soil sensing [1,2]. To date, visible and infrared spectroscopy, electrical resistivity, gamma ray spectroscopy, and X-ray spectroscopy have been investigated extensively [2].…”

Sensing of Soil Organic Matter Using Laser-Induced Breakdown Spectroscopy Coupled with Optimized Self-Adaptive Calibration Strategy

“…Reliable chemometrics models are indispensable for spectroscopic methods, as they can maximize access to chemical property and spectral information by applying mathematics, statistics and computer science. The common chemometrics methods for soil analysis include multiple linear regression (MLR), partial least squares regression (PLSR), principal component analysis (PCA) and artificial neural networks (ANN), and they have achieved good predictions combining with LIBS [17][18][19][20].…”

Multi-Sensor Approach for Tropical Soil Fertility Analysis: Comparison of Individual and Combined Performance of VNIR, XRF, and LIBS Spectroscopies

“…Although VNIR, XRF, and LIBS techniques have been evaluated individually for predicting fertility attributes [16][17][18][19][20][21], the combination of these techniques is still at its early stages of development. Recent studies have evaluated different data fusion approaches for combining VNIR and XRF data for predicting fertility attributes [22][23][24].…”