Abstract
Rapid, cost-effective, and environmentally friendly analysis of key soil fertility attributes requires an ideal combination of sensors. The individual and combined performance of visible and near infrared (VNIR) diffuse reflectance spectroscopy, X-ray fluorescence spectroscopy (XRF), and laser-induced breakdown spectroscopy (LIBS) was assessed for predicting clay, organic matter (OM), cation exchange capacity (CEC), pH, base saturation (V), and extractable (ex-) nutrients in tropical soils. A set of 102 samples, collected from two agricultural fields, with broad ranges of fertility attributes were selected. Two contrasting data fusion approaches have been applied for modeling: (i) merging spectral data of different sensors followed by partial least squares regression (PLS), known as fusion before prediction; and (ii) applying the Granger and Ramanathan (GR) averaging approach, known as fusion after prediction. Results showed VNIR as individual technique to be the best for the prediction of clay and OM content (2.61 ≤ residual prediction deviation (RPD) ≤ 3.37), while the chemical attributes CEC, V, ex-P, ex-K, ex-Ca, and ex-Mg were better predicted (1.82 ≤ RPD ≤ 4.82) by elemental analysis techniques (i.e., XRF and LIBS). Only pH cannot be predicted regardless the technique. The attributes OM, V, and ex-P were best predicted using single-sensor approaches, while the attributes clay, CEC, pH, ex-K, ex-Ca, and ex-Mg were overall best predicted using multi-sensor approaches. Regarding the performance of the multi-sensor approaches, ex-K, ex-Ca, and ex-Mg, were best predicted (RPD of 4.98, 5.30, and 4.11 for ex-K, ex-Ca and ex-Mg, respectively) using two-sensor fusion approach (VNIR + XRF for ex-K and XRF + LIBS for ex-Ca and ex-Mg), while clay, CEC and pH were best predicted (RPD of 4.02, 2.63, and 1.32 for clay, CEC, and pH, respectively) with the three-sensor fusion approach (VNIR + XRF + LIBS). Therefore, the best combination of sensors for predicting key fertility attributes proved to be attribute-specific, which is a drawback of the data fusion approach. The present work is pioneering in highlighting benefits and limitations of the in tandem application of VNIR, XRF, and LIBS spectroscopies for fertility analysis in tropical soils.
Highlights
Spectro-analytical techniques that allow direct analysis of samples are promising for environmentally friendly soil fertility characterization [1]
This study shows that the fusion of data from visible and near infrared diffuse reflectance spectroscopy (VNIR), X-ray fluorescence spectroscopy (XRF), and laser-induced breakdown spectroscopy (LIBS) can extend the number of fertility attributes predicted with optimal performance, as well as improve the prediction accuracy by exploiting the synergy through data fusion techniques
The LIBS stood out for V, ex-P and ex-Mg prediction, while the XRF resulted in optimal predictions for cation exchange capacity (CEC), ex-K, and ex-Ca
Summary
Spectro-analytical techniques that allow direct analysis of samples are promising for environmentally friendly soil fertility characterization [1]. Integrating these sensing techniques with laboratory analytical methods in hybrid laboratories [2] can modernize the traditional methods for soil analysis [3] that will enable a new paradigm in soil management. The maturation of direct analysis techniques—i.e., those requiring minimal or no sample preparation—would enable more accurate mapping of agricultural fields by means of in situ or mobile laboratory analysis [4]. Multi-sensor approaches allow the integration of information related to distinct soil properties, yielding more comprehensive and accurate characterizations of key soil fertility attributes [9,10,11]
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