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

Abstract

Rapid quantification of soil organic matter (SOM) is a great challenge for the health assessment and fertility management of agricultural soil. Laser-induced breakdown spectroscopy (LIBS) with appropriate modeling algorithms is an alternative tool for this measurement. However, the current calibration strategy limits the prediction performance of the LIBS technique. In this study, 563 soil samples from Hetao Irrigation District in China were collected; the LIBS spectra of the soils were recorded in the wavenumber range of 288–950 nm with a resolution of 0.116 nm; a self-adaptive partial least squares regression model (SAM–PLSR) was employed to explore optimal model parameters for SOM prediction; and calibration parameters including sample selection for the calibration database, sample numbers and sample location sites were optimized. The results showed that the sample capacity around 60–80, rather than all of the samples in the soil library database, was selected for calibration from a spectral similarity re-ordered database regarding unknown samples; the model produced excellent predictions, with R2 = 0.92, RPD = 3.53 and RMSEP = 1.03 g kg−1. Both the soil variances of the target property and the spectra similarity of the soil background were the key factors for the calibration model, and the small sample set led to poor predictions due to the low variances of the target property, while negative effects were observed for the large sample set due to strong interferences from the soil background. Therefore, the specific unknown sample depended strategy, i.e., self-adaptive modelling, could be applied for fast SOM sensing using LIBS for soils in varied scales with improved robustness and accuracy.

Factors affecting the biomass pellet using industrial eucalyptus bark residue

“…APS and MC were values obtained by analyzing the equation with the studied factors, BU, DU, and Fi, which had R 2 values of 0.735, 0.858, and 94.4, respectively. In general, good models have high R 2 values (close to or equal to 1) [ 61 ].…”

Advancing Soil Health: Challenges and Opportunities in Integrating Digital Imaging, Spectroscopy, and Machine Learning for Bioindicator Analysis

“…There are many overviews of machine learning applications in LIBS analysis. , Hu et al utilized a mobile LIBS system (mobileLIBS) with a spectral range of 288–950 nm to measure SOM. The system was calibrated using 563 soil samples, with SOM determined via potassium dichromate oxidation colorimetry.…”