Soil analysis with machine-learning-based processing of stepped-frequency GPR field measurements: Preliminary study

TL;DR

This study explores the use of machine learning to analyze stepped-frequency GPR data for soil parameter estimation, demonstrating potential for cost-effective, depth-resolved soil analysis in agricultural settings.

Contribution

It introduces a novel approach combining ML with SFCW GPR measurements for soil analysis and evaluates its effectiveness through extensive field data collection.

Findings

ML can predict apparent electrical conductivity from GPR data

Nugget-to-sill ratio is useful for model performance evaluation

Large-scale field data supports the feasibility of this method

Abstract

Ground Penetrating Radar (GPR) has been widely studied as a tool for extracting soil parameters relevant to agriculture and horticulture. When combined with Machine Learning (ML) methods, air-coupled Stepped Frequency Continuous Wave Ground Penetrating Radar (SFCW GPR) measurements could offer a cost-effective way to obtain depth-resolved soil data. As a first step of our study in this direction, we conducted an extensive field survey using a tractor-mounted air-coupled SFCW GPR instrument. Leveraging ML-based data processing, we evaluate the GPR instrument's ability by predicting the apparent electrical conductivity (ECaR) measured by a co-recorded Electromagnetic Induction (EMI) instrument. The large-scale field measurement campaign with 3472 co-registered and geo-located GPR and EMI data samples distributed over approximately 6600 square meters was performed on a golf course. This terrain offers high surface homogeneity but also presents the challenge of subtle soil parameter variations. Based on the results, we discuss challenges in this multi-sensor regression setting and propose the use of the nugget-to-sill ratio as a performance metric for evaluating ML models in agricultural field survey applications.