Soil analysis with machine-learning-based processing of stepped-frequency GPR field measurements: Preliminary study
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909620464779264 |
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| author | Xu, Chunlei Pregesbauer, Michael Chilukuri, Naga Sravani Windhager, Daniel Yousefi, Mahsa Julian, Pedro Ratschbacher, Lothar |
| author_facet | Xu, Chunlei Pregesbauer, Michael Chilukuri, Naga Sravani Windhager, Daniel Yousefi, Mahsa Julian, Pedro Ratschbacher, Lothar |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_15961 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Soil analysis with machine-learning-based processing of stepped-frequency GPR field measurements: Preliminary study Xu, Chunlei Pregesbauer, Michael Chilukuri, Naga Sravani Windhager, Daniel Yousefi, Mahsa Julian, Pedro Ratschbacher, Lothar Signal Processing Artificial Intelligence 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. |
| title | Soil analysis with machine-learning-based processing of stepped-frequency GPR field measurements: Preliminary study |
| topic | Signal Processing Artificial Intelligence |
| url | https://arxiv.org/abs/2404.15961 |