Multi-Sensor Soil Probe and Machine Learning Modeling for Predicting Soil Properties

• Published in: Sensors (Basel, Switzerland) Switzerland), 2024-10, Vol.24 (21), p.6855
• Main Authors: Grunwald, Sabine, Murad, Mohammad Omar Faruk, Farrington, Stephen, Wallace, Woody, Rooney, Daniel
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Carbon; digital soil core; digital soil mapping; digital twin; Electric properties; Infrared spectroscopy; Laboratories; Libraries; Machine learning; multi-sensor system; Potassium; Sensors; soil sensors; Spectrum analysis
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24216855

We present a data-driven, in situ proximal multi-sensor digital soil mapping approach to develop digital twins for multiple agricultural fields. A novel Digital Soil Core (DSC) Probe was engineered that contains seven sensors, each of a distinct modality, including sleeve friction, tip force, dielectric permittivity, electrical resistivity, soil imagery, acoustics, and visible and near-infrared spectroscopy. The DSC System integrates the DSC Probe, DSC software (v2023.10), and deployment equipment components to sense soil characteristics at a high vertical spatial resolution (mm scale) along in situ soil profiles up to a depth of 120 cm in about 60 s. The DSC Probe in situ proximal data are harmonized into a data cube providing vertical high-density knowledge associated with physical-chemical-biological soil conditions. In contrast, conventional ex situ soil samples derived from soil cores, soil pits, or surface samples analyzed using laboratory and other methods are bound by a substantially coarser spatial resolution and multiple compounding errors. Our objective was to investigate the effects of the mismatched scale between high-resolution in situ proximal sensor data and coarser-resolution ex situ soil laboratory measurements to develop soil prediction models. Our study was conducted in central California soil in almond orchards. We collected DSC sensor data and spatially co-located soil cores that were sliced into narrow layers for laboratory-based soil measurements. Partial Least Squares Regression (PLSR) cross-validation was used to compare the results of testing four data integration methods. Method A reduced the high-resolution sensor data to discrete values paired with layer-based soil laboratory measurements. Method B used stochastic distributions of sensor data paired with layer-based soil laboratory measurements. Method C allocated the same soil analytical data to each one of the high-resolution multi-sensor data within a soil layer. Method D linked the high-density multi-sensor soil data directly to crop responses (crop performance and behavior metrics), bypassing costly laboratory soil analysis. Overall, the soil models derived from Method C outperformed Methods A and B. Soil predictions derived using Method D were the most cost-effective for directly assessing soil-crop relationships, making this method well suited for industrial-scale precision agriculture applications.