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Updated: Jun 8, 2025

Manufacturing Simple and Inexpensive Soil Surface Temperature and Gravimetric Water Content Sensors

Manufacturing Simple and Inexpensive Soil Surface Temperature and Gravimetric Water Content Sensors

Published on: December 21, 2019

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

Sabine Grunwald¹, Mohammad Omar Faruk Murad², Stephen Farrington³

¹Pedometrics, Landscape Analysis & GIS Laboratory, Soil, Water, and Ecosystem Sciences Department, University of Florida, 2181 McCarty Hall, P.O. Box 110290, Gainesville, FL 32611, USA.

Sensors (Basel, Switzerland)

|November 9, 2024

Summary

This summary is machine-generated.

A novel Digital Soil Core (DSC) probe provides high-resolution, in situ soil data for digital twins. Method D, linking sensor data directly to crop responses, proved most cost-effective for precision agriculture.

Keywords:

artificial intelligence digital soil core digital soil mapping digital twin machine learning multi-sensor system scale soil properties soil sensors

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In Situ Soil Moisture Sensors in Undisturbed Soils

In Situ Soil Moisture Sensors in Undisturbed Soils

Published on: November 18, 2022

Area of Science:

Agricultural Engineering
Soil Science
Digital Agriculture

Background:

Traditional soil sampling is labor-intensive, costly, and provides limited spatial resolution.
Developing accurate digital soil maps requires integrating diverse data sources with varying resolutions.
Precision agriculture demands high-resolution soil data for optimized crop management.

Purpose of the Study:

To develop and evaluate a data-driven, in situ proximal multi-sensor approach for digital soil mapping.
To investigate the impact of scale mismatch between high-resolution sensor data and coarser laboratory measurements.
To compare four data integration methods for soil prediction modeling in almond orchards.

Main Methods:

Engineered a novel Digital Soil Core (DSC) Probe with seven distinct sensors (friction, force, dielectric, resistivity, imagery, acoustics, spectroscopy).
Collected in situ DSC sensor data and spatially co-located soil cores for laboratory analysis.
Applied Partial Least Squares Regression (PLSR) to compare four data integration methods (A, B, C, D) for soil prediction models.

Main Results:

Method C, allocating laboratory data to all sensor data within a layer, outperformed Methods A and B.
Method D, directly linking high-density sensor data to crop responses, bypassed laboratory costs and proved most cost-effective.
The DSC System provides mm-scale vertical soil data up to 120 cm depth in approximately 60 seconds.

Conclusions:

The DSC System enables high-density, in situ soil characterization for creating agricultural digital twins.
Directly linking multi-sensor soil data to crop performance (Method D) offers a cost-effective solution for precision agriculture.
This approach facilitates industrial-scale precision agriculture by providing actionable soil-crop relationship insights.