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Machine Learning Models for Enhanced Estimation of Soil Moisture Using Wideband Radar Sensor.

Akileshwaran Uthayakumar1, Manoj Prabhakar Mohan1, Eng Huat Khoo2

  • 1School of Electrical and Electronic Engineering, Nanyang Technological University (NTU), Singapore 639798, Singapore.

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Summary

Machine learning models effectively estimate soil moisture content using radar sensors. Neural networks achieved the highest accuracy (R2=0.9894), offering a cost-effective solution for agriculturists.

Keywords:
KNNSVMlinear regressionmicrowave radarneural networksoil moisturevolumetric water content

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Area of Science:

  • Agricultural Engineering
  • Remote Sensing
  • Machine Learning

Background:

  • Accurate soil moisture monitoring is crucial for efficient agricultural water management.
  • Traditional methods can be labor-intensive or lack spatial coverage.
  • Developing cost-effective, non-contact sensing technologies is essential.

Purpose of the Study:

  • To propose and evaluate machine learning models for estimating soil moisture content.
  • To utilize a microwave short-range, wideband radar sensor for soil moisture estimation.
  • To develop an accurate and affordable tool for agriculturists.

Main Methods:

  • Volumetric water content measured using a 3-10 GHz radar sensor.
  • Reflected radar signals processed to extract input features for ML models.
  • Comparison with a contact-based Vernier soil sensor.
  • Evaluation of Neural Network, Support Vector Machine (SVM), Linear Regression, and K-Nearest Neighbors (KNN) models.

Main Results:

  • Neural Network model achieved the highest accuracy with a coefficient of determination (R2) of 0.9894.
  • Linear Regression showed strong performance with Root Mean Square Error (RMSE) of 3.94 and Mean Absolute Error (MAE) of 3.54.
  • KNN and SVM models demonstrated moderate performance with varying RMSE and MAE values.

Conclusions:

  • Machine learning, particularly neural networks, offers a highly accurate method for soil moisture estimation using radar.
  • The proposed radar-based approach provides a promising, cost-effective alternative to traditional soil moisture sensors.
  • This technology can empower agriculturists with improved tools for precision farming and water resource management.