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Inline 3D Volumetric Measurement of Moisture Content in Rice Using Regression-Based ML of RF Tomographic Imaging.

Abd Alazeez Almaleeh1,2, Ammar Zakaria1,2, Latifah Munirah Kamarudin2,3

  • 1Faculty of Electrical Engineering Technology, Universiti Malaysia Perlis (UniMAP), Arau 02600, Malaysia.

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Summary

This study introduces a new 3D imaging system for measuring rice moisture content in silos. Using radio frequency tomographic imaging and machine learning, it offers continuous, non-destructive monitoring to preserve grain quality.

Keywords:
3D volumetricmachine learningmoisture contenttomographic imaging

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Area of Science:

  • Agricultural Engineering
  • Sensor Technology
  • Machine Learning

Background:

  • Rice quality and economic value depend on moisture content, necessitating frequent monitoring.
  • Current methods like grab sampling or single rod sensors offer localized, non-continuous measurements.
  • A 3D volumetric system for stored rice moisture is currently unavailable commercially.

Purpose of the Study:

  • To develop a novel, contactless, non-destructive 3D volumetric system for monitoring rice moisture content in silos.
  • To utilize low-cost wireless devices and radio frequency tomographic imaging for moisture detection.
  • To apply regression-based machine learning for accurate moisture distribution analysis.

Main Methods:

  • Deployment of low-cost wireless devices around the silo perimeter.
  • Application of radio frequency tomographic imaging to capture internal grain data.
  • Utilizing regression-based machine learning algorithms to reconstruct 3D moisture distribution.

Main Results:

  • The proposed technique provides contactless, non-destructive 3D volumetric moisture content distribution.
  • Accuracies of 83.7% or higher were achieved in detecting localized moisture levels.
  • The system enables continuous monitoring, unlike existing localized methods.

Conclusions:

  • The novel radio frequency tomographic imaging and machine learning approach offers a viable solution for 3D rice moisture monitoring.
  • This system addresses the limitations of current technologies, providing continuous and accurate insights.
  • The developed technique has the potential to significantly improve stored grain management and quality preservation.