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A Design Method for an SVM-Based Humidity Sensor for Grain Storage.

Lining Liu1,2, Chengbao Song1,2, Ke Zhu2,3

  • 1College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai'an 271018, China.

Sensors (Basel, Switzerland)
|May 11, 2024
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Summary
This summary is machine-generated.

A new, cost-effective grain humidity sensor and calibration models for millet, rice, and wheat were developed. These innovations improve moisture monitoring for large-scale grain storage, reducing losses and enhancing quality.

Keywords:
grain moisturehumidity sensormoisture calibration modelsstanding wave ratio (SWR) method

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Area of Science:

  • Agricultural Engineering
  • Sensor Technology
  • Food Science

Background:

  • Appropriate moisture content is critical for reducing grain storage losses and maintaining quality.
  • Existing humidity sensors are inadequate for large-scale Chinese grain storage due to cost, size, and implementation challenges.

Purpose of the Study:

  • Develop an economical, efficient, and deployable grain humidity sensor for large-scale storage.
  • Create humidity calibration models for millet, rice, and wheat.

Main Methods:

  • Analyzed ideal probe structure for grain humidity sensors.
  • Developed and validated sensor accuracy using millet, rice, and wheat.
  • Constructed humidity calibration models using Support Vector Machine (SVM) models.

Main Results:

  • Determined optimal probe length of 0.67 m under ideal conditions.
  • Achieved R² > 0.9 for all three crop-specific SVM humidity calibration models.
  • Demonstrated a linear relationship (R² > 0.9) between measured and model-calculated data, approximating y = x.

Conclusions:

  • The developed sensor and calibration models offer reliable technological support for humidity monitoring in large-scale grain storage and processing.
  • This technology has significant applications in enhancing grain storage practices and ensuring grain safety management.