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Online Detection of Impurities in Corn Deep-Bed Drying Process Utilizing Machine Vision.

Tao Li1, Jinjie Tong2, Muhua Liu1

  • 1College of Engineering, Jiangxi Agricultural University, Nanchang 330045, China.

Foods (Basel, Switzerland)
|December 23, 2022
PubMed
Summary

This study introduces an automated machine vision system for detecting impurities during corn drying. The system accurately identifies and quantifies contaminants like broken corncobs and stones, improving process control.

Keywords:
corndeep-bed dryingimpurities contentmachine vision

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Area of Science:

  • Agricultural Engineering
  • Computer Vision
  • Image Processing

Background:

  • Accurate online detection of impurities is crucial for stable corn deep-bed drying operations.
  • Data support is needed for self-adapting control systems in drying equipment.

Purpose of the Study:

  • To develop an automatic machine vision system for corn impurity detection.
  • To enhance corn image quality and segment impurities effectively.
  • To provide a reliable method for monitoring impurity content during drying.

Main Methods:

  • Utilized Multi-Scale Retinex with Color Restore (MSRCR) for image enhancement.
  • Employed HSV color space thresholding for image segmentation.
  • Combined morphological operations for impurity classification and recognition.

Main Results:

  • Achieved comprehensive evaluation indices of 83.05% for broken corncobs, 83.87% for broken bracts, and 87.43% for crushed stones.
  • Demonstrated the algorithm's ability to quickly and effectively identify impurities in corn images.
  • Validated the system's performance through online detection.

Conclusions:

  • The proposed machine vision approach provides effective technical support for monitoring corn impurities.
  • This method offers a theoretical basis for improving the self-adapting control of corn deep-bed drying equipment.
  • The system enables rapid and accurate identification of various impurities in corn.