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An Automated Image Processing Module for Quality Evaluation of Milled Rice.

Chinmay Kurade1, Maninder Meenu2, Sahil Kalra1

  • 1Department of Mechanical Engineering, Indian Institute of Technology, Jammu 181221, India.

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|March 29, 2023
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Summary

This study developed a low-cost rice quality assessment system using image processing and machine learning (ML). The random forest classifier achieved 77% accuracy in identifying rice varieties and pricing adulterated samples.

Keywords:
Raspberry-Piautomationcomputer visionmachine learningquality assessmentrice grains

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Area of Science:

  • Agricultural Technology
  • Computer Science
  • Data Science

Background:

  • Rice quality assessment is crucial for the food industry.
  • Traditional methods for rice quality evaluation can be time-consuming and subjective.
  • Developing automated, low-cost systems is essential for widespread adoption.

Purpose of the Study:

  • To develop a low-cost rice quality assessment system using image processing and machine learning (ML).
  • To classify different rice varieties based on extracted structural and geometric features.
  • To evaluate the performance of various ML algorithms for rice classification and pricing adulterated samples.

Main Methods:

  • Acquisition of 3081 rice grain images using a Raspberry-Pi based module.
  • Extraction of structural and geometric features (e.g., area, roundness, shape factor).
  • Classification using seven ML algorithms, including Random Forest, with performance evaluation via ROC curves.

Main Results:

  • The Random Forest classifier achieved the highest accuracy of 77% for rice variety classification.
  • The developed system successfully segmented and classified rice grains based on their types.
  • The Random Forest algorithm was also effective in determining the price of adulterated rice samples.

Conclusions:

  • A low-cost, automated rice quality assessment system can be effectively implemented using image processing and ML.
  • The Random Forest classifier is a robust model for classifying rice varieties and assessing sample value.
  • This technology offers potential for improving efficiency and accuracy in the rice industry.