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Prepackaging Sorting of Guava Fruits using Machine Vision based Fruit Sorter System based on K-Nearest Neighbor Algorithm

Author

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  • Ashok Kanade
  • Arvind Shaligram

Abstract

In the present work, a general approach is developed to estimate the ripeness level of Guava fruit without touching it. The fruits can be classified according to different conditions as pre-harvest, postharvest harvest, storage conditions in controlled environment or harsh environment. In this study the fruit under test were classified at the time of harvest in four different classes as green, ripe, overripe and spoiled using a web camera based computer vision system. This paper presents a simple method that uses a combination of digital web camera, computer and indigenously developed graphics software to measure and analyze the surface color of fruits for ripening state recognition. The recognition was done by the nearest neighbor classifier engine uses the HSI color distribution in selected ROI of fruits. Experimental results on a database of 200 guava fruits from 4 different ripening states confirm the effectiveness of the proposed approach.

Suggested Citation

  • Ashok Kanade & Arvind Shaligram, 2018. "Prepackaging Sorting of Guava Fruits using Machine Vision based Fruit Sorter System based on K-Nearest Neighbor Algorithm," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 3(3), pages 1972-1977, April.
    • Handle: RePEc:jbh:ijsrcs:v3:y2018:i3:id:hcseit1833737
    DOI: 10.32628/CSEIT1833737
    Note: Article URL: https://ijsrcseit.com/CSEIT1833737
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