Journal article Open Access

A Consumer-Friendly Machine Learning Based Mechanism to Recognize the Quality of Commercially Available Fruits

Deepti C.; Arjit Jindal; Prudhvi Reddy; Amrutha D.


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  <identifier identifierType="URL">https://zenodo.org/record/5594209</identifier>
  <creators>
    <creator>
      <creatorName>Deepti C.</creatorName>
      <affiliation>PES University Electronic City Campus, Bengaluru, India.</affiliation>
    </creator>
    <creator>
      <creatorName>Arjit Jindal</creatorName>
      <affiliation>PESIT Bangalore South Campus, Bengaluru, India.</affiliation>
    </creator>
    <creator>
      <creatorName>Prudhvi Reddy</creatorName>
      <affiliation>PESIT Bangalore South Campus, Bengaluru, India.</affiliation>
    </creator>
    <creator>
      <creatorName>Amrutha D.</creatorName>
      <affiliation>PESIT Bangalore South Campus, Bengaluru, India.</affiliation>
    </creator>
  </creators>
  <titles>
    <title>A Consumer-Friendly Machine Learning Based  Mechanism to Recognize the Quality of  Commercially Available Fruits</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>CNN based Approach, Fruit Ripening, Image Processing, Machine Learning.</subject>
    <subject subjectScheme="issn">2249-8958</subject>
    <subject subjectScheme="handle">C6270029320/2020©BEIESP</subject>
  </subjects>
  <contributors>
    <contributor contributorType="Sponsor">
      <contributorName>Blue Eyes Intelligence Engineering  &amp; Sciences Publication(BEIESP)</contributorName>
      <affiliation>Publisher</affiliation>
    </contributor>
  </contributors>
  <dates>
    <date dateType="Issued">2020-02-29</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="JournalArticle"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/5594209</alternateIdentifier>
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    <relatedIdentifier relatedIdentifierType="ISSN" relationType="IsCitedBy" resourceTypeGeneral="JournalArticle">2249-8958</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.35940/ijeat.C6270.029320</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Cultivators and sellers of many high-in-demand fruits traditionally preferred natural ripening after picking. Greed of hefty profits has motivated some of them to artificially hasten the ripening process at the cost of people&amp;rsquo;s health. Artificial ripening processes tend to degrade the entire quality of the fruit. The focus of this work is to describe a nondestructive method to detect artificial fruit ripening. To aid the detection, the proposed solution utilizes image processing and machine learning techniques to find the artificially ripened fruits. An input fruit image is selected as the test image. The next stage involves comparison of the features (histogram values) of the test image with the image of a naturally ripened one. A smartphone runs an android application to identify artificially ripened fruits. This work specifically concentrates on the commonly preferred Indian Mango and Indian Apple. The developed mechanism has an efficiency of 89-94% in correct detection.&lt;/p&gt;</description>
  </descriptions>
</resource>
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