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Performance Comparison of Deep CNN Models for Disease Diagnosis on Apple Leaves

Kota Akshith Reddy; Sharmila Banu K; Sai Kanishka Ippagunta; Chandra Havish Siddareddi; Jahnavi Polsani


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  <identifier identifierType="URL">https://zenodo.org/record/5412797</identifier>
  <creators>
    <creator>
      <creatorName>Kota Akshith Reddy</creatorName>
      <affiliation>Department of Computer Science Major, Vellore Institute of Technology, Vellore (Tamil Nadu), India.</affiliation>
    </creator>
    <creator>
      <creatorName>Sharmila Banu K</creatorName>
      <affiliation>Assistant Professor, Department of Computer Science and Engineering, Vellore Institute of Technology, Vellore (Tamil Nadu), India.</affiliation>
    </creator>
    <creator>
      <creatorName>Sai Kanishka Ippagunta</creatorName>
      <affiliation>Pursuing, B.Tech (CSE), Department of Information Security, Vellore Institute of Technology, Vellore (Tamil Nadu), India.</affiliation>
    </creator>
    <creator>
      <creatorName>Chandra Havish Siddareddi</creatorName>
      <affiliation>Department of Electronics and Communications Engineering, Vellore institute of Technology, Vellore (Tamil Nadu), India.</affiliation>
    </creator>
    <creator>
      <creatorName>Jahnavi Polsani</creatorName>
      <affiliation>Pursuing, B.Tech, Department of Computer Science Engineering (CSE), Vellore Institute of Technology, Vellore (Tamil Nadu), India.</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Performance Comparison of Deep CNN Models for Disease Diagnosis on Apple Leaves</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2021</publicationYear>
  <subjects>
    <subject>Apple Orchards,Convolutional Neural Networks , Decision Trees, Machine Learning , Multilayer Perceptron Neural Networks.</subject>
    <subject subjectScheme="issn">2249-8958</subject>
    <subject subjectScheme="handle">100.1/ijeat.F30400810621</subject>
  </subjects>
  <contributors>
    <contributor contributorType="Sponsor">
      <contributorName>Blue Eyes Intelligence Engineering  and Sciences Publication (BEIESP)</contributorName>
      <affiliation>Publisher</affiliation>
    </contributor>
  </contributors>
  <dates>
    <date dateType="Issued">2021-08-30</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="JournalArticle"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/5412797</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="ISSN" relationType="IsCitedBy" resourceTypeGeneral="JournalArticle">2249-8958</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.35940/ijeat.F3040.0810621</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;The apple is one of the most cultivated fruits in the world. They are round in shape and their color varies from green to red. Apple Orchards face constant threats from a large number of insects and pathogens and the early detection of these diseases can help in mitigating these harmful effects. An apple tree takes around six to ten years to mature and produce fruit and therefore, the production costs are high and there is no room for such diseases to get a healthy fruit and a profitable yield. Delayed or incorrect diagnosis of these diseases can lead to using either inadequate or more than required chemicals or using a wrong chemical altogether to treat the plant. Historically, this problem was solved using conventional machine learning algorithms like SVMs, Decision Trees and Random Forests. However, in recent times, the approach to solve this problem has shifted to deep learning, specifically Convolutional Neural Networks. CNN&amp;rsquo;s are powerful tools that can be used for image classification. We can get state-ofthe-art results even by using small amounts of data and little to no data preprocessing. In this work, we are going to compare some of the state of the art CNN architectures on the task of accurately classifying a given image into different categories of diseases or as a healthy leaf. Finally, experimental results are conveyed and performance analysis of these various architectures has been done.&amp;nbsp;&lt;/p&gt;</description>
  </descriptions>
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