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<ArticleSet>
  <Article>
    <Journal>
      <PublisherName>IJIRCSTJournal</PublisherName>
      <JournalTitle>International Journal of Innovative Research in Computer Science and Technology</JournalTitle>
      <PISSN>I</PISSN>
      <EISSN>S</EISSN>
      <Volume-Issue>Volume 13 Issue 3</Volume-Issue>
      <PartNumber/>
      <IssueTopic>Computer Science </IssueTopic>
      <IssueLanguage>English</IssueLanguage>
      <Season>May - June 2025</Season>
      <SpecialIssue>N</SpecialIssue>
      <SupplementaryIssue>N</SupplementaryIssue>
      <IssueOA>Y</IssueOA>
      <PubDate>
        <Year>2025</Year>
        <Month>05</Month>
        <Day>20</Day>
      </PubDate>
      <ArticleType>Computer Sciences</ArticleType>
      <ArticleTitle>Benchmarking Deep Learning for Multi-Class Plant Disease Diagnosis: A Critical Review</ArticleTitle>
      <SubTitle/>
      <ArticleLanguage>English</ArticleLanguage>
      <ArticleOA>Y</ArticleOA>
      <FirstPage>89</FirstPage>
      <LastPage>94</LastPage>
      <AuthorList>
        <Author>
          <FirstName>Manisha Bajpai</FirstName>          
          <AuthorLanguage>English</AuthorLanguage>
          <Affiliation/>
          <CorrespondingAuthor>Y</CorrespondingAuthor>
          <ORCID/>
                      <FirstName>Deepshikha</FirstName>          
          <AuthorLanguage>English</AuthorLanguage>
          <Affiliation/>
          <CorrespondingAuthor>N</CorrespondingAuthor>
          <ORCID/>
                    <FirstName>Raj Gaurang Tiwari</FirstName>          
          <AuthorLanguage>English</AuthorLanguage>
          <Affiliation/>
          <CorrespondingAuthor>N</CorrespondingAuthor>
          <ORCID/>
           
        </Author>
      </AuthorList>
      <DOI>https://doi.org/10.55524/ijircst.2025.13.3.15</DOI>
      <Abstract>The performance of many deep learning models for the classification of multi-class plant diseases is examined in this study.&amp;nbsp; Accurate and effective solutions are necessary because plant disease identification is crucial to agricultural productivity.&amp;nbsp; Publicly accessible datasets of plant disease images are used to assess deep learning models, especially convolutional neural networks (CNNs), and transfer learning architectures such as ResNet and VGGNet.&amp;nbsp; These models are compared in the study according to their generalizability, accuracy, and computing efficiency.&amp;nbsp; The results are intended to shed light on the best deep learning methods for managing and detecting plant diseases in the real world.</Abstract>
      <AbstractLanguage>English</AbstractLanguage>
      <Keywords>Machine Learning, Deep learning, CNNs, artificial intelligence, ResNet, VGGNet,  Plant diseases detection.</Keywords>
      <URLs>
        <Abstract>https://ijircst.org/abstract.php?article_id=1378</Abstract>
      </URLs>      
    </Journal>
  </Article>
</ArticleSet>