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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 10 Issue 3</Volume-Issue>
      <PartNumber/>
      <IssueTopic>Engineering</IssueTopic>
      <IssueLanguage>English</IssueLanguage>
      <Season>May - June 2022</Season>
      <SpecialIssue>N</SpecialIssue>
      <SupplementaryIssue>N</SupplementaryIssue>
      <IssueOA>Y</IssueOA>
      <PubDate>
        <Year>2022</Year>
        <Month>06</Month>
        <Day>23</Day>
      </PubDate>
      <ArticleType>Computer Sciences</ArticleType>
      <ArticleTitle>Design and Analysis of Prediction Model Using Machine Learning In Agriculture</ArticleTitle>
      <SubTitle/>
      <ArticleLanguage>English</ArticleLanguage>
      <ArticleOA>Y</ArticleOA>
      <FirstPage>72</FirstPage>
      <LastPage>75</LastPage>
      <AuthorList>
        <Author>
          <FirstName>Diksha Gupta</FirstName>          
          <AuthorLanguage>English</AuthorLanguage>
          <Affiliation/>
          <CorrespondingAuthor>Y</CorrespondingAuthor>
          <ORCID/>
                      <FirstName>Dr. Yojna Arora</FirstName>          
          <AuthorLanguage>English</AuthorLanguage>
          <Affiliation/>
          <CorrespondingAuthor>N</CorrespondingAuthor>
          <ORCID/>
                    <FirstName>Dr. Aarti Chugh</FirstName>          
          <AuthorLanguage>English</AuthorLanguage>
          <Affiliation/>
          <CorrespondingAuthor>N</CorrespondingAuthor>
          <ORCID/>
           
        </Author>
      </AuthorList>
      <DOI>https://doi.org/10.55524/ijircst.2022.10.3.14</DOI>
      <Abstract>The reality of worldwide population growth and climate change demand that agriculture production can&amp;nbsp;be increased. Traditional study findings which are difficult to extend to all conceivable fields since these are dependent on certain soil types, climatic circumstances, and background management combinations that aren&amp;#39;t appropriate or transferable to all farms. There is no way for evaluating the efficacy of endless cropping system interactions (including many management practises) to crop production across the World. We demonstrate that dynamic interactions, that cannot be examined in repetitive trials, which are linked with considerable crop output variability and therefore the possibility for big yield gains, using massive databases and artificial intelligence. Our method can help to speed up agricultural research, discover sustainable methods, and meet future food demands. This is a paper attempted that at crop yield prediction using machine learning techniques with historic crop production data. For this, data has been collected from data.gov.in and data.world.</Abstract>
      <AbstractLanguage>English</AbstractLanguage>
      <Keywords>About four Machine learning, Big Data Analysis, Forecasting, Artificial Intelligence, Algorithms, Prediction and Analysis.</Keywords>
      <URLs>
        <Abstract>https://ijircst.org/abstract.php?article_id=955</Abstract>
      </URLs>      
    </Journal>
  </Article>
</ArticleSet>