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<records>

  <record>
    <language>eng</language>
          <publisher>Enviro Research Publishers</publisher>
        <journalTitle>Current Agriculture Research Journal</journalTitle>
          <issn>2347-4688</issn>
              <eissn>2321-9971</eissn>
        <publicationDate>2023-09-05</publicationDate>
    
        <volume>11</volume>
        <issue>2</issue>

 
    <startPage>686</startPage>
    <endPage>694</endPage>

         <doi></doi>
        <publisherRecordId>15698</publisherRecordId>
    <documentType>article</documentType>
    <title language="eng">Recommending and Predicting Crop Yield using Smart Machine Learning Algorithm (SMLA)</title>

    <authors>
	 


      <author>
       <name>K. Sutha</name>

 
		
	<affiliationId></affiliationId>
      </author>
    

	 


      <author>
       <name>N. Indumathi</name>


		
	<affiliationId></affiliationId>
      </author>

    

	 


      <author>
       <name>S. Uma Shankari</name>

		
	<affiliationId></affiliationId>
      </author>
    

	


	


	
    </authors>
    
	    <affiliationsList>
	    
		
		<affiliationName affiliationId="1">Department of MCA, College of Science and Humanities, SRM Institute of Science and Technology, Ramapuram Campus, Chennai, Tamil Nadu – 600 089, India.</affiliationName>
    

		
		
		
		
		
	  </affiliationsList>






    <abstract language="eng">Agriculture is always needed by every human and responsible for the economic growth of a country. Developed countries likewise America, Japan, China are leading and making other countries too dependent on their technologies. But developing countries like India are expecting a lot of new technological innovations in the field of agriculture. Innovations may be in the form of smart machines, automation systems, sensor-based instruments, etc. and an advantage for society. In this paper, we have proposed Recommending and Predicting Crop Yield using Smart Machine Learning Algorithm (SMLA). The proposed algorithm namely SMLA is compared with other traditional algorithms to predict crop yield. In comparison to other algorithms the proposed algorithm works efficiently and produces 95% accuracy.</abstract>

    <fullTextUrl format="html">https://www.agriculturejournal.org/volume11number2/recommending-and-predicting-crop-yield-using-smart-machine-learning-algorithm-smla/</fullTextUrl>



      <keywords language="eng">
        <keyword>Agriculture; Crop; Decision Support System; Smart Machine Learning Algorithm (SMLA); Yield</keyword>
      </keywords>

  </record>
</records>