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<records>
  <record>
    <language>eng</language>
    <publisher>Science and Education Publishing</publisher>
    <journalTitle>Applied Ecology and Environmental Sciences</journalTitle>
    <eissn>2328-3920</eissn>
    <publicationDate>2022-08-09</publicationDate>
    <volume>10</volume>
    <issue>8</issue>
    <startPage>509</startPage>
    <endPage>518</endPage>
    <doi>10.12691/aees-10-8-3</doi>
    <publisherRecordId>AEES20221083</publisherRecordId>
    <documentType>article</documentType>
    <title language="eng">Intelligent Smart Agriculture Methane Crop Yields Detection and Prediction for Machine Learning Techniques</title>
    <authors>
      <author>
        <name>S. P. Ramesh</name>
        <email>spramesh.me@gmail.com</email>
        <affiliationId>1</affiliationId>
      </author>
      <author>
        <name>Dr. Muthusamy Periyasamy</name>
        <affiliationId>2</affiliationId>
      </author>
    </authors>
    <affiliationsList>
      <affiliationName affiliationId="1">Research Scholar, Shri Venkateshwara University, Gajraula, Uttar Pradesh, India</affiliationName>
      <affiliationName affiliationId="2">Professor, Shri Venkateshwara University, Gajraula, Uttar Pradesh, India</affiliationName>
    </affiliationsList>
    <abstract language="eng">The MEVM (methane energy value model) was created for several energy crops. Machine learning has been created with big data developments and better enrolling than make new open doors for data genuine science in the multi-disciplinary agri-business progresses territory. By applying machine learning to sensor data, farm the chief's frameworks are forming into ceaseless artificial intelligence engaged tasks that give rich recommendations and encounters to farmer decision help and action. IoT contraptions give data about the nature of developing fields and a short time later make a move dependent upon the farmer input. The arrangement endeavors to mastermind diverse possible unstructured associations of crude data, assembled from different kinds of IoT devices, united and advanced self-ruling style using the upside of model changes and model-driven designing to change data in a coordinated structure.</abstract>
    <fullTextUrl format="pdf">http://pubs.sciepub.com/aees/10/8/3/aees-10-8-3.pdf</fullTextUrl>
    <keywords language="eng">
      <keyword>machine learning</keyword>
      <keyword>big data</keyword>
      <keyword>Internet of Things (IoT)</keyword>
    </keywords>
  </record>
</records>