Journal article Open Access
M. Alagurajan; C. Vijayakumaran
<?xml version='1.0' encoding='utf-8'?> <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:adms="http://www.w3.org/ns/adms#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dct="http://purl.org/dc/terms/" xmlns:dctype="http://purl.org/dc/dcmitype/" xmlns:dcat="http://www.w3.org/ns/dcat#" xmlns:duv="http://www.w3.org/ns/duv#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:frapo="http://purl.org/cerif/frapo/" xmlns:geo="http://www.w3.org/2003/01/geo/wgs84_pos#" xmlns:gsp="http://www.opengis.net/ont/geosparql#" xmlns:locn="http://www.w3.org/ns/locn#" xmlns:org="http://www.w3.org/ns/org#" xmlns:owl="http://www.w3.org/2002/07/owl#" xmlns:prov="http://www.w3.org/ns/prov#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xmlns:schema="http://schema.org/" xmlns:skos="http://www.w3.org/2004/02/skos/core#" xmlns:vcard="http://www.w3.org/2006/vcard/ns#" xmlns:wdrs="http://www.w3.org/2007/05/powder-s#"> <rdf:Description rdf:about="https://zenodo.org/record/5593844"> <dct:identifier rdf:datatype="http://www.w3.org/2001/XMLSchema#anyURI">https://zenodo.org/record/5593844</dct:identifier> <foaf:page rdf:resource="https://zenodo.org/record/5593844"/> <dct:creator> <rdf:Description> <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/> <foaf:name>M. Alagurajan</foaf:name> <org:memberOf> <foaf:Organization> <foaf:name>Department of CSE, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India.</foaf:name> </foaf:Organization> </org:memberOf> </rdf:Description> </dct:creator> <dct:creator> <rdf:Description> <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/> <foaf:name>C. Vijayakumaran</foaf:name> <org:memberOf> <foaf:Organization> <foaf:name>Associate Professor, Department CSE, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India.</foaf:name> </foaf:Organization> </org:memberOf> </rdf:Description> </dct:creator> <dct:title>ML Methods for Crop Yield Prediction and Estimation: An Exploration</dct:title> <dct:publisher> <foaf:Agent> <foaf:name>Zenodo</foaf:name> </foaf:Agent> </dct:publisher> <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2020</dct:issued> <dcat:keyword>Machine Learning, Crop Yield,</dcat:keyword> <dct:subject> <skos:Concept> <skos:prefLabel>2249-8958</skos:prefLabel> <skos:inScheme> <skos:ConceptScheme> <dct:title>issn</dct:title> </skos:ConceptScheme> </skos:inScheme> </skos:Concept> </dct:subject> <dct:subject> <skos:Concept> <skos:prefLabel>C5775029320/2020©BEIESP</skos:prefLabel> <skos:inScheme> <skos:ConceptScheme> <dct:title>handle</dct:title> </skos:ConceptScheme> </skos:inScheme> </skos:Concept> </dct:subject> <schema:sponsor> <rdf:Description> <rdf:type rdf:resource="http://xmlns.com/foaf/0.1/Agent"/> <foaf:name>Blue Eyes Intelligence Engineering & Sciences Publication(BEIESP)</foaf:name> <org:memberOf> <foaf:Organization> <foaf:name>Publisher</foaf:name> </foaf:Organization> </org:memberOf> </rdf:Description> </schema:sponsor> <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2020-02-29</dct:issued> <dct:language rdf:resource="http://publications.europa.eu/resource/authority/language/ENG"/> <owl:sameAs rdf:resource="https://zenodo.org/record/5593844"/> <adms:identifier> <adms:Identifier> <skos:notation rdf:datatype="http://www.w3.org/2001/XMLSchema#anyURI">https://zenodo.org/record/5593844</skos:notation> <adms:schemeAgency>url</adms:schemeAgency> </adms:Identifier> </adms:identifier> <dct:relation rdf:resource="http://issn.org/resource/ISSN/2249-8958"/> <owl:sameAs rdf:resource="https://doi.org/10.35940/ijeat.C5775.029320"/> <dct:description><p>Machine learning Has performed a essential position within the estimation of crop yield for both farmers and consumers of the products. Machine learning techniques learn from data set related to the environment on which the estimations and estimation are to be made and the outcome of the learning process are used by farmers for corrective measures for yield optimization. This paper we explore various ML techniques utilized in crop yield estimation and provide the detailed analysis of accuracy of the techniques.</p></dct:description> <dct:accessRights rdf:resource="http://publications.europa.eu/resource/authority/access-right/PUBLIC"/> <dct:accessRights> <dct:RightsStatement rdf:about="info:eu-repo/semantics/openAccess"> <rdfs:label>Open Access</rdfs:label> </dct:RightsStatement> </dct:accessRights> <dct:license rdf:resource="https://creativecommons.org/licenses/by/4.0/legalcode"/> <dcat:distribution> <dcat:Distribution> <dcat:accessURL rdf:resource="https://doi.org/10.35940/ijeat.C5775.029320"/> <dcat:byteSize>453167</dcat:byteSize> <dcat:downloadURL rdf:resource="https://zenodo.org/record/5593844/files/C5775029320.pdf"/> <dcat:mediaType>application/pdf</dcat:mediaType> </dcat:Distribution> </dcat:distribution> </rdf:Description> </rdf:RDF>
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