Journal article Open Access
M. Alagurajan; C. Vijayakumaran
<?xml version='1.0' encoding='UTF-8'?> <record xmlns="http://www.loc.gov/MARC21/slim"> <leader>00000nam##2200000uu#4500</leader> <datafield tag="041" ind1=" " ind2=" "> <subfield code="a">eng</subfield> </datafield> <datafield tag="653" ind1=" " ind2=" "> <subfield code="a">Machine Learning, Crop Yield,</subfield> </datafield> <controlfield tag="005">20211023134842.0</controlfield> <controlfield tag="001">5593844</controlfield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="u">Associate Professor, Department CSE, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India.</subfield> <subfield code="a">C. Vijayakumaran</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="u">Publisher</subfield> <subfield code="4">spn</subfield> <subfield code="a">Blue Eyes Intelligence Engineering & Sciences Publication(BEIESP)</subfield> </datafield> <datafield tag="856" ind1="4" ind2=" "> <subfield code="s">453167</subfield> <subfield code="z">md5:46d80e103f7a19681bd738fb4d6e5cb8</subfield> <subfield code="u">https://zenodo.org/record/5593844/files/C5775029320.pdf</subfield> </datafield> <datafield tag="542" ind1=" " ind2=" "> <subfield code="l">open</subfield> </datafield> <datafield tag="260" ind1=" " ind2=" "> <subfield code="c">2020-02-29</subfield> </datafield> <datafield tag="909" ind1="C" ind2="O"> <subfield code="p">openaire</subfield> <subfield code="o">oai:zenodo.org:5593844</subfield> </datafield> <datafield tag="909" ind1="C" ind2="4"> <subfield code="c">3506-3508</subfield> <subfield code="n">3</subfield> <subfield code="p">International Journal of Engineering and Advanced Technology (IJEAT)</subfield> <subfield code="v">9</subfield> </datafield> <datafield tag="100" ind1=" " ind2=" "> <subfield code="u">Department of CSE, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India.</subfield> <subfield code="a">M. Alagurajan</subfield> </datafield> <datafield tag="245" ind1=" " ind2=" "> <subfield code="a">ML Methods for Crop Yield Prediction and Estimation: An Exploration</subfield> </datafield> <datafield tag="540" ind1=" " ind2=" "> <subfield code="u">https://creativecommons.org/licenses/by/4.0/legalcode</subfield> <subfield code="a">Creative Commons Attribution 4.0 International</subfield> </datafield> <datafield tag="650" ind1="1" ind2="7"> <subfield code="a">cc-by</subfield> <subfield code="2">opendefinition.org</subfield> </datafield> <datafield tag="650" ind1="1" ind2=" "> <subfield code="a">ISSN</subfield> <subfield code="0">(issn)2249-8958</subfield> </datafield> <datafield tag="650" ind1="1" ind2=" "> <subfield code="a">Retrieval Number</subfield> <subfield code="0">(handle)C5775029320/2020©BEIESP</subfield> </datafield> <datafield tag="520" ind1=" " ind2=" "> <subfield code="a"><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></subfield> </datafield> <datafield tag="773" ind1=" " ind2=" "> <subfield code="n">issn</subfield> <subfield code="i">isCitedBy</subfield> <subfield code="a">2249-8958</subfield> </datafield> <datafield tag="024" ind1=" " ind2=" "> <subfield code="a">10.35940/ijeat.C5775.029320</subfield> <subfield code="2">doi</subfield> </datafield> <datafield tag="980" ind1=" " ind2=" "> <subfield code="a">publication</subfield> <subfield code="b">article</subfield> </datafield> </record>
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