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  Published Paper Details:

  Paper Title

Crop yield prediction in machine learning models - A Survey

  Authors

  E.KALAIARASI,  DR.A.ANBARASI

  Keywords

crop suitability, land suitability, data mining, classification, agricultural data mining

  Abstract


The fast pace of urban development minimize the agricultural lands. Owing to poor rainfall and drastic climatic changes farmers often face challenges to sustain cultivation of crops with respect to crop cycle. With growing economic competition and rising population, governmental agencies design long term plans which rarely address the farmer�s needs. To meet the global demands agriculturist needs to investigate every opportunity that could improve agricultural production and growth. Whether to expand agricultural lands or to improve the production farmers needs to assess the suitability between land and crops. The investigation of land suitability and crop suitability has attracted many researchers to utilize latest technology such as remote sensing, geographical information systems etc. This paper aims to survey on recent researches on crop and land suitability using data mining techniques.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2002060

  Paper ID - 191776

  Page Number(s) - 559-564

  Pubished in - Volume 8 | Issue 2 | February 2020

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  E.KALAIARASI,  DR.A.ANBARASI,   "Crop yield prediction in machine learning models - A Survey", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.8, Issue 2, pp.559-564, February 2020, Available at :http://www.ijcrt.org/papers/IJCRT2002060.pdf

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ISSN: 2320-2882
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ISSN and 7.97 Impact Factor Details


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ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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