A new approach for predicting crop yield prediction using data mining techniques

Author/s

Surya A. Venkaiah

Student of Amity School of Engineering and Technology (ASET), Amity University Campus, Noida, India

Kondajji Swati Sunitha

Assistant Professor of Amity School of Engineering and Technology (ASET), Amity University Campus, Noida, India

Abstract

Agriculture is the most important sector of Indian Economy. Indian agriculture sector accounts for 18 per cent of India’s gross domestic product (GDP) and provides employment to 50% of the countries workforce. We all know agriculture is the most important factor which influence the economy of India and it also offers employment to 50% population of India. People of India are practicing agriculture for many years and the result were never satisfying due to many factors that affect the crop. Day by day environment is changing and is not stable at various places. It is very important for the farmer to cultivate their farm in good climatic conditions, under such conditions they need technology that predict the environment. In such cases data mining is the apt technology for prediction. Data mining contains various prediction algorithms like id3, cart, c4.5, random forest algorithm. In this Project we are using Id3 and cart algorithms as a prediction techniques and it is possible obtain the information from the prediction algorithms which helps farmers to cultivate the appropriate crop.

Keywords

Datamining, crop yield prediction, ID3 and Cart algorithm.

To cite this article

Venkaiah, S.A., & Sunitha, K.S.(2019). A new approach for predicting crop yield prediction using data mining techniques, International Journal of Engineering, IT and Scientific Research (IJEISR). Vol. 3, No. 1, pp.42-48. Doi:10.31219/osf.io/f5xws

Copyright

Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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