This paper is published in Volume-5, Issue-3, 2019
Area
Machine Learning
Author
Suhas L., Sangamesh, Prakash kumar, Supriya B. N.
Org/Univ
SJB Institute of Technology, Bangalore, Karnataka, India
Keywords
Machine Learning, Multilinear regression, Decision tree regression, Crop prediction
Citations
IEEE
Suhas L., Sangamesh, Prakash kumar, Supriya B. N.. Rice crop yield prediction using machine learning techniques, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.
APA
Suhas L., Sangamesh, Prakash kumar, Supriya B. N. (2019). Rice crop yield prediction using machine learning techniques. International Journal of Advance Research, Ideas and Innovations in Technology, 5(3) www.IJARIIT.com.
MLA
Suhas L., Sangamesh, Prakash kumar, Supriya B. N.. "Rice crop yield prediction using machine learning techniques." International Journal of Advance Research, Ideas and Innovations in Technology 5.3 (2019). www.IJARIIT.com.
Suhas L., Sangamesh, Prakash kumar, Supriya B. N.. Rice crop yield prediction using machine learning techniques, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.
APA
Suhas L., Sangamesh, Prakash kumar, Supriya B. N. (2019). Rice crop yield prediction using machine learning techniques. International Journal of Advance Research, Ideas and Innovations in Technology, 5(3) www.IJARIIT.com.
MLA
Suhas L., Sangamesh, Prakash kumar, Supriya B. N.. "Rice crop yield prediction using machine learning techniques." International Journal of Advance Research, Ideas and Innovations in Technology 5.3 (2019). www.IJARIIT.com.
Abstract
In an agricultural country like India where agriculture is the main source of occupation for most of the people, there are many factors that contribute to the total yield of the crop being cultivated. Depending on the various soil conditions and climatic conditions the yield of the crop might vary. It would be helpful for the farmer to know which crop gives more profit in certain climatic condition. This paper proposes the use of a machine learning technique to help farmer predict the yield for a particular year.
