Open AccessDesign and Implementation of Various Regression Models for Yield Prediction
Author(s) -
V. K. Jain,
V. Vaidhehi
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.e2766.039520
Subject(s) - uttar pradesh , tamil , random forest , yield (engineering) , agriculture , regression analysis , statistics , linear regression , work (physics) , mathematics , predictive modelling , agricultural engineering , geography , computer science , engineering , machine learning , socioeconomics , economics , mechanical engineering , linguistics , philosophy , materials science , archaeology , metallurgy
Agriculture is the backbone of India. In order to support farmers in India, this research is focused on the design of various predictive models that are used to predict the yield value for a specific crop in Indian states. This research work considers Rice, Wheat, and Bajra crops in Tamil-Nadu, Rajasthan, Uttar Pradesh states respectively. The various regression models such as Linear, Multiple, C4.5 and Random Forest are considered in this work. R squared value is used to evaluate the performance of the regression models. The result of this work shows that Random Forest model is better in performance.