Open AccessImplementation of Effective Crop Selection by Using the Random Forest Algorithm
Author(s) -
R. Karthikeyan,
M Gowthami,
A Abhishhek,
P. Karthikeyan
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i3.34.19209
Subject(s) - random forest , yield (engineering) , crop yield , crop , machine learning , selection (genetic algorithm) , computer science , agriculture , algorithm , agricultural engineering , artificial intelligence , agronomy , engineering , ecology , materials science , metallurgy , biology
Accurate predictions of crop yield are critical for developing agriculture. We have provided a machine-learning method, Random Forests which has a ability to predict crop yield corresponds to the current climate and biophysical change. We have collected a large crop yield data from various sources. These data are used for both for the model training and testing.RF was found huge capable of predicting crop yields and over performed MLR standards in every performance statistics that were compared. From various results that shows that RF is an efficient machine-learning algorithm for crop predictions at current condition and has a huge accuracy in data analysis.