Open AccessExperimental Analysis of Machine Learning Algorithms Based o n Agricultural Dataset f or Improving Crop Yield Prediction
Author(s) -
Kusum Lata,
Sajidullah S. Khan
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.f9308.109119
Subject(s) - c4.5 algorithm , naive bayes classifier , machine learning , agriculture , decision tree , random forest , computer science , artificial intelligence , algorithm , adaboost , tree (set theory) , statistical classification , data mining , support vector machine , mathematics , geography , mathematical analysis , archaeology
Agriculture is the primary research study area in India as agriculture is the main source of income for various communities. In classification algorithm for agricultural dataset according to production, area, crop and seasons. Here, four classification algorithms are used with the help of WEKA tool. These algorithms are namely the present scenario, there is a call to renovate the enormous agriculture data into diverse technologies and make them accessible to the farmer for improved decision making. The endeavor of this work is to find out the finest Random Tree, J48, Bayes Net and KStar etc. The captured results revealed that Random tree algorithm performed well in terms of error rate and provides slightly better performance than KStar, Bayes Net and J48 classifiers. In this paper, our objective is to apply machine learning techniques to mine constructive information from the agricultural dataset to improve the crop yield prediction for major crops in Nashik district of Maharashtra.