A STUDY AND ANALYSIS OF RICE AND WHEAT PRODUCTION IN INDIA BASED ON SEASON USING MACHINE LEARNING CLASSIFICATION ALGORITHMS

Indian agriculture is based on season and rainfall. Most of the crops are cultivated in Kharif and Rabi season. Nowadays drastic changes in climatic conditions and rain fall leads to the biggest threat for the crop cultivation and gain more yield to fulfil the requirement of increasing population. Most of the peoples need rice and wheat grains as their daily meal. So better and advanced technology required to analyse and find the strategies for crop cultivation, protecting the crop from diseases and maximizing the crop production. Machine Learning techniques played a vital role in analysis and management of crop, soil, season, rainfall, crop diseases and production.  This work attempts to find the efficient tree based ML classifier to classify the rice and wheat production data based on season. Random Forest, Random Tree, RepTree, NB Tree and J48 classifiers are used to classify the rice and wheat production data. The experimental results shows Random Forest classifier produces high accuracy value (98.3796%) in classification of crop production data based on season.