A Classifier Ensemble Approach for Prediction of Rice Yield based on Climatic Variability for Coastal Odisha Region of India

Abstract

Agriculture is the backbone of Indian economy especially rice production, but due to several reasons the expected rice yields are not produced. The rice production mainly depends on climatic parameters such as rainfall, temperature, humidity, wind speed etc. If the farmers can get the timely advice on variation of climatic condition, they can take appropriate action to increase the rice production. This factor motivate us to prepare a computational model for the farmers and ultimately to the society also. The main contribution of this work is to present a classifier ensemble based prediction model by considering the original rice yield and climatic datasets of coastal districts Odisha namely Balasore, Cuttack and Puri for the period of 1983 to 2014 for Rabi and Kharif seasons. This ensemble method uses five diversified classifiers such as Support Vector Machine, k-Nearest Neighbour, Naive Bayesian, Decision Tree, and Linear Discriminant Analysis. This is an iterative approach; where at each iteration one classifier acts as main classifier and other four classifiers are used as base classifiers whose output has been considered after taking the majority voting. The performance measure increases 95.38% to 98.10% and 95.38% to 98.10% for specificity, 88.48% to 96.25% and 83.60% to 94.81% for both sensitivity and precision and 91.78% to 97.17% and 74.48% to 88.59% for AUC for Rabi and Kharif seasons dataset of Balasore district and also same improvement in Puri and Cuttack District. Thus the average classification accuracy is found to be above 96%.