Rice Crop Yield Prediction Using Multi-Level Machine Learning Techniques

Abstract

Agriculture and allied production contributes to Indian economy and food security of India. Crop yield predictive model will help farmers and agriculture department and organization to take better decisions. In this paper we are proposingmulti-level, machine learning algorithms to predict rice crop yield. Here, data were collected from Indian Government website for 4 districts of Karnataka, i.e., Mysore, Mandya Raichur and Koppal, these data were publically available. In our proposed method initially, we have performed data pre-processing using z-score, normalization and Standardizing residuals on collected data, then multilevel decision tree and multilevel multiple linear regression methods are presented to predict the rice crop yield and evaluated the performance of both. The experimental results shows that the multiple linear regression is accurate than the decision tree technique. This prediction will guide the farmer to make better decision to gain better yield and for their livelihood in particular temperature or climatic scenario.