Development of an intelligent prediction tool for rice yield based on machine learning techniques

Abstract

Intelligent systems based on machine learning techniques. such as classification. clustering. are gaining Wide spread popularity in real world applications. This paper presents work on developing a software system for predicting rice yield from climate and plantation data. In this work. the main focu s is on classification and clustering techniques for data analysis based on statistical and machine learning approaches. Support vector machine algorithm is developed for classification of rice plantation data. Whereas kernel-based clustering algorithm is developed for finding clusters in climate data. The values of these parameters at various points oftime constitute time series. As the next step, correlation and regression analysis is applied for analyzing the impact of various parameters on the rice yield. and also for predicting the yield.