Sugarcane Crop Yield Prediction Using Data Mining Application by Weka Machine Learning Tool.

Abstract

Data Mining concept is very useful for to discover important hidden patterns from large data. About 65percent of population in Sangli district is engaged in agriculture activities with sugarcane largely cultivated in irrigated area. For this study we have collected data sample from sangli district. In agriculture domain, crop yield prediction is very important and crucial. Agriculture domain problem that remains to be solved on the available past (historical) data.

In this research paper researcher has focused on the impact of weather/climate parameters as rainfall, temperature. On the sugarcane crop productivity. Data mining is very innovative research area in agriculture crop yield analysis. Ms-Excel is used for graphical presentation of crop data and weather data parameters. Weka is a machine learning tool specially we can used for prediction of result variable. We can find association between dependent and independent variables. This work aims at finding reliable data mining techniques to achieve a high accuracy result of yield prediction. In this research paper we noted actual and predicted crop productivity using linear regression and Smoreg(SVM).