With the emergence of artificial intelligence and computer science, data mining has received an enormous amount of boost. Recently, data mining algorithms have been successfully used in the field of agriculture for predicting the yield of crops. Crop yield prediction involves predicting the yield of crops from available historic data like weather parameters, soil parameters and historic crop yield. Regression is a data mining function that predicts a number. Regression techniques are very useful in predicting the yield of crops. In this study, the focus is on the development of regression techniques in agricultural field. Different regression techniques such as quadratic, pure-quadratic, interactions and polynomial are used for predicting the yields of wheat, maize and cotton crops. Finally regression models are proposed which are able to accurately predict the yields of cotton, maize and wheat. The best regression model is selected based on Root Mean Squared Error (RMSE), R2 and Mean Percentage Prediction Error (MPPE) values.
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