Analyzing the Impact of Feature Selection on Crop Yield Prediction

In agriculture sector, machine learning has been widely used by researchers for crop yield prediction. However, it is quite difficult to identify the most critical features from a dataset. Feature selection techniques allow us to remove the extraneous and noisy features from the original feature set. The feature selection techniques help the model to focus only on the important features of the data, thus reducing execution time and improving efficiency of the model. The aim of this study is to determine relevant subset features for achieving high predictive performance by using different feature selection techniques like Filter methods, Wrapper methods and embedded methods. In this work, different feature selection techniques like Rank-based feature selection technique, weighted feature selection technique and Hybrid Feature Selection Technique have been applied to the agricultural data. The optimal feature set returned by different feature selection techniques is used for yield prediction using Linear regression, Random Forest, and Decision Tree Regressor. The accuracy of prediction obtained using the above three methods has been analyzed by using different evaluation parameters. This study helps in increasing predictive accuracy with the minimum number of features.