Crop Recommender System Based on Ensemble Classifiers

Abstract

Farmers are facing problems because they are unable to manage cultivation because of bad weather conditions and uneven rainfall. Thus, to reduce the problems of farmers, the latest technologies are introduced such as machine learning to implement crop recommendation systems. A wide range of classification techniques are used, and a specific model is selected based on their accuracy levels. By using feature selection techniques, the raw data is converted into a dataset which is useful for efficiently training the model with relevant data. Reducing redundant data and utilizing just the aspects that are significantly relevant in deciding the model’s final output will improve the model’s accuracy. The findings show that, compared to other classifiers, the ensemble approach delivers better prediction with a 99.54% accuracy rate. document is a ‘‘live’’ template and already defines the components of your paper [title, text, heads, etc.] in its style sheet.