Crop Prediction Using Feature Selection And Ensemble Techniques

Abstract

Research in agriculture is expanding. Agriculture relies heavily on environmental and soil aspects, including temperature, humidity, and rainfall to anticipate crops. In the past, farmers had control over the selection of the crop to be grown, monitoring the development and timing of its harvest. The difficult process of forecasting crops in agriculture has resulted in the creation and testing of several models. such as Classification Techniques of Machine learning. The purpose of this research is to enhance the accuracy of the crop forecast by employing Ensemble Techniques. Ensembling In comparison to the current classification techniques, the Decision Tree, Support Vector Machine, and Random Forest algorithms perform better and provide greater accuracy.