Optimizing Crop Forecasts: Leveraging Feature Selection and Ensemble Methods

Abstract

Agricultural research is undergoing significant advancements, particularly in the realm of crop forecasting. Historically, the success of agriculture has been deeply intertwined with understanding environmental and soil variables, such as temperature, humidity, and precipitation, as these factors play a pivotal role in crop growth and yield. Traditionally, farmers made informed decisions about which crops to plant, monitored their growth, and determined the optimal harvest time. However, predicting crop outcomes has always been a complex endeavor. To address this challenge, various models, especially Classification Techniques of Machine Learning, have been developed and tested. This study focuses on improving crop prediction accuracy by leveraging Ensemble Techniques. When comparing the Ensemble approach with existing classification methods, it was observed that algorithms like Decision Tree, Support Vector Machine, and Random Forest outperformed their counterparts and delivered superior accuracy.