CROP SELECTION OPTIMIZATION USING ENSEMBLE CLASSIFICATION

Abstract

The optimum crop for a location must be chosen in order to boost agricultural productivity, but this is not always a simple process. The study's overarching objective is to provide farmers with a fresh, data-driven strategy for improving their crop selection abilities. It builds a robust prediction engine by combining numerous machine learning approaches rather than depending on just one. This class has methods such as Gradient Boosting, Random Forest, and Support Vector Machines. Climate, soil, and crop production data are all part of an ensemble model's big dataset, which it uses to make better predictions. It is clear from the results that this combined approach outperforms using just one model. It also provides agricultural planners and farmers with a potent tool that can make farming more sustainable while simultaneously increasing yields. Last but not least, this approach integrates cutting-edge technology with scientific concepts to enhance the efficacy and efficiency of farming.