machine learning

Accurate crop recommendation systems are essential for optimizing agriculturalproductivity and sustainability, yet existing approaches often fail to integrate diverse environmental factors and adapt to location-specific conditions. This study proposes a hybrid machine learning model that leverages soil and climate parameters through a threestage pipeline: Random Forest for feature selection, Extreme Gradient Boosting for robust prediction, and a lightweight Feed forward Neural Network for final decision-making.

Due to lack of scalability of feature selection algorithms when applied in a centralized manner, most classification algorithms perform sub-optimally especially in the presence of irrelevant and redundant features in high dimensional datasets-large feature size small instances. Though it is imperative to remove insignificant features to improve learning, the process is complex and time-consuming.