A Robust Ensemble Approach for Crop Recommendation Based on Environmental Parameters

In the era of smart agriculture, accurate crop recommendation plays a pivotal role in enhancing agricultural productivity and ensuring food security. Traditional methods of crop selection often rely on local practices and static knowledge, which may not account for dynamic environmental factors such as soil characteristics, temperature, rainfall, and humidity. This paper presents a robust ensemble machine learning framework for crop recommendation that integrates the predictive strengths of Random Forest, K-Nearest Neighbors, and Naïve Bayes classifiers. The proposed system is trained on a comprehensive dataset comprising environmental and agronomic parameters and aims to provide precise recommendations for optimal crop cultivation in a given region. The ensemble model, utilizing a voting mechanism, demonstrates superior performance in comparison to individual classifiers across multiple evaluation metrics, achieving a test accuracy of 97.40%. Experimental results indicate that the ensemble approach offers enhanced generalization, improved robustness, and reliable crop suggestions. This study underscores the potential of ensemble learning in supporting data-driven agricultural planning and paves the way for realworld applications such as mobile-based decision support systems for farmers.