Soft Voting Ensemble with Explainable AI for Sustainable Crop Recommendation

Successful crop recommendation is critical to make sustainable agriculture and food security possible in nations where farmers largely rely on traditional methods. This study proposes a machine learning-based approach employing ensemble techniques to enhance decision-making in crop recommendation. Multiple classifiers, including Random Forest, Naive Bayes, SVM, XGBoost, and ANN, were developed and compared, with the Soft Voting Ensemble model achieving the highest accuracy of 99.55%. Further, explainable artificial intelligence techniques like LIME were used to make model predictions interpretable to end users. It is evident that machine learning, when combined with explainable techniques, offers an effective solution to support farmers with transparent and fact-based recommendations. By providing both highly accurate predictions and clear justifications for each recommendation, the system enhances farmers’ trust and confidence in adopting technology-driven solutions. This synergy between ensemble learning and explainable AI ensures reliability and transparency, making it a significant step toward data-driven agricultural decision-making.