A Framework for Enhanced Crop and Fertilizer Recommendations Using Machine Learning, Explainable AI, and RPA

This work explores the challenge of crop and fertilizer prediction by leveraging machine learning and AI to enhance agricultural practices, particularly for small-scale farmers in India. A major contribution in this research is the use of Robotic Process Automation (RPA) based on the UiPath platform, which enhances the accuracy and efficiency of data collection. The basis of this research puts forward a new ensemble technique that combines XGBoost and Random Forest, which demonstrates better predictive accuracy than traditional models and other ensemble techniques. The proposed framework achieved an accuracy of 98.0% and an F1 score of 98.2% on crop and fertilizer recommendation tasks, outperforming baseline models by over 3%. These results demonstrate the model’s effectiveness and reliability in real-world agricultural scenarios. We present an integrated framework that combines these ensemble techniques with Explainable AI (XAI). This technique ensures that the generated predictions are explainable, thus making it possible for stakeholders to understand and accept the recommendations offered.