Optimized Method for Prediction and Recommendation of Crops Using Fusion Ensemble Learning Crop Recommendation Model

Fusion Ensemble Learning Crop Recommendation Model introduces an advanced crop recommendation system utilizing machine learning (ML), Robotic Process Automation (RPA) with UiPath, and Explainable AI (XAI). The system aggregates historical and real-time IoT data on weather and soil nutrient content to provide optimized recommendations for crops and NPK (Nitrogen, Phosphorus, and Potassium). By creating an FELCR (Fusion Ensemble Learning Crop Recommendation model) using Random Forest, CatBoost, and XGBoost models, the system achieves robust and accurate crop predictions under various environmental conditions. UiPath automates the collection of this data, streamlining the process and ensuring farmers have timely inputs. To enhance transparency, the model incorporates XAI techniques, specifically LIME (Local Interpretable Model-agnostic Explanations), which allows farmers to understand and trust the recommendations, making the system highly user friendly. The ensemble model demonstrated an accuracy of 99.70%, and its recommendations have been widely adopted by farmers. This study aligns with the United Nations Sustainable Development Goals (SDGs), particularly Goal 2, promoting sustainable and efficient agricultural practices.