An Agentic Explainable AI System for Agricultural Crop Production Prediction and Suitability Mapping

Existing agriculture cannot maximize crop production and selection due to complex interactions among environmental, management, and geographical factors. This paper presents a web application that forecasts crop yields and assesses crop suitability from structured agricultural data. The system provides two predictive modules: a Random Forest Regressor for forecasting crop production from ICRISAT historical data, and a pre-trained Deep Neural Network (DNN) for classifying crop suitability among Tamil Nadu districts from environmental and geographic inputs. An agent-based Explainable AI (XAI) workflow, powered by Large Language Models (Google Gemini and Groq’s Llama 3.1 via the phi-agent framework), automates the Random Forest pipeline—handling training, evaluation (MAE, R²), and feature importance extraction—while generating transparent, user-friendly explanations. DNN predictions are visualized on an interactive Folium map with accompanying LLM-generated explanations. This twin strategy offers both predictive output and actionable insights, making advanced analytics accessible to farmers and advisors via a Streamlit interface. The methodology and implementation show the system’s value for real-world agricultural decision support.