Abstract: Agricultural commodity markets are highly volatile due to the combined influence of climatic variability, seasonal production cycles, supply–demand imbalances, transportation constraints, and policy interventions. In developing economies such as India, unpredictable market prices significantly affect farmers income stability, food security, and national economic planning. Traditional statistical forecasting approaches often fail to capture the complex, nonlinear, and temporal dependencies inherent in agricultural price data. This paper presents an AI-driven framework for market price forecasting and risk analysis of agricultural commodities using machine learning and deep learning techniques. Historical price data of major crops such as rice, wheat, maize, and tomato are analyzed to predict future price trends and assess market risks. Models including Autoregressive Integrated Moving Average (ARIMA), Random Forest Regression, and Long Short-Term Memory (LSTM) neural networks are implemented and evaluated. The proposed system integrates data preprocessing, feature engineering, predictive modeling, and risk assessment modules to provide actionable insights for farmers, traders, and policymakers. Experimental results demonstrate that deep learning models outperform traditional methods in forecasting accuracy, while the risk analysis module effectively quantifies price volatility and potential losses. The proposed framework supports informed decision-making and contributes to the development of intelligent agricultural market systems.

Keywords: Artificial Intelligence, Machine Learning, Agricultural Commodities, Price Forecasting, Risk Analysis, Time-Series Analysis

Downloads: | DOI: 10.17148/IJIREEICE.2026.14413