Development AI/ML Based Models for Predictions Prices for Agri-Horicultural Commodities Such as Pulses and Vegetables (Onion, Potato, Onion)

Abstract: Agricultural and horticultural commodity prices are subject to considerable fluctuations due to changes in supply and demand, market arrivals, seasonal variations, weather conditions, production levels, transportation costs, government policies, and other economic factors. Such price uncertainty creates difficulties for farmers in deciding when and where to sell their produce and also affects traders, consumers, and policymakers. his research focuses on the development of Artificial Intelligence (AI) and Machine Learning (ML) based models for predicting the prices of selected agri-horticultural commodities, namely onion, potato, and pulses. Several AI/ML algorithms are considered for price prediction, including Linear Regression. these models represent different approaches to learning relationships between historical market conditions and future commodity prices. Their predictive performance is evaluated using standard statistical measures such as short long- term memory (LSTM). The developed models can contribute to improved market planning, reduced price uncertainty, and more informed agricultural decision-making.

Keywords: Artificial Intelligence, Machine Learning, Price Prediction, Commodity Price Forecasting, Linear Regression, Long short- term memory(LSTM).