Crop Price Prediction Using Machine Learning

Agricultural commodity price forecasting represents one of the most critical challenges in modern agricultural economics, directly impacting millions of stakeholders across the global food supply chain. Traditional statistical and econometric methods have proven inadequate in modeling the complex, non-linear dynamics that characterize contemporary agricultural markets. This comprehensive survey examines the transformative role of machine learning and artificial intelligence technologies in revolutionizing crop price prediction systems. Through systematic analysis of advanced algorithmic approaches, this paper investigates the implementation of Long Short-Term Memory (LSTM) networks, eXtreme Gradient Boosting (XGBoost), and hybrid ensemble methodologies in agricultural price forecasting applications. The survey synthesizes findings from multiple technological implementations demonstrating that machine learning approaches can achieve remarkable accuracy rates, with hybrid LSTM+XGBoost systems achieving up to 91% prediction accuracy in crop price forecasting tasks. This paper provides critical insights into the integration of weather data, seasonal patterns, and economic indicators with advanced predictive models, highlighting both technological capabilities and implementation challenges. The findings indicate significant potential for AI-driven systems to enhance agricultural market efficiency while supporting informed decision-making for farmers, traders, and policymakers.