Utilizing Artificial Intelligence to Forecast Market Trends and Enhance Supply Chain Strategies in Agriculture

This investigation elucidates the transformative role of Artificial Intelligence (AI) in revolutionizing agriculture across North America, with a focus on the United States, Canada, and Mexico.By leveraging advanced AI methodologies, particularly Gated Re-current Units (GRUs)-a sophisticated variant of Recurrent Neural Networks (RNNs)-this study addresses pressing agricultural challenges, including market volatility, demand forecasting, and price fluctuations.GRUs were selected for their efficacy in handling sequential data, mitigating issues like vanishing gradients, and delivering precise predic-tions for crops such as maize and potatoes.Performance metrics, including Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), demonstrate exceptional accuracy, notably for maize yields in Mexico (RMSE: 1224) and potato yields in Canada (RMSE: 23145).Utilizing comprehensive crop yield datasets, this research underscores AI's ability to provide actionable insights, enabling farmers, suppliers, and distributors to optimize inventory, reduce waste, and strategically time market entry.The study also explores market scenario simulations, adoption barriers such as data accessibility, and the need for stakeholder training.Through detailed case studies, we illustrate AI's capacity to fortify agricultural supply chains, enhancing adaptability to dynamic market conditions.These findings affirm AI's potential to foster resilience, efficiency, and profitability, offering stakeholders critical tools for resource management and long-term strategic planning.