The global food industry has faced significant challenges in pricing perishable commodities such as vegetables. As consumer demands for food quality increase and public and private standards for food safety and quality become more stringent, the window for selling these products at peak value has narrowed, and the financial impact of spoilage and waste has grown considerably. Optimizing the pricing of perishable goods is thus a critical concern. However, the unpredictability of consumer purchasing patterns, supply chain variations, seasonal changes, and product quality disparities make it difficult to determine the most effective pricing strategies. Traditional manual pricing methods struggle to meet these contemporary challenges, necessitating the adoption of advanced technologies such as Artificial Intelligence (AI) to automate and refine pricing decisions for maximized revenue. This research aims to apply reinforcement learning approach to enhance pricing strategies for perishable vegetable commodities.
