Blockchain-Based Agri-Food Supply Chain

agri-food supply chain involves multiple stake- holders and complex processes, often leading to issues such as lack of transparency, data tampering, inefficiency in logistics, and reduced farmer profitability. Traditional centralized systems are vulnerable to manipulation, where false quality or production data can mislead consumers and disrupt market dynamics. To overcome these challenges, this research introduces an integrated framework that combines Blockchain technology with Deep Reinforcement Learning (DRL) to ensure both traceability and intelligent decision-making. The blockchain layer functions as a decentralized ledger that immutably records every transaction, enabling trust, accountability, and data integrity among farmers, distributors, retailers, and consumers. Smart contracts automate key operations such as registration, product transfer, and validation without third-party interference. Complementing this, the DRL-based Supply Chain Management (DR-SCM) model continuously learns from changing market conditions including demand, price trends, and logistics constraints to recommend optimal actions for production scheduling, inventory control, and sales timing. This adaptive intelligence allows farmers to maximize profits, minimize waste, and align supply with consumer demand in real time. Simulation results and performance evaluations indicate that the proposed Blockchain DRL framework significantly enhances transparency, operational efficiency, and profit optimization compared to traditional heuristic and static models. This work demonstrates a sustainable and intelligent approach to modernizing agri-food supply chain management through the synergy of secure distributed systems and advanced learning algorithms.