Block chain Enabled Farmer Centric Supply chain Management using Reinforcement learning

Farmers produce agri-food items for which purchasers pay in agri-food supply chains (ASCs). In the interim, purchasers accentuate the significance of guaranteeing the wellbeing of agri-food, and ranchers expect expanded income. Detectability and the board of agri-food items productively presents huge difficulties because of ASCs' dynamic and intricacy. Notwithstanding, most of current frameworks can't satisfy the necessities for ASC discernibility and the board. To begin, in order to guarantee decentralized security for agri-food, we develop a blockchain-based ASC design for item discernibility and use information in ASCs to address these issues. Then, an amazing decision is made about how to transport and store agri-food items to get the most out of them and make the most money using a Deep Reinforcement Learning (DR-SCM) method. A number of replication experiments are carried out so that the proposed blockchain- based architecture and DR-SCM system can be used in a variety of ASC scenarios. Implementing the recommended blockchain-based ASC configuration ensures meticulous thing conspicuousness, according to the disclosures. DR-SCM may also use Q-learning and heuristic approaches to boost item production.