In developing nations like India, the agri-food supply chain faces challenges and difficulties such as minimal traceability, limited transparency, and reliance on intermediaries, which can lead to farmer abuse and eroded customer trust. This study suggests and evaluates a paradigm that combines blockchain technology with artificial intelligence (AI) to improve agri-food systems' traceability, transparency, and equity. IoT-based sensing, IPFS for decentralised data storage, Ethereum-based smart contracts, and AI-driven analytics with a Random Forest model are all integrated into the system. Using a hypothetical onion supply chain case study, a four-layer architecture comprising user interface, AI analytics, blockchain infrastructure, and IoT data capture was created and assessed. Major performance gains over conventional systems are demonstrated by the results, which include a 35% decrease in operating expenses, 90% data consistency, and 75% increased transparency. A 93% task completion rate and a System Usability Score (SUS) of 78.2 were obtained from stakeholder usability testing. Proof-of-authority consensus has been suggested as a remedy for Sybil attacks and consensus delays, which are the issues in low-connectivity areas. A suitable roadmap for implementing blockchain-AI solutions in agriculture is presented in this paper. Multilingual interfaces, offline functionality, voice assistance, federated learning for privacy-preserving AI, and real-world scaling through platforms like Polygon or Hyperledger will be the main areas of future growth.
Burburi et al. (Wed,) studied this question.