Published Paper Details:

The agricultural supply chains (Agri-SCs) are under increasing pressure of food-safety requirements, climate changes, post harvest losses and lack of trust and coordination between farmers, aggregators, processors and retailers. Compared to current project infrastructure and practices, blockchain technology (BCT) provides tamper-proof, decentralized processes for storing data and executing so-called smart contracts, and computational intelligence (CI) -- metaheuristic optimization, machine learning, deep learning, reinforcement learning and federated learning -- provides predictive and prescriptive insights from noisy distributed agricultural data. A systematic literature review (SLR) is presented of the research at BCT-CI intersection in Agri-SC management, including journal articles, conference papers and previous SLRs published primarily from 2019 until 2026. The literature is categorized into three threads: 1) blockchain-centric solutions for traceability and trust; 2) CI-centric solutions for forecasting and multi-objective optimization; and 3) co-developed blockchain-centric integrated CI solutions such as blockchain-anchored federated learning, AI-enhanced smart contracts, and blockchain-integrated hybrid metaheuristic-deep learning optimizers. A comparative taxonomy of platforms and algorithms is suggested, and common challenges are presented, such as scalability, interoperability, data quality, cost, and regulatory ambiguity, while future developments and directions are proposed to address these issues, including everything from privacy-preserving federated optimization to quantum-resilient ledgers and generative-AI-mediated multi-agent coordination.