Abstract—The global agri-food supply chain operates as a highly fragmented, multi-tiered ecosystem spanning smallholder farms, seed and agrochemical suppliers, grain elevators, processing plants, cold-chain logistics carriers, wholesale distributors, retail supermarkets, and regulatory food safety authorities. Despite rapid advancements in Precision Agriculture and the Internet of Agricultural Things (IoAT), contemporary food supply chain management systems remain predominantly reliant on centralized client-server databases. This structural centralization introduces severe vulnerabilities, including single-point-of-failure data loss, asymmetric information silos, tampering with organic and Protected Designation of Origin (PDO) certifications, economically motivated food adulteration, delayed foodborne pathogen recall workflows, and protracted financial settlement cycles for smallholder farmers. Blockchain technology—characterized by decentralized consensus, immutable cryptographic hash chaining, content-addressed off-chain storage, and deterministic smart contracts—provides a trustless cyber-physical foundation to resolve these systemic inefficiencies. This paper presents a comprehensive architectural, mathematical, and operational investigation of blockchain technology across the smart agriculture and agri-food value chain. We formalize a five-layer Farm-to-Fork reference architecture integrating LoRaWAN/RFID field perception, GS1 Electronic Product Code Information Services (EPCIS), InterPlanetary File System (IPFS) off-chain storage, permissioned consortium ledgers (Hyperledger Fabric and Sawtooth), and automated smart contracts. We formulate rigorous mathematical models for mass-balance anti-dilution verification, Arrhenius kinetic shelf-life decay during cold-chain transit, reputation-weighted decentralized weather-oracle parametric crop insurance payouts, and dual-storage compression efficiency. Finally, we synthesize real-world industrial pilots, benchmark empirical transaction latency, CPU load, and storage scalability across leading frameworks, and propose concrete solutions to the 'Garbage-In, Garbage-Out' (GIGO) physical-to-digital oracle dilemma.
No takes yet. Share an insight, caveat, or question.
Kumar et al. (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: