From Traceability to Predictive Intelligence: A Systematic–Narrative Review of IoT, Blockchain, and AI in Agriculture

Abstract

The convergence of Internet of Things (IoT), blockchain, and artificial intelligence (AI) has emerged as a transformative force in advancing sustainable agriculture. These technologies collectively address persistent challenges in traceability, resource optimization, data security, and decision support. This review conducts a systematic–narrative analysis of journal publications from 2020 to 2025, synthesizing insights across three thematic dimensions: (i) IoT–Blockchain frameworks for agricultural supply chain traceability, (ii) IoT–AI/ML approaches for predictive farming and crop health monitoring, and (iii) blockchain-driven data management for breeding and genomic applications. Across the themes, the reviewed studies demonstrate substantial improvements, including enhanced supply chain transparency, predictive accuracy exceeding 95% in crop disease classification, and scalable storage architectures for high-throughput breeding data. However, challenges persist in interoperability, scalability, energy efficiency, and end-user adoption. The review identifies critical research gaps and emphasizes the need for integrated frameworks that balance technological sophistication with practical usability. By consolidating current advances and limitations, this study provides a comprehensive understanding of the evolving IoT–Blockchain–AI ecosystem in agriculture and outlines future directions toward sustainable, transparent, and intelligent agri-food systems.