Blockchain, IoT and AI Race to Fix the World’s Broken Food Supply Chain

Every third bite of food the world produces never reaches a human mouth. Roughly one third of all food produced for human consumption is lost or wasted somewhere between the farm and the fork, generating greenhouse gas emissions, deepening food insecurity and draining more than one trillion dollars from the global economy every year. A new systematic review published in Food Science & Nutrition argues that the tools needed to reverse this trajectory already exist, and that they are digital: blockchain, the Internet of Things (IoT) and artificial intelligence (AI), working in concert across the agri-food supply chain.

The review, which screened more than 250 records from Google Scholar, Scopus, Web of Science, IEEE Xplore and industry reports and selected over 100 studies published between 2017 and 2026, maps how Industry 4.0 technologies are reshaping every stage of the journey from primary production to final distribution. The stakes could hardly be higher. Global food demand is projected to rise by between 59 and 98 percent by 2050, while climate change erodes crop productivity, water resources, soil quality and even the nutritional quality of what is grown. The European Green Deal and its Farm to Fork strategy have set 2050 as the target for the continent’s first climate-neutral status, placing the food supply chain squarely at the center of sustainability policy.

At the heart of the technological argument is blockchain, a distributed ledger that keeps an ever-growing list of data entries that every node in the network checks. Unlike conventional databases, records on a blockchain cannot be altered or deleted without the consensus of all participants, a property known as immutability. Combined with cryptographic hashing, decentralization and programmable smart contracts, the technology promises tamper-proof documentation of a product’s complete journey, from the supplier of raw materials to the consumer’s shopping basket. The review highlights the now-famous Walmart and IBM pilot from 2016, in which tracing a package of mangoes to its farm of origin fell from seven days to just 2.2 seconds, with recall costs reportedly reduced by around 30 percent.

That speed matters most when things go wrong. Food recalls are among the most damaging events a food company can face, and complex supply chains can cost up to 93 billion dollars while raising recall risks. Blockchain-based traceability allows companies to narrow recall ranges, identify and isolate products from specific suppliers, and act on real-time product information rather than paper records that are laborious and error-prone. The review also cites tamper-proof records for organic certifications that increased consumer trust by 50 percent, decentralized ledgers for halal food traceability supporting compliance with EU and MENA regulations, and IoT-blockchain integration for seafood provenance that eliminated counterfeit products.

Where blockchain provides trust, the Internet of Things provides eyes. IoT encompasses wireless sensor networks, geographic information systems, GPS, radio-frequency identification and sensors that continuously capture data on temperature, humidity, soil moisture and location. In logistics, RFID sensors for real-time cold-chain monitoring cut food spoilage by 22 percent in one cited study, while autonomous drones for warehouse inventory management increased efficiency by 35 percent. Soil moisture sensors for precision irrigation delivered water savings of 25 percent, and satellite imaging for crop health monitoring raised yields by 15 percent. GPS-enabled tracking of livestock movement has even helped prevent disease outbreaks on farms.

The review is candid that no single technology can solve every problem on its own. Blockchain scores excellently on traceability and security but carries high implementation costs and only moderate scalability. IoT offers excellent real-time monitoring and scalability but is vulnerable to cyberattacks across three distinct layers of its networks: the application layer, the network layer and the perception layer, where open insecure ports, weak login mechanisms and inadequate encryption create entry points for attackers. AI provides excellent decision support but demands enormous volumes of high-quality data and substantial processing power. The authors argue that only an integrated framework, in which sensors feed secure ledgers and intelligent algorithms, delivers the transparency, safety, efficiency and sustainability the sector needs.

Artificial intelligence is the third pillar, and arguably the most rapidly evolving. Machine learning systems can recognize patterns in vast datasets, forecast demand for perishable goods and optimize harvest schedules; one cited study reports AI-driven demand forecasting improved accuracy by 40 percent. Artificial neural networks, inspired by the human brain, scale from small garden setups to regional monitoring systems, supporting everything from localized disease detection to large-scale yield prediction. Convolutional neural networks excel at spatial tasks such as crop-weed separation and fruit maturity assessment, while recurrent neural networks handle sequential data like plant growth monitoring. Machine learning algorithms for dynamic pricing of surplus food reduced waste by 18 percent, and robotics for automated food picking cut labor costs by 60 percent.

Less obvious but equally consequential is the role of natural language processing. By applying topic modeling techniques such as Latent Dirichlet Allocation to customer reviews, companies can identify recurring themes in consumer discourse about product quality, supplier relationships and delivery timetables. Monitoring shifts in topic prevalence can flag emerging problems, for instance a rise in conversations about product damage that may indicate packing or handling failures. Sentiment analysis converts subjective customer feedback into data-driven decisions, while speech processing, which has advanced from Bell Labs’ 1952 Audrey system to near human-like recognition, now supports agricultural question-and-answer systems, livestock farming and unmanned aerial vehicles, helping to address labor shortages.

The obstacles, however, remain formidable. Cutting-edge technologies are often prohibitively expensive for small-scale producers and smallholder farmers, precisely the actors who dominate global food production. Integrating numerous systems across multiple supply chain participants creates interoperability problems that make seamless data sharing difficult. Data security and privacy concerns grow as digital platforms proliferate, and many workers lack the digital literacy to use these tools effectively, calling for large-scale training programs. Skepticism and resistance to change further slow adoption. The review also flags a deeper epistemological weakness: blockchain’s immutability guarantees that records cannot be altered after upload, but it cannot ensure the veracity of raw data before it reaches the ledger, the so-called garbage-in problem that no cryptographic technique can solve alone.

Looking forward, the authors chart an ambitious research agenda. They call for unified AI-blockchain-IoT platforms connecting sensing, secure data management and intelligent decision-making; explainable AI to build stakeholder trust in algorithmic agricultural decisions; blockchain interoperability standards; edge computing that processes data closer to farms and warehouses; federated learning that enables collaborative model development without sharing sensitive data; and green blockchains with energy-efficient consensus mechanisms. Digital twins, virtual representations of farms, logistics networks and food products, could allow simulation and optimization before real-world deployment, while smart cold-chain systems combining IoT sensing, AI prediction and blockchain traceability target spoilage directly. Crucially, they warn that many proposed systems remain stuck at the conceptual or prototype stage, and that longitudinal studies and large-scale pilot projects are needed to validate the technology’s promise. If those pilots succeed, the farm-to-fork journey may finally become as transparent, efficient and sustainable as the food system of 2050 demands.

Subject of Research: Digital transformation of sustainable agri-food supply chains using blockchain, IoT and artificial intelligence

Article Title: Towards a Sustainable Food Supply Chain: Digital Transformation via Blockchain, IoT and AI

Article References: Jabbar, Z., Liaqat, A., Ahsan, S., Iqbal, R., Khaliq, A., Chughtai, M. F. J., Faiz, F., Bashir, M. Z., Mehmood, T., Bugingo, E., & Khalid, M. Z. (2026). Towards a Sustainable Food Supply Chain: Digital Transformation via Blockchain, IoT and AI. Food Science & Nutrition, 14(10), Article e72422. https://doi.org/10.1002/fsn3.72422

Image Credits: AI Generated

DOI: 10.1002/fsn3.72422

Keywords: blockchain, Internet of Things, artificial intelligence, food supply chain, food traceability, food waste, smart farming, smart contracts, machine learning, food safety, sustainability, agri-food