Perishable Goods Monitoring with AI-Powered IoT and Blockchain

Abstract

Perishable goods—from fresh produce and dairy to seafood and biologics—degrade rapidly when storage and transport conditions deviate from their narrow optimal ranges. Temperature excursions, humidity swings, ethylene accumulation, shocks, and delays compound along complex, multi-actor supply chains, contributing to large-scale food loss, diminished quality, and safety incidents. Contemporary monitoring is fragmented, post-hoc, and siloed; data fidelity and trust are frequently contested, hampering coordinated response. This manuscript proposes an integrated architecture that combines (i) lowpower IoT sensing across storage and transit nodes, (ii) ondevice and edge AI for anomaly detection and Remaining Shelf Life (RSL) prediction, and (iii) a permissioned blockchain that notarizes critical events, enforces smartcontract guardrails, and anchors verifiable off-chain telemetry. We review relevant literature on cold-chain IoT, intelligent packaging (e.g., time-temperature indicators and Date of Publication: 10-06-2026 e-noses), shelf-life modeling with machine learning, and blockchain traceability. We then outline a methodology for deploying a hybrid edge–cloud pipeline: TinyML models perform real-time detection on microcontrollers; cloud services run multi-task learning for RSL and risk scoring; hashed sensor batches are committed to a Hyperledger Fabric ledger; bulky streams are stored via contentaddressed IPFS with on-chain references. A simulation study models 10,000 shipments across farm–DC–retail legs, benchmarking baseline operations vs. the proposed system under realistic latency, packet loss, and workload profiles. Results (simulation) suggest reductions in temperatureexcursion minutes, faster root-cause analysis, and improved traceability with low verification overhead. We conclude with implementation guidance, statistical measures for validation, and research opportunities in privacypreserving analytics and policy-aligned data governance. Key background figures on global food loss and the benefits of traceable cold chains motivate the approach.