From Farm to Fork: Optimizing Cold-Chain Logistics through IoT and Machine Learning

As global food systems face pressure from population growth and climate volatility, cold-chain logistics remains a critical bottleneck for food security. This study investigates the optimization of "Farm to Fork" supply chains through the integration of Internet of Things (IoT) sensors and Agentic Machine Learning (ML). By 2026, the industry has shifted from passive monitoring to autonomous decision-making; however, empirical frameworks for this transition remain sparse.

This research proposes a Digital Twin (DT) architecture that synthesizes multi-modal IoT telemetry—including temperature, humidity, and ethylene gas—to create a real-time biological profile of perishable goods. We employ Long Short-Term Memory (LSTM) networks to forecast temperature excursions with a 3.5-hour lead time, achieving an R² accuracy of 0.91. Furthermore, we introduce an Agentic AI layer capable of autonomous rerouting, shifting the logistics paradigm from First-In, First-Out (FIFO) to a dynamic First-Expired, First-Out (FEFO) model.

Simulated testbed results indicate that the proposed system reduces post-harvest spoilage by 66% and decreases logistics-related energy consumption by 22% compared to baseline reactive models. Most notably, the transition to agentic autonomy reduced decision latency from 82 minutes to a near-instantaneous 1.2 seconds. These findings suggest that the convergence of IoT and ML enhances food security and provides a scalable pathway toward decarbonizing agricultural logistics. The paper concludes by addressing remaining barriers to adoption, specifically data interoperability and the necessity for edge-computing resilience in rural transit zones.