Evidence record 11439 · automatically gathered

Towards IoT-Fog-ML integration for temperature break detection and prediction in fresh produce cold chains: a systematic review and architectural framework

IntroductionGlobally, 1.3 billion tons of food is lost or wasted each year, negatively impacting food security, the economy, and the climate. Fresh fruits and vegetables (FFVs), with their short shelf life and temperature sensitivity, are the most affected. This study systematically evaluates the integration of Machine Learning (ML), Adaptive Learning (AL), the Internet of Things (IoT), and Fog computing for temperature-break detection and prediction in FFVs supply chains. It critically evaluate

Record details

Published: 17 July 2026
Source: Frontiers in Artificial Intelligence
Category: Research
Topics: Jobs & economy · Environment
Retrieved: 18 July 2026

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ethics.ai (17 July 2026), “Towards IoT-Fog-ML integration for temperature break detection and prediction in fresh produce cold chains: a systematic review and architectural framework,” evidence record 11439, https://ethics.ai/record/11439 (originally published by Frontiers in Artificial Intelligence).

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