{
  "id": 11439,
  "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1830032",
  "title": "Towards IoT-Fog-ML integration for temperature break detection and prediction in fresh produce cold chains: a systematic review and architectural framework",
  "summary": "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",
  "authors": "Jeremiah Taguta",
  "category": "research",
  "topics": "jobs-economy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-17T00:00:00.000Z",
  "fetched_at": "2026-07-18T05:10:55.931Z",
  "source_slug": "x-frontiers-in-artificial-intelligence",
  "source_name": "Frontiers in Artificial Intelligence",
  "source_homepage": "https://www.frontiersin.org/journals/artificial-intelligence",
  "ethics_ai_record_url": "https://ethics.ai/record/11439",
  "original_url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1830032",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}