Scalable and Efficient Food Quality Monitoring Using CNN for Supply Chain Optimization

  • Unique Paper ID: 175785
  • Volume: 11
  • Issue: 11
  • PageNo: 5018-5021
  • Abstract:
  • Ensuring the accurate prediction of the Remaining Shelf-Life (RSL) for Fresh Fruits and Vegetables (FFVs) during transportation is vital for effective planning and cost management. The Internet of Things (IoT) allows real-time processing of environmental data, but existing RSL models are often qualitative, invasive, or static. This study presents a new, validated RSL model that dynamically estimates general decay rates based on respiration rates, integrating them over time. Unlike previous methods, it is non-invasive and does not require pre-deployment experiments. A simplified surrogate model was also developed to facilitate real-time applications in IoT systems. Testing with various fresh products showed minimal prediction errors, proving the model's reliability.

Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 11
  • PageNo: 5018-5021

Scalable and Efficient Food Quality Monitoring Using CNN for Supply Chain Optimization

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