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Real-Time Classification of Ochratoxin a Contamination in Grapes Using AI-Enhanced IoT.

Mohamed Riad Sebti1, Zohra Dakhia1, Sonia Carabetta2

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Summary
This summary is machine-generated.

This study presents an AI-powered system for early detection of Ochratoxin A (OTA) in viticulture using environmental data. The low-power, on-site device offers a scalable solution for enhancing wine and grape product safety.

Keywords:
Internet of Thingsagrifoodedge computingochratoxin

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Area of Science:

  • Food Science and Technology
  • Agricultural Science
  • Environmental Monitoring

Background:

  • Ochratoxin A (OTA) contamination poses significant risks to wine and grape product safety and quality.
  • Early detection and management of OTA are crucial for the viticulture industry.

Purpose of the Study:

  • To develop and validate an autonomous, on-site system for early detection and management of Ochratoxin A (OTA) in viticulture.
  • To leverage environmental data and artificial intelligence for real-time OTA monitoring.

Main Methods:

  • A function based on chemical analysis was developed to estimate OTA concentrations and create a labeled synthetic dataset.
  • Two AI models were trained for OTA presence detection and concentration range classification.
  • Models were deployed on a low-power M5Stick C+ microcontroller for real-time, autonomous data processing and MQTT transmission.

Main Results:

  • The AI models demonstrated robust and consistent performance across multiple test sets.
  • The M5Stick C+ enabled rapid, low-power, real-time inference and data transmission.
  • The system proved to be a scalable, cost-effective, on-site solution for OTA monitoring.

Conclusions:

  • The developed system provides an effective enhancement to food safety in grape and wine production.
  • This autonomous approach reduces the need for domain experts and extensive resources for OTA detection.
  • The study validates a novel, integrated approach for proactive management of mycotoxin contamination in viticulture.