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Related Experiment Video

Updated: Jun 28, 2026

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Combining AI Tools with Non-Destructive Technologies for Crop-Based Food Safety: A Comprehensive Review.

Hind Raki1, Yahya Aalaila1, Ayoub Taktour2

  • 1College of Computing, University Mohammed VI Polytechnic, Ben Guerir 43150, Morocco.

Foods (Basel, Switzerland)
|January 11, 2024
PubMed
Summary
This summary is machine-generated.

This review explores Artificial Intelligence (AI) tools for crop food safety. It highlights limited research on AI analytical strategies for ensuring safety from farm to fork.

Keywords:
chemometricsfood contaminantsfood processesmachine learningspectroscopysustainability

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Area of Science:

  • Agricultural Science
  • Food Science
  • Computer Science

Background:

  • Food safety and security are critical global concerns across the farm-to-fork continuum.
  • The agrifood system's complexity necessitates advanced analytical methods for maintaining food safety.
  • Machine learning (ML) and deep learning (DL) are emerging as key technologies for food safety assessment.

Purpose of the Study:

  • To review and synthesize ongoing research on Artificial Intelligence (AI) applications in crop food safety.
  • To provide a consensus view on analytical strategies using AI for food-crop safety.
  • To identify gaps in the literature regarding AI's role in crop-based food safety.

Main Methods:

  • Conducted an exhaustive literature review using the "TriScope Keywords-based Synthesis" methodology.
  • Applied strict inclusion and exclusion criteria to select relevant studies.
  • Performed deep reading and analysis of selected papers to extract key information.

Main Results:

  • Identified a growing body of research on AI in food safety, particularly ML and DL.
  • Found limited studies specifically focusing on the analytical application of AI tools for crop-based food safety.
  • Highlighted the novelty of this comprehensive review in addressing this specific research area.

Conclusions:

  • AI tools offer significant potential for enhancing crop food safety assessment.
  • Further research is needed to develop and integrate AI-driven analytical strategies for crop safety.
  • This review serves as a foundational resource for future studies in AI for food-crop safety.