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AI mushroom identification apps pose risks for foragers. Testing revealed even top tools fail nearly 15% of the time, proving unreliable for critical identification decisions.

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Area of Science:

  • Mycology
  • Artificial Intelligence
  • Computational Biology

Background:

  • AI-driven mushroom identification apps are increasingly popular among foragers.
  • Reliance on these tools carries significant risks due to potential inaccuracies.

Purpose of the Study:

  • To evaluate the accuracy and reliability of popular AI-based mushroom identification applications.
  • To assess the risks associated with using AI for critical mushroom identification.

Main Methods:

  • Over 100 photos of approximately 60 mushroom species were collected under real-world foraging conditions.
  • Multiple leading AI-based mushroom identification applications were tested using this dataset.

Main Results:

  • The best-performing AI tool exhibited an error rate of nearly 15%.
  • Other tested applications demonstrated even lower accuracy.
  • None of the applications consistently provided a single, correct identification across all tests.

Conclusions:

  • Current AI mushroom identification applications are not reliable enough for definitive identification.
  • Foragers should exercise extreme caution and not solely depend on AI for identifying mushrooms, especially concerning safety.