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AI-mediated risks and real-life challenges in mushroom foraging
Nik V Kuznetsov1,2, Yu Xia3, Sergei Kuznetsov3
1ASPIRE Precision Medicine Research Institute Abu Dhabi, United Arab Emirates University, Al Ain, United Arab Emirates. nikkuznetsov@inbox.com.
NPJ Science of Food
|February 24, 2026
Summary
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.
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.
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