Automatic Fungi Recognition: Deep Learning Meets Mycology

Research output: Contribution to journalJournal articleResearchpeer-review


  • Fulltext

    Final published version, 15 MB, PDF document

The article presents an AI-based fungi species recognition system for a citizen-science community. The system’s real-time identification too — FungiVision — with a mobile application front-end, led to increased public interest in fungi, quadrupling the number of citizens collecting data. FungiVision, deployed with a human-in-the-loop, reaches nearly 93% accuracy. Using the collected data, we developed a novel fine-grained classification dataset — Danish Fungi 2020 (DF20) — with several unique characteristics: species-level labels, a small number of errors, and rich observation metadata. The dataset enables the testing of the ability to improve classification using metadata, e.g., time, location, habitat and substrate, facilitates classifier calibration testing and finally allows the study of the impact of the device settings on the classification performance. The continual flow of labelled data supports improvements of the online recognition system. Finally, we present a novel method for the fungi recognition service, based on a Vision Transformer architecture. Trained on DF20 and exploiting available metadata, it achieves a recognition error that is 46.75% lower than the current system. By providing a stream of labeled data in one direction, and an accuracy increase in the other, the collaboration creates a virtuous cycle helping both communities.

Original languageEnglish
Article number633
Issue number2
Number of pages22
Publication statusPublished - 2022

Bibliographical note

Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.

    Research areas

  • Artificial intelligence, Classification, Computer vision, Fine-grained, Fungi, Machine learning, Recognition, Species, Species recognition

Number of downloads are based on statistics from Google Scholar and

No data available

ID: 291214411