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Automatic Fungi Recognition: Deep Learning Meets Mycology

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F22%3A43966003" target="_blank" >RIV/49777513:23520/22:43966003 - isvavai.cz</a>

  • Alternative codes found

    RIV/68407700:21230/22:00362939

  • Result on the web

    <a href="https://www.mdpi.com/1424-8220/22/2/633" target="_blank" >https://www.mdpi.com/1424-8220/22/2/633</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.3390/s22020633" target="_blank" >10.3390/s22020633</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Automatic Fungi Recognition: Deep Learning Meets Mycology

  • Original language description

    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.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    20205 - Automation and control systems

Result continuities

  • Project

    Result was created during the realization of more than one project. More information in the Projects tab.

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2022

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    SENSORS

  • ISSN

    1424-8220

  • e-ISSN

    1424-8220

  • Volume of the periodical

    22

  • Issue of the periodical within the volume

    2

  • Country of publishing house

    CH - SWITZERLAND

  • Number of pages

    22

  • Pages from-to

    1-22

  • UT code for WoS article

    000746955100001

  • EID of the result in the Scopus database

    2-s2.0-85122898591