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
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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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