ArticleSensors (Basel, Switzerland)2022
Automatic Fungi Recognition: Deep Learning Meets Mycology.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Novel imaging approaches for visualizing root-mycorrhizal fungal interactions.Journal of experimental botany · 2026Review
- Advancing mold identification in the routine laboratory, performance of smartphone-based imaging and a newly developed convolutional neural network.Microbiology spectrum · 2026Article
- Article
- Classification of Mycena andSensors (Basel, Switzerland) · 2025Article
- Review
- Deep Learning-Based Classification of Macrofungi: Comparative Analysis of Advanced Models for Accurate Fungi Identification.Sensors (Basel, Switzerland) · 2024Article
- Exploring Fungal Diversity in Seagrass Ecosystems for Pharmaceutical and Ecological Insights.Journal of fungi (Basel, Switzerland) · 2024Review
- Article
- Unlocking secrets of microbial ecotoxicology: recent achievements and future challenges.FEMS microbiology ecology · 2023Review
- Plant Parasitic Nematode Identification in Complex Samples with Deep Learning.Journal of nematology · 2023Article
- An artificial intelligence model to identify snakes from across the world: Opportunities and challenges for global health and herpetology.PLoS neglected tropical diseases · 2022Article
- A Small Sample Recognition Model for Poisonous and Edible Mushrooms based on Graph Convolutional Neural Network.Computational intelligence and neuroscience · 2022Article
- The power of citizen science to advance fungal conservation.Conservation lettersArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.