{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,26]],"date-time":"2026-07-26T02:05:38Z","timestamp":1785031538728,"version":"3.55.0"},"reference-count":36,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,2,5]],"date-time":"2025-02-05T00:00:00Z","timestamp":1738713600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The identification of species within filamentous fungi is crucial in various fields such as agriculture, environmental monitoring, and medical mycology. Traditional identification methods based on morphology have a low demand for advanced equipment usage and heavily depend on manual observation and expertise. However, this approach may struggle to differentiate between species in a genus due to their potential visual similarities, making the process time-consuming and subjective. In this study, we present an AI-based fungal species recognition model that utilizes deep learning techniques applied to time-lapse images. The training dataset, derived from fungi strains in the IBT Culture Collection, comprised 26,451 high-resolution images representing 110 species from 35 genera. The dataset was divided into a training set and validation subsets. We implemented three advanced deep learning architectures\u2014ResNet50, DenseNet-121, and Vision Transformer (ViT)\u2014to assess their effectiveness in accurately classifying fungal species. By utilizing images from early growth stages (days 2\u20133.5) for training and testing and later stages (days 4\u20137) for validation, our approach shortens the fungal identification process by 2\u20133 days, significantly reducing the associated workload and costs. Among the models, the Vision Transformer achieved the highest accuracy of 92.6%, demonstrating the effectiveness of our method. This work contributes to the automation of fungal identification, providing a reliable and efficient solution for monitoring fungal growth and diversity over time, which would be useful for culture collections or other institutions that handle a large number of new isolates in their daily work.<\/jats:p>","DOI":"10.3390\/info16020109","type":"journal-article","created":{"date-parts":[[2025,2,6]],"date-time":"2025-02-06T04:47:31Z","timestamp":1738817251000},"page":"109","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Automated Fungal Identification with Deep Learning on Time-Lapse Images"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6492-7858","authenticated-orcid":false,"given":"Marjan","family":"Mansourvar","sequence":"first","affiliation":[{"name":"Department of Biotechnology and Biomedicine (DTU Bioengineering), Technical University of Denmark, S\u00f8ltofts Plads, 2800 Kongens Lyngby, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karol Rafal","family":"Charylo","sequence":"additional","affiliation":[{"name":"Department of Electrical and Photonics Engineering (DTU Electro), Technical University of Denmark, S\u00f8ltofts Plads, 2800 Kongens Lyngby, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3799-6062","authenticated-orcid":false,"given":"Rasmus John Normand","family":"Frandsen","sequence":"additional","affiliation":[{"name":"Department of Biotechnology and Biomedicine (DTU Bioengineering), Technical University of Denmark, S\u00f8ltofts Plads, 2800 Kongens Lyngby, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steen Smidth","family":"Brewer","sequence":"additional","affiliation":[{"name":"Department of Biotechnology and Biomedicine (DTU Bioengineering), Technical University of Denmark, S\u00f8ltofts Plads, 2800 Kongens Lyngby, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7650-9300","authenticated-orcid":false,"given":"Jakob Bl\u00e6sbjerg","family":"Hoof","sequence":"additional","affiliation":[{"name":"Department of Biotechnology and Biomedicine (DTU Bioengineering), Technical University of Denmark, S\u00f8ltofts Plads, 2800 Kongens Lyngby, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1653","DOI":"10.1016\/j.cub.2021.01.074","article-title":"A genome-scale phylogeny of the kingdom Fungi","volume":"31","author":"Li","year":"2021","journal-title":"Curr. 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