Academic Journal
Innovative infrastructure to access Brazilian fungal diversity using deep learning
| العنوان: | Innovative infrastructure to access Brazilian fungal diversity using deep learning |
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| المؤلفون: | Thiago Chaves, Joicymara Santos Xavier, Alfeu Gonçalves dos Santos, Kelmer Martins-Cunha, Fernanda Karstedt, Thiago Kossmann, Susanne Sourell, Eloisa Leopoldo, Miriam Nathalie Fortuna Ferreira, Roger Farias, Mahatmã Titton, Genivaldo Alves-Silva, Felipe Bittencourt, Dener Bortolini, Emerson L. Gumboski, Aldo von Wangenheim, Aristóteles Góes-Neto, Elisandro Ricardo Drechsler-Santos |
| المصدر: | PeerJ, Vol 12, p e17686 (2024) |
| بيانات النشر: | PeerJ Inc., 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Medicine LCC:Biology (General) |
| مصطلحات موضوعية: | Deep learning, Computer vision, CNN, Image classification, Fungi, Medicine, Biology (General), QH301-705.5 |
| الوصف: | In the present investigation, we employ a novel and meticulously structured database assembled by experts, encompassing macrofungi field-collected in Brazil, featuring upwards of 13,894 photographs representing 505 distinct species. The purpose of utilizing this database is twofold: firstly, to furnish training and validation for convolutional neural networks (CNNs) with the capacity for autonomous identification of macrofungal species; secondly, to develop a sophisticated mobile application replete with an advanced user interface. This interface is specifically crafted to acquire images, and, utilizing the image recognition capabilities afforded by the trained CNN, proffer potential identifications for the macrofungal species depicted therein. Such technological advancements democratize access to the Brazilian Funga, thereby enhancing public engagement and knowledge dissemination, and also facilitating contributions from the populace to the expanding body of knowledge concerning the conservation of macrofungal species of Brazil. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2167-8359 |
| Relation: | https://peerj.com/articles/17686.pdf; https://peerj.com/articles/17686/; https://doaj.org/toc/2167-8359 |
| DOI: | 10.7717/peerj.17686 |
| URL الوصول: | https://doaj.org/article/dab2766956aa44b399361e9c085b3220 |
| رقم الانضمام: | edsdoj.b2766956aa44b399361e9c085b3220 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 21678359 |
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| DOI: | 10.7717/peerj.17686 |