• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

利用深度学习获取巴西真菌多样性的创新基础设施。

Innovative infrastructure to access Brazilian fungal diversity using deep learning.

机构信息

Brazilian National Institute for Digital Convergence-INCoD, Universidade Federal de Santa Catarina, Florianópolis, Santa Catarina, Brazil.

Institute of Agricultural Science, Universidade Federal dos Vales do Jequitinhonha e Mucuri, Unaí, Minas Gerais, Brazil.

出版信息

PeerJ. 2024 Jul 9;12:e17686. doi: 10.7717/peerj.17686. eCollection 2024.

DOI:10.7717/peerj.17686
PMID:39006015
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11243970/
Abstract

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.

摘要

在本次研究中，我们采用了由专家精心构建的全新数据库，其中包含了在巴西野外采集的大型真菌，拥有超过 13894 张代表 505 个不同物种的照片。使用该数据库的目的有两个：首先，为具有自主识别大型真菌物种能力的卷积神经网络（CNN）提供训练和验证；其次，开发一个功能齐全的移动应用程序，配备先进的用户界面。该界面专门用于获取图像，并利用训练有素的 CNN 提供的图像识别功能，为图像中描绘的大型真菌物种提供潜在的鉴定。这些技术进步使巴西真菌的获取民主化，从而提高公众的参与度和知识传播，同时也方便公众为巴西大型真菌物种保护的不断扩展的知识库做出贡献。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/45fd/11243970/c01fd177af69/peerj-12-17686-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/45fd/11243970/a8e81c43bc4c/peerj-12-17686-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/45fd/11243970/13f1b9fb688f/peerj-12-17686-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/45fd/11243970/4b3f97eb7066/peerj-12-17686-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/45fd/11243970/2c7250369015/peerj-12-17686-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/45fd/11243970/88f50632a884/peerj-12-17686-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/45fd/11243970/c82a34d0d8c2/peerj-12-17686-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/45fd/11243970/3d8801c22bb2/peerj-12-17686-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/45fd/11243970/e249c7ecf58c/peerj-12-17686-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/45fd/11243970/c01fd177af69/peerj-12-17686-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/45fd/11243970/a8e81c43bc4c/peerj-12-17686-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/45fd/11243970/13f1b9fb688f/peerj-12-17686-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/45fd/11243970/4b3f97eb7066/peerj-12-17686-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/45fd/11243970/2c7250369015/peerj-12-17686-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/45fd/11243970/88f50632a884/peerj-12-17686-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/45fd/11243970/c82a34d0d8c2/peerj-12-17686-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/45fd/11243970/3d8801c22bb2/peerj-12-17686-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/45fd/11243970/e249c7ecf58c/peerj-12-17686-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/45fd/11243970/c01fd177af69/peerj-12-17686-g009.jpg

相似文献

1
Innovative infrastructure to access Brazilian fungal diversity using deep learning.利用深度学习获取巴西真菌多样性的创新基础设施。
PeerJ. 2024 Jul 9;12:e17686. doi: 10.7717/peerj.17686. eCollection 2024.
2
MABAL: a Novel Deep-Learning Architecture for Machine-Assisted Bone Age Labeling.MABAL：一种用于机器辅助骨龄标注的新型深度学习架构。
J Digit Imaging. 2018 Aug;31(4):513-519. doi: 10.1007/s10278-018-0053-3.
3
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning.用于计算机辅助检测的深度卷积神经网络：卷积神经网络架构、数据集特征与迁移学习
IEEE Trans Med Imaging. 2016 May;35(5):1285-98. doi: 10.1109/TMI.2016.2528162. Epub 2016 Feb 11.
4
Deep convolutional neural networks for mammography: advances, challenges and applications.深度学习卷积神经网络在乳腺 X 线摄影中的应用：进展、挑战和应用。
BMC Bioinformatics. 2019 Jun 6;20(Suppl 11):281. doi: 10.1186/s12859-019-2823-4.
5
Insights and approaches using deep learning to classify wildlife.深度学习在野生动物分类中的应用研究进展与方法
Sci Rep. 2019 May 31;9(1):8137. doi: 10.1038/s41598-019-44565-w.
6
Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification.基于深度神经网络的植物病害叶片图像分类识别
Comput Intell Neurosci. 2016;2016:3289801. doi: 10.1155/2016/3289801. Epub 2016 Jun 22.
7
Automated Taxonomic Identification of Insects with Expert-Level Accuracy Using Effective Feature Transfer from Convolutional Networks.使用卷积网络的有效特征迁移，实现昆虫自动分类，达到专家级别的准确性。
Syst Biol. 2019 Nov 1;68(6):876-895. doi: 10.1093/sysbio/syz014.
8
Mini-review: Brazilian fungi diversity for biomass degradation.综述：巴西真菌多样性及其对生物质的降解作用。
Fungal Genet Biol. 2013 Nov;60:9-18. doi: 10.1016/j.fgb.2013.07.005. Epub 2013 Jul 19.
9
White blood cells detection and classification based on regional convolutional neural networks.基于区域卷积神经网络的白细胞检测与分类。
Med Hypotheses. 2020 Feb;135:109472. doi: 10.1016/j.mehy.2019.109472. Epub 2019 Nov 4.
10
Facial Expressions Recognition for Human-Robot Interaction Using Deep Convolutional Neural Networks with Rectified Adam Optimizer.基于修正 Adam 优化器的深度卷积神经网络的人机交互中的面部表情识别。
Sensors (Basel). 2020 Apr 23;20(8):2393. doi: 10.3390/s20082393.

本文引用的文献

1
An artificial intelligence approach to classify pathogenic fungal genera of fungal keratitis using corneal confocal microscopy images.基于角膜共聚焦显微镜图像的人工智能方法对真菌性角膜炎病原菌属的分类。
Int Ophthalmol. 2023 Jul;43(7):2203-2214. doi: 10.1007/s10792-022-02616-8. Epub 2023 Jan 3.
2
Species determination using AI machine-learning algorithms: Hebeloma as a case study.使用人工智能机器学习算法进行物种鉴定：以Hebeloma为例
IMA Fungus. 2022 Jun 30;13(1):13. doi: 10.1186/s43008-022-00099-x.
3
Automatic Fungi Recognition: Deep Learning Meets Mycology.
自动真菌识别：深度学习与真菌学相遇。
Sensors (Basel). 2022 Jan 14;22(2):633. doi: 10.3390/s22020633.
4
Citizen science project reveals novel fusarioid fungi () from urban soils.公民科学项目揭示了城市土壤中的新型镰孢菌类真菌。
Fungal Syst Evol. 2021 Dec;8:101-127. doi: 10.3114/fuse.2021.08.09. Epub 2021 Oct 12.
5
Deep learning-based quantification of arbuscular mycorrhizal fungi in plant roots.基于深度学习的植物根系丛枝菌根真菌定量分析。
New Phytol. 2021 Dec;232(5):2207-2219. doi: 10.1111/nph.17697. Epub 2021 Sep 16.
6
Deep learning for species identification of bolete mushrooms with two-dimensional correlation spectral (2DCOS) images.基于二维相关光谱（2DCOS）图像的深度学习用于牛肝菌物种鉴定
Spectrochim Acta A Mol Biomol Spectrosc. 2021 Mar 15;249:119211. doi: 10.1016/j.saa.2020.119211. Epub 2020 Nov 14.
7
The design and application of an automated microscope developed based on deep learning for fungal detection in dermatology.基于深度学习的皮肤科真菌检测用自动化显微镜的设计与应用。
Mycoses. 2021 Mar;64(3):245-251. doi: 10.1111/myc.13209. Epub 2020 Dec 7.
8
Deep learning approach to describe and classify fungi microscopic images.深度学习方法描述和分类真菌微观图像。
PLoS One. 2020 Jun 30;15(6):e0234806. doi: 10.1371/journal.pone.0234806. eCollection 2020.
9
101 genomes: A test case for predicting lifestyles and emergence of pathogens.101个基因组：预测病原体的生活方式和出现情况的一个案例
Stud Mycol. 2020 Feb 1;96:141-153. doi: 10.1016/j.simyco.2020.01.003. eCollection 2020 Jun.
10
Citizen science project reveals high diversity in Didymellaceae (Pleosporales, Dothideomycetes).公民科学项目揭示了座囊菌科（座囊菌目，座囊菌纲）的高度多样性。
MycoKeys. 2020 Mar 10;65:49-99. doi: 10.3897/mycokeys.65.47704. eCollection 2020.