创新的基础设施可以使用深度学习访问巴西的真菌多样性
Thiago Chaves1, Joicymara Santos Xavier2, Alfeu Gonçalves Dos Santos1
1Brazilian National Institute for Digital Convergence-INCoD, Universidade Federal de Santa Catarina, Florianópolis, Santa Catarina, Brazil.
PeerJ
|July 15, 2024
概括
这项研究引入了巴西宏观真菌的新数据库,用于训练卷积神经网络 (CNN) 进行自动物种识别,从而实现公共使用和真菌保护工作的移动应用程序.
科学领域:
- 菌类学 菌类学是指菌类学.
- 计算机科学 计算机科学
- 生物多样性信息学 生物多样性信息学
背景情况:
- 精确识别大真菌物种对于生态研究和保护至关重要.
- 现有的宏真菌鉴定方法可能耗时,需要专家知识.
- 需要可访问的工具来帮助识别和记录巴西真菌.
研究的目的:
- 开发一个全面的,专家策划的巴西宏观真菌数据库,包括大量的摄影数据.
- 训练和验证卷积神经网络 (CNN) 用于自动化宏真菌物种识别.
- 为基于图像的宏真菌识别创建一个用户友好的移动应用程序,促进公众参与和数据收集.
主要方法:
- 编制了一个精心结构化的巴西宏观真菌数据库,包括505个物种的13894张照片.
- 使用自主物种识别策划图像数据库对卷积神经网络 (CNN) 的培训和验证.
- 开发一款具有先进用户界面的移动应用程序,用于图像采集和人工智能驱动的识别建议.
主要成果:
- 成功创建了一个大型的,经过专家验证的巴西宏观真菌数据库.
- 经过训练的CNN有能力自主识别图像中的宏菌种.
- 开发一个功能性移动应用程序原型,供公众使用.
结论:
- 开发的数据库和CNN模型为巴西的大型真菌识别提供了强大的工具.
- 移动应用程序使巴西真菌知识的获取民主化,促进公众参与和公民科学.
- 这项技术支持加强生物多样性监测和保护巴西宏观真菌的努力.
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