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机翼干扰模式 (WIPs) 和机器学习用于对一些医学兴趣的Aedes物种进行分类

Arnaud Cannet¹, Camille Simon-Chane², Aymeric Histace²

¹Direction des affaires sanitaires et sociales de la Nouvelle-Calédonie, Nouméa, France.

Scientific reports

|October 17, 2023

概括

一个自动化系统使用翅膀图案来识别艾迪斯蚊子物种,这些蚊子是疾病的关键载体. 这种由人工智能驱动的工具为大规模昆虫学调查和疾病控制工作提供了具有成本效益的解决方案.

科学领域:

医学昆虫学医学昆虫学
载体传播疾病监测监测载体传播疾病监测
在生物学中的人工智能.

背景情况:

吸血性艾迪斯蚊子是病毒性和细菌性疾病的重要载体.
全球气候变化和新出现的动物传染病需要对Aedes物种进行先进的现场识别工具.
目前的昆虫学调查受限于需要熟练的技术人员和昂贵的设备.

研究的目的:

开发一种自动化分类系统,用于使用翅膀干扰模式识别Aedes物种.
利用深度学习和卷积神经网络进行准确的分类学分类.
为增强大规模昆虫学调查提供实用工具.

主要方法:

创建了一个数据库,包含24个Aedes物种的494个光微图.
在选定的物种上使用深度学习方法训练了一个卷积神经网络.
该系统在属,亚属和物种分类学层面进行了准确性测试.

主要成果:

在属级别上分类Aedes的准确性达到了95%.
在三分之二的子属 (Ochlerotatus和Stegomyia) 中获得了超过85%的准确性.
成功地分类了10种Aedes物种中的8种,准确度超过70%.

结论:

开发的方法显示了自动化Aedes物种识别的巨大潜力.
这种人工智能驱动的方法可以支持未来的大规模昆虫学调查.
该系统为载体控制和疾病监测工作提供了一个有希望的工具.

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