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整合机器学习技术来加强边缘化城市社区的动物监测

Fabio Neves Souza^1,2, Adedayo Michael Awoniyi¹, Rodrigo Dalvit Carvalho da Silva³

¹Instituto de Saúde Coletiva Universidade Federal da Bahia Salvador BA Brazil.

Ecology and evolution

|November 3, 2025

概括

机器学习准确地分析动物跟踪板,为传统的害虫监测方法提供了更快,更便宜的替代方案. 这种方法有助于疾病生态和动物管理,特别是在资源有限的地区.

科学领域:

生态生态学生态生态学
计算机科学计算机科学
公共卫生公共卫生

背景情况:

动物害虫防治需要有效的种群监测.
目前的捕捉和手动跟踪板分析等方法昂贵且耗时.
解释跟踪牌需要大量的专业知识和时间.

研究的目的:

开发和评估机器学习 (ML) 技术,用于分析动物跟踪板.
为了比较基于ML的分析与常规的人类解释的准确性.
提供一种更有效,更具成本效益的动物监测方法.

主要方法:

使用图像处理技术 (Otsu方法,全球值) 来准备跟踪板图像.
应用了缩小尺寸的方法 (主要组件分析 - PCA,独立组件分析 - ICA,传奇时刻 - LM).
使用K-最近邻居 (k-NN) 分类来预测PCA,ICA和LM结果的特征向量.

主要成果:

通过PCA和LM方法,与传统的手册解释进行了有利的比较.
ML方法为动物监测提供了一个及时且具有成本效益的替代方案.
识别了跟踪板上的关键模式,以进行有效的分析.

结论:

ML的整合显著提高了动物监测协议.
这种新的方法对低收入和中等收入国家尤其有利.
改善了用于管理动物分布热点和控制动物传播的动物病的监测辅助工具.

关键词:

机器学习是机器学习.害虫动物动物主要组件分析的主要组件分析动物管理动物管理控制动物病控制动物病.

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