蜜蜂在一起:在基于AI的监测中加入蜜蜂音频数据集以进行蜂巢推断
Augustin Bricout1,2, Philippe Leleux1, Pascal Acco1
1Laboratory for Analysis and Architecture of Systems (LAAS-CNRS), University of Toulouse, 31077 Toulouse, France.
Sensors (Basel, Switzerland)
|September 28, 2024
概括
本研究介绍了使用音频分类监测蜂巢健康状况的BeeTogether数据集. 新的对比式学习方法提高了分类准确性和在不同蜜蜂群体中的概括性.
科学领域:
- 蜂类学 蜂类学 蜂类学
- 生态生态学 生态生态学
- 农业 农业 农业 农业
- 生物声学是一种生物声学.
背景情况:
- 蜂巢健康监测对生物学,生态学和农业至关重要.
- 音频传感器为蜂巢监测提供了一种非侵入性的方法.
- 现有的用于蜜蜂分类的音频数据集往往缺乏跨不同蜂巢的概括性.
研究的目的:
- 为了解决蜂巢音频分类中的概括限制.
- 为蜜蜂健康研究创建一个标准化,开放的数据集.
- 开发新的分类方法,以改善蜂巢外推.
主要方法:
- 审查并将开放的音频数据集合并到Kaggle上的"BeeTogether"数据集中.
- 实现了数据增强和测量蜂巢外推的方法.
- 基准使用了经典分类器,并引入了基于学习的对比分类器.
- 在无监督数据上获得绝对标签的过程的原型.
主要成果:
- 经典分类器的准确性很好,但对未见的蜂巢的概括性很差,这与之前的研究一致.
- 分类性能受到殖民地特定音频特征的显著影响.
- 对比式学习分类器显示了更好的准确性和蜂群推断能力.
- "BeeTogether"数据集为评估概括提供了一个标准化的框架.
结论:
- 统一的数据集和先进的分类技术对于稳健的蜂巢监测至关重要.
- 对比式学习提供了一种有希望的方法来克服音频分类中的特定殖民地偏见.
- 这项工作有助于在农业中更有效地应用蜜蜂健康监测.
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