用三轴加速度计数据对年龄小马的放牧行为进行分类的深度学习方法:试点研究
Uta Kamiya1, Kasumi Kakiuchi1, Kensuke Kawamura2
1School of Agriculture and Animal Science, Obihiro University of Agriculture and Veterinary Medicine, Obihiro, Hokkaido 080-8555, Japan.
Journal of equine veterinary science
|October 3, 2025
概括
一个深度学习模型准确地使用上安装的加速度计来分类马的放牧行为. 这项技术为牧场管理和马群福利提供了精确的,自动化的监控.
科学领域:
- 马类科学 马类科学
- 动物行为 动物行为
- 机器学习 机器学习
背景情况:
- 精确监测马的放牧行为对于牧场管理和福利至关重要.
- 传统的观测方法是劳动密集型的,缺乏详细的时间数据.
研究的目的:
- 开发和验证一个深度学习模型来分类马的放牧和非放牧行为.
- 利用上安装的加速度计数据进行自动行为分析.
主要方法:
- 四匹年龄小的马被装配了安装在下巴上的三轴加速度计.
- 在19个小时内收集了数据,通过视频注释了230,286个点.
- 在不同的数据参数上训练和评估深度学习模型 (CNN,LSTM,CNN+LSTM).
主要成果:
- 综合CNN+LSTM模型实现了98.0%的测试准确率和1.00的AUC.
- 高F1分数 (0.99为牧场,0.97为非牧场) 表示强大的分类.
- 放牧行为集中在牧场外围,而非放牧行为更集中在中心.
结论:
- 集成CNN和LSTM的深度学习框架使用加速度计准确地分类马的放牧行为.
- 这种非侵入性,高分辨率的方法使牧场系统中的自动行为监测成为可能.
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