自动化系统用于预测产犊时间和使用轨迹数据和运动特征进行牛群分类
Wai Hnin Eaindrar Mg1, Thi Thi Zin2, Pyke Tin3
1Interdisciplinary Graduate School of Agriculture and Engineering, University of Miyazaki, Miyazaki, 889-2192, Japan.
Scientific reports
|January 18, 2025
概括
通过新的自动化系统,现在可以准确地预测牛犊. 这项技术将分娩分类为正常或异常,并预测时间,改善畜牧管理和动物福利.
科学领域:
- 农业科学 农业科学
- 动物科学动物科学
- 计算机视觉 计算机视觉
背景情况:
- 准确的分娩时间预测对于畜牧管理和动物福利至关重要.
- 目前的方法可能缺乏精度和自动化.
- 行为分析为改善预测提供了潜力.
研究的目的:
- 开发一种用于牛犊分类和时间预测的自动化系统.
- 为了利用12小时的轨迹数据进行个别牛的行为分析.
- 通过精确预测生牛事件来加强畜牧业管理.
主要方法:
- 用一个定制的YOLOv8模型来有效地检测牛和过噪音.
- 与全球ID优化的定制追踪算法 (CTA) 确保了连续,准确的牛追踪.
- 为了分类和预测,提取了三个总运动特征和三个累积运动特征.
主要成果:
- 自动化系统在12小时内检测和跟踪20头牛的总准确率达到99%.
- 根据不同特征,正常/异常的分类准确度达到100%,95%和85%.
- 分别在6小时,9小时和8小时内实现了产卵时间预测精度.
结论:
- 开发的系统提供了自动化,准确的分娩分类和时间预测.
- 这项技术支持及时的农民干预,增强奶牛和小牛的健康.
- 该系统优化了资源分配和农场效率,为可持续的畜牧业做出了贡献.
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