人工智能和循环值用于乳牛中压力检测
Samuel Lascano Rivera1,2, Luis Rivera2,3, Hernán Benavides1
1Research Department, State Polytechnic University of Carchi, Tulcán 040101, Ecuador.
Sensors (Basel, Switzerland)
|November 13, 2025
概括
这项研究使用深度学习和昼夜节律分析来检测奶牛的压力. 该系统可以在一小时前准确预测压力水平,从而能够及时进行干预,以改善动物福利.
科学领域:
- 动物科学动物科学
- 计算生物学 计算生物学
- 机器学习 机器学习
背景情况:
- 压力会对乳牛的福利和生产力产生负面影响.
- 传统的压力检测方法往往是主观的,缺乏持续监测.
- 了解昼夜节律对于评估动物福祉至关重要.
研究的目的:
- 开发一种用于早期检测乳牛压力的新方法.
- 将昼夜节律分析与深度学习相结合,以提高压力分类.
- 为积极的动物福利管理提供预测工具.
主要方法:
- 使用传感器对行为生物标志物 (食,休息,反) 的持续监测.
- 通过快速里埃转换 (FFT) 提取昼夜节律特征.
- 开发和训练长期短期记忆 (LSTM) 神经网络,用于压力分类.
主要成果:
- 在压力检测中,LSTM模型实现了82.3%的准确性和0.847 AUC.
- 该模型以一小时的领先时间展示了预测能力.
- 性能明显超过了基准逻辑回归模型.
结论:
- 综合方法有效地检测和预测乳牛的压力.
- 这项技术为精密畜牧业和动物福利提供了一个实用的工具.
- 早期发现压力可以及时进行预防性干预,从而改善动物健康.
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