多传感器集成和机器学习用于草食动物食行为的高分辨率分类.
Bashiri Iddy Muzzo1, Kelvyn Bladen2, Andres Perea3
1Department of Wildland Resources, Quinney College of Natural Resources, Utah State University, Logan, UT 4322-5230, USA.
Animals : an open access journal from MDPI
|April 12, 2025
概括
机器学习模型准确地分类牛的行为,如放牧和休息. 与Random Forest的交叉验证对于复杂的牛活动模式是最可靠的,突出了对连续移动数据的需求.
科学领域:
- 动物行为科学 动物行为科学
- 机器学习在农业中的应用.
- 畜牧业监控技术 畜牧业监控技术
背景情况:
- 准确分类牛的行为对于福利和生产力至关重要.
- 机器学习为复杂的动物运动数据的自动分析提供了潜力.
- 区分微妙的行为,如放牧,休息和反需要复杂的模型.
研究的目的:
- 评估和比较各种机器学习模型的性能,以分类不同的牛行为.
- 评估随机测试分割 (RTS) 和交叉验证 (CV) 数据分区方法的有效性.
- 确定影响不同行为状态的关键运动衍生预测因素.
主要方法:
- 应用了六种机器学习模型:感知器,物流回归,支持向量机,K-最近邻居,随机森林 (RF) 和XGBoost (XGB).
- 分类活动状态,食行为 (放牧,休息,行走,反),姿势状态和综合行为-姿势状态.
- 使用随机测试分割 (RTS) 和交叉验证 (CV) 进行模型评估.
主要成果:
- 在整体状态和食行为分类方面,XGBoost显示了最高的准确性 (74.5% RTS,69.4% CV).
- 随机森林 (RF) 在分类特定行为如放牧,休息,反和姿势状态方面表现优于XGBoost (例如,姿势的CV为83.9%).
- 像速度和Actindex这样的运动数据特征是关键预测因素,特定的传感器值 (X,Z) 影响着与姿势相关的行为.
结论:
- 交叉验证 (CV) 是对复杂的牛行为数据的机器学习模型评估的可靠方法,特别是随机森林.
- 连续记录设备和详细的移动数据对于准确的牛行为监测至关重要.
- 机器学习,特别是射频,显示了对畜牧业活动的自动化和精确分析的巨大潜力.
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