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使用先进的深度学习方法监测牛养殖活动
Muhammad Asim1, Bareera Anam1, Muhammad Nadeem Ali1
1Department of Software & Communications Engineering, Hongik University, Sejong City 30016, Republic of Korea.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
本研究介绍了一种基于视觉的系统来监测牛的行为,克服了传统传感器的局限性. 计算机视觉方法准确地识别了细粒度的活动,例如乳牛的雌激素检测.
科学领域:
- 农业技术 农业技术
- 计算机视觉 计算机视觉
- 动物科学动物科学
背景情况:
- 基于传感器的牛监测面临诸多挑战,包括高成本,动物不适和不准确的测量.
- 现有的方法往往缺乏精确的健康和管理所需的细粒度.
研究的目的:
- 引入和评估基于视觉的牛活动监测系统.
- 用深度学习分析细粒度牛的行为,包括用深度学习检测雌激素.
- 在这种情况下,比较YOLOv8和YOLOv9算法的性能.
主要方法:
- 在一个商业奶牛场部署了头顶摄像头,以捕捉不受约束的群体行为.
- 创建一个自定义数据集,其中包含2956张图像,并对四种行为进行注释:站立,躺卧,放牧和.
- 深度学习算法的应用 (YOLOv8和YOLOv9) 用于行为分类.
主要成果:
- YOLOv8-L实现了91.11%的平均平均精度 (mAP).
- YOLOv9-E实现了一个90.23%的mAP.
- 基于视觉的系统在分析不同农场条件下的细粒度牛活动方面表现出有效性.
结论:
- 基于视觉的监测为畜牧业中基于传感器的系统提供了一个可行的替代方案.
- 深度学习模型,特别是YOLOv8,在对牛的详细行为进行分类方面表现出高准确性,以改善牛群管理和雌激发检测.
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