使用改进的YOLOV5和养功能区域建议,自动识别和量化幼猪的养行为
Yizhi Luo1,2, Jinjin Xia3, Huazhong Lu2
1Institute of Facility Agriculture, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China.
Animals : an open access journal from MDPI
|February 24, 2024
概括
使用改进的YOLOV5的新方法准确地识别了复杂环境中的小猪养行为. 这项技术有助于分析养模式,优化农场管理策略.
科学领域:
- 动物科学动物科学
- 计算机视觉 计算机视觉
- 农业技术 农业技术
背景情况:
- 精确监测牲畜养行为对于优化动物福利和农场管理至关重要.
- 目前用于识别猪养行为的现有方法可能受到环境因素的限制,例如照明和猪姿势.
研究的目的:
- 开发和验证一种基于计算机视觉的新方法,用于识别和量化幼生猪养行为.
- 为了提高猪头检测和在具有挑战性的环境中食区域定位的准确性.
主要方法:
- 开发了一种改进的YOLOV5模型,其中包含了一个变压器模块,用于准确检测小猪头.
- 设计了一种方法来定义不规则的养区域,使用低谷特征和图像比率.
- 通过计算在确定养区域内检测到的小猪头的比例来量化养行为.
主要成果:
- 与标准YOLOV5s模型相比,增强的YOLOV5模型显示平均精度 (mAP) 增加了5.8%,F1得分增加了4.7%.
- 持续24小时的监测显示,在群体养的幼猪中,有明显的昼间食节奏,在特定的早晨和下午期间活动的高峰.
- 该模型成功地确定了中午和夜间食活动的减少.
结论:
- 拟议的方法为猪养行为的自动识别和量化提供了一个强大的解决方案.
- 这项技术为改进农场养方案和改善猪生产管理提供了宝贵的数据.
- 这些发现凸显了了解昼间养模式对于有效的畜牧管理的重要性.
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