智能乳制品农业:用于牛奶产量分类任务的移动应用程序
Allan Hall-Solorio1, Graciela Ramirez-Alonso1, Alfonso Juventino Chay-Canul2
1Computer Vision and Data Science Lab, Facultad de Ingeniería, Universidad Autónoma de Chihuahua, Circuito Universitario Campus II, Chihuahua 31125, Mexico.
Animals : an open access journal from MDPI
|July 29, 2025
概括
使用YOLOv11的深度学习模型通过检测乳头来分类奶牛的牛奶产量. 这有助于非专业的乳制品生产人员,尤其是在传统数据收集难以实现的地方.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 乳制品生产依赖于精确的牛奶产量评估进行管理.
- 传统的方法可能是劳动密集型,在某些现场条件下不切实际.
- 自动化系统为有效和可访问的数据收集提供了潜力.
研究的目的:
- 开发和评估一种轻量级,基于图像的深度学习模型,用于分类奶牛的牛奶产量.
- 为了自动检测部区域,用于产量类别预测.
- 创建一个实用工具,用于非专业用户进行现场级别的评估.
主要方法:
- 使用YOLOv11架构进行对象检测和分类.
- 在公开的牛图像数据集和305天的牛奶产量记录上训练模型.
- 建立了低,中,高产阶级的门,并进行了30次训练,以获得强度.
主要成果:
- 该模型的整体精度为0.408 ± 0.044,回忆率为0.739 ± 0.095,mAP@50为0.492 ± 0.031.
- 低收益阶层显示了最高的绩效指标.
- 错误分类主要是在类边界附近观察到的,这凸显了对一致图像采集的需求.
结论:
- 该研究表明,在乳制品生产中使用基于视觉的深度学习模型的实际可行性.
- 开发的模型和移动应用程序可以帮助决策,特别是在资源有限的环境中.
- 自动检测为非专业的牛奶产量评估提供了一种可行的方法.
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