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基于语义细分和反向传播神经网络的牛的智能体重预测
Beibei Xu1,2, Yifan Mao3, Wensheng Wang4
1Agricultural Economics and Information Institute, Jiangxi Academy of Agriculture Sciences, Nanchang, China.
Frontiers in artificial intelligence
|February 13, 2024
概括
现在可以使用人工智能准确预测牛的体重. 这种非侵入性方法采用图像分析和反向传播 (BP) 神经网络,改善畜牧管理和动物福利.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 传统的牛体重估计是劳动密集型和侵入性的.
- 在畜牧管理中需要有效的,非侵入性的方法.
- 精准农业需要先进的监测技术.
研究的目的:
- 开发一种智能,非侵入性系统来预测牛的体重.
- 提高畜牧管理的效率和可持续性.
- 通过先进技术促进动物福利.
主要方法:
- 利用语义细分与混合ResNet-101-D和Squeeze-Excitation (SE) 注意力机制从奶牛图像中提取特征.
- 员工体型参数和物理测量用于训练回归模型.
- 实施并比较各种机器学习模型用于重量估计.
主要成果:
- 背向传播 (BP) 神经网络在体重预测方面表现出卓越的表现.
- 获得了13.11磅的平均绝对误差 (MAE) 和22.73磅的根平均平方误差 (RMSE).
- 该系统成功地在没有身体接触的情况下估计了牛的体重.
结论:
- 拟议的人工智能驱动的方法为牛体重预测提供了准确而非侵入性的解决方案.
- 这项技术通过消除物理处理,显著改善了动物福利.
- 该研究推进了精准农业和福利农业实践.
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