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通过图像处理和机器学习,通过热应力操作自动分析肉的高,中,低活动
Oluwadamilola Moyin Oso1, Nicolas Mejia-Abaunza1, Venkat Umesh Chandra Bodempudi2
1Department of Poultry Science, University of Georgia, GA 30602, USA.
Poultry science
|March 7, 2025
概括
可以使用从图像分析中获得的鸟类活动指数 (BAI) 来监测肉热应激. 营养补充剂和环境条件显著影响肉活动水平和热应激反应.
科学领域:
- 动物科学动物科学
- 农业工程 农业工程
- 计算生物学 计算生物学
背景情况:
- 热应激显著影响家禽的福利和生产力.
- 使用图像处理和机器学习的自动活动监控为量化肉行为提供了解决方案.
- 了解热应激下的行为变化对于改善家禽管理至关重要.
研究的目的:
- 为了评估中等热量暴露对肉行为活动的影响.
- 评估图像处理和机器学习在量化肉活动中的有效性.
- 研究营养治疗和环境因素对肉行为的影响.
主要方法:
- 在两个营养组中利用了132只Cobb 500肉 (对照组与补充25-氧维生素D3).
- 暴露在周期性热量 (29.56 ± 1.34 °C,76.97% ± 5.98%湿度) 和热中性条件下的鸟类.
- 分析鸟类活动指数 (BAI) 使用视频录制,差异化和k-means集群.
主要成果:
- 与对照组相比,补充组表现出明显更高的活动指数 (P < 0.01).
- 循环加热显著降低了高和中 BAI 值.
- BAI与羊群年龄有中等的相关性,并根据营养治疗和环境条件而有所变化.
结论:
- 鸟类活动指数 (BAI) 是预测肉热应激的一个有价值的工具.
- BAI的有效性可能会受到鸟类的年龄,饮食,温度和湿度的影响.
- 进一步的研究可以改进BAI,以便在家禽中更准确地预测热应激.
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