基于人工智能的预测和检测乳牛的数字皮肤炎的早期发病,使用红外热谱
Marcelo Feighelstein1, Amir Mishael2, Tamir Malka2
1University of Haifa, Haifa, Israel.
Scientific reports
|December 1, 2024
概括
使用人工智能和红外热学 (IRT) 可以在牛中早期检测数字性皮肤炎 (DD). 这项技术可以在临床症状出现前两天预测DD,改善牛的福利和乳制品的利能力.
科学领域:
- 兽医医学 兽医医学 兽医医学
- 动物科学动物科学
- 计算机科学 计算机科学
背景情况:
- 数字性皮肤炎 (DD) 是一种普遍存在的牛脚疾病,影响动物福利和奶牛场经济.
- 早期检测和预测DD对于缓解,减少奶产和生育问题至关重要.
研究的目的:
- 使用红外热学 (IRT) 应用深度学习和计算机视觉来早期检测和预测奶牛的数字皮肤炎.
- 评估热图像,提取的颜色特征和温度数据作为DD检测模型的输入的有效性.
主要方法:
- 利用基于深度学习的计算机视觉算法,对来自奶牛的红外热图 (IRT) 数据进行了分析.
- 研究了各种数据输入:原始热图像,IRT的统计色彩特征和记录的温度值.
- 开发了用于"日0"DD检测和预测DD发病前两天的模型.
主要成果:
- 在临床症状出现的第一天 ("0日") 检测数字性皮肤炎的准确性超过81%.
- 在临床症状出现前两天预测数字皮肤炎的准确率超过70%.
- 展示了IRT与AI结合的潜力,用于实时自动化DD监控.
结论:
- 红外热学 (IRT) 与人工智能 (AI) 结合,为乳牛早期数字皮肤炎 (DD) 检测提供了一个有前途的方法.
- 基于人工智能的IRT分析可以促进及时干预,提高动物福利和农场生产力.
- 这项技术支持开发牛脚健康的自动化实时监测系统.
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