从使用机器学习技术的3D图像来预测泽西乳牛的身体状况
Rasmus B Stephansen1, Coralia I V Manzanilla-Pech1, Grum Gebreyesus1
1Center for Quantitative Genetics and Genomics, Aarhus University, 8000-Aarhus C, Denmark.
Journal of animal science
|November 9, 2023
概括
自动化的3D成像准确地预测奶牛的身体状况,改善群体管理和遗传评估. 这种具有成本效益的方法通过精确的身体状况评分来提高生育能力和牛福利.
科学领域:
- 动物科学动物科学
- 农业工程 农业工程
- 机器学习 机器学习
背景情况:
- 奶牛的身体状况是健康,福利和生产力的关键指标.
- 手动的身体状况评分是劳动密集型的,需要熟练的技术人员.
- 准确的,常规的身体状况评估对于乳牛群管理和遗传改进至关重要.
研究的目的:
- 开发和验证可靠的自动化模型,用3D成像来预测泽西牛的身体状况.
- 为了比较不同的机器学习算法对身体状况预测的性能.
- 评估使用3D图像分析进行经济有效和频繁的身体状况监测的可行性.
主要方法:
- 通过使用微软Xbox One Kinect v2摄像头,收集了 808 头泽西牛的 2,253 张 3D 背景图像.
- 从3D图像中提取了轮和背部高度特征,与类预测器 (评估器,群体,平价等) 相结合. ) 的情况.
- 采用H2O AutoML来评估深度学习 (DL) 和梯度提升机 (GBM) 进行分类/回归,以及部分最小平方 (PLS) 进行回归.
主要成果:
- DL模型在准确的身体状况评分中获得了48.1%的准确性,在0.5个单位的偏差范围内获得了93.5%的准确性.
- 在分类准确度方面,DL超过GBM.
- DL和PLS回归方法显示了可比性能,确定系数分别为0.66和0.67.
- 基于群体的验证导致准确度略有下降,但仍显示出高性能 (>38%准确,>87%与0.5单位偏差).
结论:
- 3D成像提供了一种可行和可靠的方法,用于在泽西牛的自动化身体状况预测.
- 这种自动化方法为手动评分提供了经济有效的替代方案,使得更频繁的监控成为可能.
- 开发的模型可以显著提高奶牛场管理,生殖效率和遗传评估.
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