Cockburn Marianne
Agroscope, Competitiveness and System Evaluation, 8356 Ettenhausen, Switzerland.
Animals (Basel). 2020 Sep 18;10(9):1690. doi: 10.3390/ani10091690.
Dairy farmers use herd management systems, behavioral sensors, feeding lists, breeding schedules, and health records to document herd characteristics. Consequently, large amounts of dairy data are becoming available. However, a lack of data integration makes it difficult for farmers to analyze the data on their dairy farm, which indicates that these data are currently not being used to their full potential. Hence, multiple issues in dairy farming such as low longevity, poor performance, and health issues remain. We aimed to evaluate whether machine learning (ML) methods can solve some of these existing issues in dairy farming. This review summarizes peer-reviewed ML papers published in the dairy sector between 2015 and 2020. Ultimately, 97 papers from the subdomains of management, physiology, reproduction, behavior analysis, and feeding were considered in this review. The results confirm that ML algorithms have become common tools in most areas of dairy research, particularly to predict data. Despite the quantity of research available, most tested algorithms have not performed sufficiently for a reliable implementation in practice. This may be due to poor training data. The availability of data resources from multiple farms covering longer periods would be useful to improve prediction accuracies. In conclusion, ML is a promising tool in dairy research, which could be used to develop and improve decision support for farmers. As the cow is a multifactorial system, ML algorithms could analyze integrated data sources that describe and ultimately allow managing cows according to all relevant influencing factors. However, both the integration of multiple data sources and the obtainability of public data currently remain challenging.
奶农使用畜群管理系统、行为传感器、喂养清单、繁殖计划和健康记录来记录畜群特征。因此，大量的奶牛数据变得可用。然而，缺乏数据整合使得奶农难以分析他们奶牛场的数据，这表明这些数据目前尚未得到充分利用。因此，奶牛养殖中仍然存在诸如奶牛寿命短、生产性能差和健康问题等多重问题。我们旨在评估机器学习（ML）方法是否能够解决奶牛养殖中一些现有的问题。本综述总结了2015年至2020年间发表在奶牛领域的经过同行评审的机器学习论文。最终，本综述考虑了来自管理、生理学、繁殖、行为分析和喂养等子领域的97篇论文。结果证实，机器学习算法已成为奶牛研究大多数领域的常用工具，特别是用于预测数据。尽管有大量的研究，但大多数经过测试的算法在实际应用中表现并不足以实现可靠的实施。这可能是由于训练数据不佳。来自多个农场的更长时间段的数据资源可用性将有助于提高预测准确性。总之，机器学习是奶牛研究中一个有前途的工具，可用于为奶农开发和改进决策支持。由于奶牛是一个多因素系统，机器学习算法可以分析综合数据源，这些数据源描述并最终允许根据所有相关影响因素来管理奶牛。然而，目前多个数据源的整合以及公共数据的可获取性仍然具有挑战性。
