Use of Machine Learning to Predict Transition Success in Dairy Cows in an Automatic Milking System.

过渡期(产犊前3周至产后3周)是奶牛的关键时期，被认为是导致奶牛健康问题的主要因素。对牛的精确管理，特别是对高危个人和群体的预测和/或识别，允许采取纠正做法，将疾病的风险或影响降至最低。机器人自动挤奶提供了利用传感器数据预测过渡成功并帮助预防相关疾病的机会，从而通过改进过渡管理提供了产生巨大影响的潜力。该合作项目与世界领先的机器人挤奶技术公司(利利国际)合作，旨在开发使用先进的数据分析技术和多数据流预测过渡成功的算法。开发的工具将被集成到Lely系统中，有可能对全球乳制品行业产生重大影响。成功的申请者将获得使用机器学习算法的知识，如神经网络、K近邻、支持向量机和决策树。此外，该行业合作伙伴将为学生提供参与将研究成果转化为行业相关工作的机会，并通过接触Lely网络提供独特的培训体验。研究将在诺丁汉的乳品科学创新中心(CDSI)进行，利用最近对这一高水平研究基础设施的投资。成功的学生还将与工业合作伙伴莱利国际公司共度一段时间。更多信息和申请：申请者必须拥有动物科学、兽医科学、应用统计学、兽医流行病学或类似学科的第一个或2.1个本科学位(或至少2.2个硕士学位)，并对定量分析和流行病学具有浓厚的兴趣。这个与行业相关的博士项目以诺丁汉大学兽医与科学学院为基础，与Lely International合作，旨在利用先进的数据分析技术和多数据流，探索和开发预测奶牛转型成功的算法。预测和/或识别高危个人和群体，允许采取纠正做法，最大限度地减少与过渡有关的疾病的风险或影响，为过渡奶牛管理提供巨大收益的潜力。机器人自动挤奶提供了利用传感器数据预测过渡成功的机会，从该项目开发的算法将集成到一家世界领先的机器人挤奶技术公司的系统中。此外，行业合作伙伴(Lely)将通过他们的行业网络为学生提供独特的培训机会，并为学生提供参与与向行业转化研究成果相关的工作的机会。

The transition period (3 weeks pre- to 3 weeks post-calving) is a critical time for dairy cattle and is recognised as a major contributor to health problems in dairy cows. Precision management of cattle, specifically prediction and/or identification of high risk individuals and groups, allows for corrective practices to minimize risk or impact of disease. Automated milking by robots provides an opportunity to utilise sensor data to predict transition success and aid in prevention of associated diseases and therefore offering the potential for large impact through improved transition management. This collaborative project, with a world-leading robotic milking technology company (Lely International), aims to develop algorithms to predict transition success using advanced data analytical techniques and multiple data streams. Tools developed will be integrated into Lely systems with the potential to provide significant impact on the dairy industry worldwide. The successful applicant will gain knowledge in the use of machine learning algorithms such as Neural Networks, K-nearest Neighbour, Support Vector Machines and Decision Trees. In addition, the industrial partner will provide the student with the opportunity to participate in work related to translation of research outputs to the industry and offer a unique training experience through exposure to Lely networks.The research will be conducted at the 'Centre for Dairy Science Innovation' (CDSI) at Nottingham, utilising recent investments in this high-level research infrastructure. The successful student will also spend a period of time with the industrial partner, Lely International. Further information and Application: Applicants should have a first or 2.1 undergraduate degree (or a minimum of a 2.2 degree in addition to a Masters degree) in Animal Science, Veterinary Science, Applied Statistics, Veterinary Epidemiology or similar subjects, and should have a strong interest in quantitative analysis and epidemiology.This industry linked PhD project, based at the School of Veterinary Medicine and Science, University of Nottingham and in collaboration with Lely International, aims to explore and develop algorithms to predict transition success in dairy cattle, using advanced data analytical techniques and multiple data streams. Prediction and/or identification of high risk individuals and groups, allows for corrective practices to minimize risk or impact of disease associated with transition, offering the potential for huge gains in transition cow management. Automated milking by robots provides an opportunity to utilise sensor data to predict transition success and algorithms developed from this project will be integrated into the systems of a world-leading robotic milking technology company. In addition the industrial partner (Lely) will provide the student with a unique training opportunity via their industry networks and offer the student opportunities to participate in work related to translation of research outputs to the industry.