Use of precision technologies and advanced analytical techniques for early detection and measurement of lameness in dairy herds

早期干预对于改善治疗结果和减少奶牛跛行复发至关重要；然而，这取决于对跛行奶牛的可靠检测。传感器和智能计算技术的进步及其在农场上的使用为实现这一目标提供了可能性，因此有可能通过减少跛行为该行业带来巨大收益。这项研究旨在利用尖端传感器技术，探索和开发新的数据驱动解决方案，用于准确自动识别奶牛的跛行。开发基于技术的客观方法来测量跛足的方法将包括：1)使用现有的商业可用的传感器技术来对跛行进行分类；2)研究新型传感器对跛行进行分类的可行性3)优化多个传感器的使用和从传感器数据中学习算法的性能研究将结合先进的数据分析，包括机器学习，与开发新的测量方法的实际方面相结合。此外，工业合作伙伴(农业和园艺发展局；AHDB)将为学生提供参与与向该行业转化研究成果相关的工作的机会。成功的申请者将获得特征工程方面的知识，并使用各种机器学习算法，如神经网络、K近邻、支持向量机和决策树。这项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AHDB共度一段时间。

Early intervention is critical to improving treatment outcomes and reducing recurrence of lameness in dairy cows; however, this is dependent on the reliable detection of lame cows. Advances in sensor and smart computing technologies and their use on-farm provide possibilities to achieve this and therefore potential to produce huge gains for the industry through lameness reduction. This research aims to explore and develop novel data driven solutions for accurate automated identification of lameness in dairy cattle, using cutting-edge sensor technologies. The approaches to develop technology-based objective methods to measure lameness will include; 1) Using existing commercially available senor technologies to classify lameness 2) Investigate the feasibility of novel sensors to classify lameness 3) Optimise the use of multiple sensors and performance of learning algorithms from sensor data The research will combine advanced data analytics, including machine learning, with the practical aspects of developing a novel methodology of measurement. In addition, the industrial partner (Agriculture and Horticulture Development Board; AHDB) will provide the student with the opportunity to participate in work related to translation of research outputs to the industry. The successful applicant will gain knowledge in the feature engineering and use various machine learning algorithms, such as Neural Networks, K-nearest Neighbor, Support Vector Machines and Decision Trees. 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, AHDB.