PredictinB statg Tus of dairy cows from mid infra-red spectral data using machine learning

牛结核病（bTB）是英国和其他国家流行的一种慢性、传染性和人畜共患（即，它可以传播给人类）疾病，对英国养牛业，特别是英格兰西南部和南威尔士的养牛业构成了重大挑战。英国环境、食品和农村事务部（DEFRA）将bTB列为全球四种最重要的牲畜疾病之一。40多年来，bTB在英格兰和威尔士牛群中的持续传播一直是一场社会经济灾难，对大大小小的农业企业造成了灾难性和毁灭性的破坏。2017年，英国因结核病被屠宰的动物数量超过43500只。事实证明，这种疾病很难完全根除，使用的技术为社会所接受，成本也为英国纳税人所接受。目前每年的费用估计超过1.75亿英镑，每个农场每次爆发结核病的平均费用为3.4万英镑。关于野生动物作为家畜疾病储存库的作用的持续的两极分化辩论使进展缓慢。该项目旨在通过利用最先进的深度学习技术，从奶牛的常规牛奶记录中开发一种非侵入性工具，以通过牛奶分析（通过分光光度法）预测bTB状态。深度学习是基于学习数据表示的更广泛的机器学习方法家族的一部分，而不是特定于任务的算法（算法是用于解决计算的过程）。深度学习的工作原理是模仿人类大脑的工作方式，包括向计算机系统提供大量数据，计算机系统可以利用这些数据对其他数据做出决策。该分析方法已被我们的团队成功应用于预测奶牛的妊娠状态，准确度很高，因此人们对bTB在牛奶中留下信号的期望很高，该信号可以通过应用于MIR光谱数据的深度学习来检测。在bTB领域广泛活跃的商业合作伙伴（NMR, National Milk Records）的参与确保了结果可以迅速应用，在短期内产生最大的影响。此外，NMR在奶牛群管理（包括疾病管理）方面有着悠久的历史，因此这个项目的结果将在奶牛养殖户熟悉的环境中得到利用，确保其广泛采用。

Bovine tuberculosis (bTB) is a chronic, infectious and zoonotic (i.e., it can be transmitted to humans) disease endemic in the UK and other countries, and presents a significant challenge to the UK cattle sector particularly in the south west of England and south Wales. The Department for Environment, Food and Rural Affairs (DEFRA) lists bTB as one of the four most important livestock diseases globally. The continued spread of bTB among cattle in England and Wales has been a socioeconomic disaster for over 40 years, causing catastrophic and devastating damage to farming businesses both large and small. In 2017 the number of animals in the UK slaughtered due to bTB was in excess of 43,500. The disease has proven difficult to completely eradicate using techniques that are socially acceptable and at a cost acceptable to the UK taxpayer. Current costs are estimated at over £175 million per year with an average cost of £34,000 per bTB outbreak per farm. The continued polarised debate on the role of wildlife as a farmed cattle disease reservoir is making progress slow. This project seeks to develop a non-invasive tool created from routine milk recording of dairy cattle to predict bTB status from milk analysis (by spectrophotometry) by exploiting state of the art Deep Learning techniques. Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms (an algorithm is a process followed to solve calculations). Deep learning works by imitating the way that the human brain works and involves feeding a computer system a large volume of data, which it can use to make decisions about other data. This method of analysis has been successfully deployed by our group to predict pregnancy status in dairy cows with high accuracy and hence expectations are high that bTB leaves a signal in milk that can be detected with Deep Learning applied to MIR spectral data. The involvement of a commercial partner (NMR, National Milk Records) that is extensively active in the bTB area ensures that results can be rapidly applied to maximise impact in the short term. Furthermore, NMR has a long history of supporting dairy farmers in herd management (including disease) and so the results of this project will be exploited in a familiar context for dairy farmers ensuring its widespread uptake.