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

使用机器学习根据中红外光谱数据预测奶牛的状态

• 批准号: BB/S009396/1
• 负责人: Michael Coffey
• 金额: $ 31.09万
• 依托单位: Scotland's Rural College
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FS009396%2F1
• 关键词: PredictinB; statg; Tus; dairy; cows

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.

牛结核病（bTB）是一种慢性、传染性和人畜共患（即，它可以传播给人类），是英国和其他国家的地方病，并且对英国养牛业提出了重大挑战，特别是在英格兰西南部和南威尔士。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在英格兰和威尔士的牛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bTB而屠宰的动物数量超过43，500只。事实证明，使用社会可接受的技术和英国纳税人可接受的成本很难完全根除这种疾病。目前的成本估计每年超过1.75亿英镑，每个农场每次爆发bTB的平均成本为34，000英镑。关于野生动物作为养殖牛疾病储存库的作用的持续两极分化的辩论正在使进展缓慢。该项目旨在开发一种非侵入性工具，该工具是从奶牛的常规牛奶记录中创建的，通过利用最先进的深度学习技术从牛奶分析（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，并向计算机系统提供大量数据，它可以使用这些数据来对其他数据做出决策。我们的研究小组已经成功地部署了这种分析方法，以高精度预测奶牛的妊娠状态，因此人们对bTB在牛奶中留下信号的期望很高，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，国家牛奶记录）的参与确保了结果可以迅速应用，以在短期内产生最大的影响。此外，NMR在支持奶农牛群管理（包括疾病）方面有着悠久的历史，因此该项目的结果将在奶农熟悉的环境中进行利用，以确保其广泛吸收。

项目成果

Automated Processing and Phenotype Extraction of Ovine Medical Images Using a Combined Generative Adversarial Network and Computer Vision Pipeline.

• DOI: 10.3390/s21217268
• 发表时间: 2021-10-31
• 期刊: Sensors (Basel, Switzerland)
• 作者: Robson JF;Denholm SJ;Coffey M
• 通讯作者: Coffey M