AI to monitor changes in social behaviour for the early detection of disease in dairy cattle

在英国，牛奶是经济的关键组成部分，也是重要的营养来源。有几种疾病经常在英国奶牛身上发展，这些疾病危及健康和福利，并导致农民和行业的经济损失。还发现，生病的奶牛对甲烷排放的贡献不成比例，从而影响了该部门的环境可持续性。此外，要满足社会对粮食生产的需求，高福利比以往任何时候都更加重要。为了帮助农民检测和治疗这些疾病，农民现在可以使用多种自动监测奶牛的解决方案。所有这些技术的一个严重缺点是，它们专注于检测晚期疾病的可观察到的症状，此时治疗选择可能有限，产奶量持续减少，动物福利受到更严重的影响。奶牛对感染和创伤的反应是将对生存和恢复不是立即必要的行为-如社会互动-优先考虑那些在更长时间内仍然至关重要的行为，在最近的一项研究中，我们发现患有早期乳房炎的个体的社会探索、对他人的梳理和接受头部撞击的能力较低。因此，我们假设社会行为变化可能是疾病的早期预测因素。对于忙碌的农民来说，检测社会行为变化是困难的，但通过使用摄像机和人工智能(AI)在关键关注点进行监测是可能的，例如在饲料铺位排队挤奶或喂奶时。我们已经开发出高度健壮的人工智能，可以在视频中跟踪奶牛的运动，并通过它们独特的皮毛图案识别每一个个体。其他人现在已经展示了从视频中很好地分类从属和竞争的社交互动，因此我们现在建议结合这两个想法来跟踪牛群中每一头被识别的奶牛随着时间的推移活动和社交行为的变化。通过收集摄像机拍摄我们约翰·奥达克中心奶牛场主牛棚的两年视频，我们将训练一个模型，了解随着时间的推移，哪些类型的行为会发生变化，这些行为表明不同的早期疾病。我们将重点关注乳房炎和跛行，因为这些疾病在我们的数据中发病率最高，对英国乳制品行业来说也是最重要的。与此同时，我们将对我们牛群中的一部分人的唾液进行采样，这样我们就可以确定炎症的一般水平，使我们能够看到我们的行为预测指标对特定疾病的特异性有多高。奶农是他们牛的行为和个性方面的专家，他们的意见对于帮助理解农场数据中的反复无常以及我们的系统是如何运作的至关重要。我们将通过在招募的农场网络中部署我们的系统来测试我们的系统，并将对农民进行深入的半结构化访谈，了解他们在摄像头放置方面的经验(包括农场工人的侵入性和社会接受度)、运营以及对他们的农场、农场工人或动物管理、健康和福利的任何其他感知影响。同样重要的是，我们在设计系统时要考虑到行业的方方面面，让他们有不同的见解和专业知识来设置警报级别，设计用户友好的界面，并讨论更多进入市场的途径，如疾病监测。因此，我们组建了一个合作伙伴联盟，涵盖从农民到兽医、供应链、数据/诊断服务提供商和业务发展等所有关键领域，我们都有与他们成功接触和影响的良好记录。通过协商，我们将制定一项可持续的战略，让有意义的业外利益相关者和公众参与我们的制度和成果，帮助促进公众/利益相关者对制度的广泛了解和接受。

In the UK, dairy milk is a key part of the economy and an important source of nutrition. There are several diseases that regularly develop in UK dairy cows which compromise health and welfare, and lead to economic losses for the farmer and industry. Ill cows have also been found to contribute disproportionately to methane emissions and hence the environmental sustainability of the sector. In addition, high welfare is more important than ever to satisfy societal demands for food production.To help farmers detect and treat these diseases, numerous solutions for automated monitoring of dairy cattle are now available to farmers. A critical disadvantage of all these technologies is that they are focussed on detecting the observable symptoms of later stage disease, when treatment options may be limited, reduction of milk production persistent and animal welfare more severely compromised.A cow's response to infection and trauma is to de-prioritise behaviours not immediately essential to survival and recovery - such as social interactions - in favour of those that remain critical for longer, In a recent study we have found that social exploration, the grooming of others and receiving headbutts were lower in individuals with early stage mastitis. We hence hypothesise that social behaviour changes could be early predictors of disease.Detecting social behaviour changes is difficult for the busy farmer, but is possible by monitoring them at key focal points, such as when queueing for milking or feeding at the feed bunk, using video cameras and artificial intelligence (AI). We have developed highly robust AI that can track the motion of cows in video and recognises each individual through their distinctive coat pattern. Others have now demonstrated good classification of affiliative and agonistic social interactions from video and hence we now propose combining the two ideas to track changes in activities and social behaviours over time for each identified cow in a herd. From collecting two years of video from 64 cameras covering the main barn at our John Oldacre Centre dairy farm, we will train a model that learns what types of behaviours change over time that are indicative of different early stage diseases. We will focus on mastitis and lameness, as these diseases have the greatest incidence in our data and are the most important for the UK dairy industry. At the same time, we will sample the saliva of a subset of our herd so we can determine general levels of inflammation, enabling us to see how specific our behavioural predictors are to particular diseases.Dairy farmers are specialists in the behaviour and personalities of their cattle and their input will be vital to helping understand vagaries in farm data and how our system is functioning. We will test our system by deploying it at a network of recruited farms, and will conduct in-depth semi-structured interviews with the farmers regarding their experiences of camera placement (including intrusiveness and social acceptance by farm workers), operation and any other perceived impacts to their farms, farm workers or animal management, health and welfare. It is also critical that we design the system with all facets of industry, to engage their diverse insights and expertise in setting alert levels, designing user-friendly interfaces that will be well placed to be uptaken and discussing additional routes to market such as for disease surveillance. We have therefore assembled a consortium of partners covering all key areas from farmers to vets, the supply chain, data/diagnostic service providers and business development, all of whom we have a proven track record of successful engagement and impact with. Through consultation we will develop a sustainable strategy for meaningful lay stakeholder and public involvement with our system and results, helping to promote a widespread understanding and public/stakeholder acceptance of the system.