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

人工智能监测社会行为变化，及早发现奶牛疾病

• 批准号: BB/X017559/1
• 负责人: Andrew Dowsey
• 金额: $ 85.19万
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FX017559%2F1
• 关键词: AI; monitor; changes; social; behaviour

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.

在英国，奶牛是经济的关键部分，也是营养的重要来源。在英国奶牛上经常发展几种疾病，这些疾病损害了健康和福利，并导致农民和工业造成经济损失。还发现生病的母牛会导致甲烷排放量不成比例，因此该行业的环境可持续性。此外，高福利对于满足粮食生产的社会需求比以往任何时候都更为重要。为了帮助农民发现和治疗这些疾病，现在可以为农民提供许多自动监测奶牛的解决方案。所有这些技术的一个至关重要的缺点是，它们集中在检测后期疾病的可观察症状上，当治疗方案可能受到限制时，牛奶产量的减少和动物福利的减少和动物福利更加严重。牛对感染和创伤的反应。对于我们的互动而言，不再是社会互动的不断危害 - 在某种程度上不立即进行社会互动，而不是立即进行社会互动 - 在这些互动中 - 在这些互动中的批判性，以及这些互动，以及以下以下以下措施。探索，在早期乳腺炎的患者中，他人的修饰和接受头撞的较低。因此，我们假设社会行为的变化可能是疾病的早期预测因素。对繁忙的农民进行社会行为的变化很难进行检测，但是可以通过在关键焦点上监视它们，例如使用摄像机和人工智能（AI）在饲料中排队或在饲料中排队时。我们已经开发了高度鲁棒的AI，可以在视频中跟踪牛的运动，并通过其独特的外套图案来识别每个人。现在，其他人已经表现出对视频中的会员和激动社会互动的良好分类，因此我们现在建议将两个想法结合在一起，以跟踪活动和社交行为的变化，因为每只确定的母牛都会随着时间的流逝。从64台摄像机收集两年的视频，涵盖了我们的John Oldacre Center奶牛场的主要谷仓，我们将培训一个模型，该模型随着时间的推移而变化，这些模型随着时间的推移而变化，这些行为表明了不同的早期疾病。我们将专注于乳腺炎和la行，因为这些疾病在数据中的发病率最高，并且对英国乳制品行业来说是最重要的。同时，我们将采样牛群子集的唾液，以便我们可以确定炎症的一般水平，使我们能够了解我们的行为预测因素对特定疾病的特定预测因素。养殖农民是其牛的行为和个性的专家，他们的投入将在农场数据和我们的系统中有效地了解型号的范围是至关重要的。我们将通过在招募农场的网络中部署系统来测试我们的系统，并将与农民进行深入的半结构化访谈，以了解他们的相机安置经验（包括农场工人的侵入性和社交接受），运营以及对农场，农场工人或动物管理，健康，健康，健康和福利的任何其他感知的影响。同样至关重要的是，我们必须使用行业各个方面设计该系统，以吸引其各种见解和专业知识来设定警报级别，设计用户友好的界面，这些接口将得到很好的位置，并讨论了其他市场路线，例如疾病监视。因此，我们已经组建了一个合作伙伴联盟，涵盖了从农民到兽医，供应链，数据/诊断服务提供商和业务发展的所有关键领域，所有这些领域都拥有成功的互动和影响力记录。通过咨询，我们将制定一种可持续的战略，以有意义的利益相关者和公众参与我们的系统和结果，从而有助于促进对系统的广泛理解和公众/利益相关者的接受。