Improving cattle health by developing novel data fusion and machine learning approaches to Internet of things livestock data

通过开发物联网牲畜数据的新型数据融合和机器学习方法来改善牛的健康

• 批准号: 2182032
• 金额: --
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2182032
• 关键词: Improving; cattle; health; developing; novel

The worldwide demand for meat and animal products is expected to increase by at least 40% in the next 15 years. In the last 20 years numbers of cows have decreased by approximately 25% and herd size has gradually increased from 89 to 170 (AHDB, 2014). The cost of endemic cattle diseases (BVD, mastitis, respiratory disease, Johne's ,Bovine tuberculosis etc.) is above £1Billion/year with UK sales of veterinary medicines for farmed animals are over £290M per annum (CHAWG, 2014). Most intervention to disease on farms is reactive rather than proactive (Ruston et al., 2015). Indicators such as raise in temp, change in activity or behaviour are early indicators of disease and can be measured with various senor technologies. An effective, automated precision monitoring solution would be of huge benefit for the early detection of disease in cattle, however, there are no algorithms for cattle health yet that have high predictive value. Key reasons are :a) There are diverse systems utilised such as wearable sensors (measuring body temperature, activity, pH, animal movement, locations), non-wearable sensors (measuring ambient temperature and humidity), automatic milking systems collecting production data, weighing platforms, measures within milk, as well as a raft of health, fertility and production data. The heterogeneity of data type, quantity and quality creates serious challenges for data aggregation and management, different data fusion methods need to be explored and developed and evaluatedb) multifactorial diseases, though one can monitored no of variables but there are complex and often correlated patterns, all need to be accounted for effective machine learning (ML) solution. Figure below shows pattern of temperature and activity of cows monitored and features high correlation between these)c) data representativeness; since most models are trained on only subset of data and in human telemetry domain there are several approaches ensembles, to gather the diversity of the seen data enabling ongoing learning and thus giving good and improved performance (Fischer et al., 2015). None yet been tried for animal health sensing dataThe above issues currently greatly limit the chances of achieving a performant ML-based health monitoring solution for the cattle. Methods: Proposes research will use existing available unique dataset of animal IoT data (see section 5) The data will include 10 farms and over 1000 animals. We will use various data fusion techniques to combine information from a multi-sensor data array to validate signals and create features (Dong and HE, 2007). We will then use signals pre-processing techniques such as FFT and others developed as part of our current project EL4L (el4l.com). We will then evaluate various machine learning algorithms and ensemble method (such as KNN, neural networks, Random forests etc.) utilizing Microsoft Azure Machine Learning Studio already utilized by the groups. Models will be validated on 'new' data. Key Questions are: 1. What methods are best for data fusion (signal level fusion, feature level fusion or decision level fusion for predicting cattle health (disease event, high somatic cell counts) and production (milk, weight gain) and what are penalties of those (with respect to performance, hardware implementation, software implementation)? Year 12. What features are important and have higher predictive value for early prediction of disease i.e single features, fused features ? How early can we predict health event on cattle farm? (related to 1) Year 23. Does using ensemble methods (using online and offline machine learning classifier) gives higher predictive value for this use case? (year 2-3) and validation study to test this on new data

全球对肉类和动物产品的需求预计将在未来15年内增长至少40%。在过去20年中，奶牛数量减少了约25%，牛群规模从89头逐渐增加到170头（AHDB，2014）。地方性牛病（BVD、乳腺炎、呼吸道疾病、约翰氏病、牛结核病等）的成本超过10亿英镑/年，英国用于养殖动物的兽药销售额每年超过2.9亿英镑（CHAWG，2014）。对农场疾病的大多数干预是反应性的而不是主动性的（Ruston等人，2015年）的报告。体温升高、活动或行为变化等指标是疾病的早期指标，可以用各种传感器技术进行测量。一个有效的、自动化的精确监测解决方案将对早期发现牛的疾病有巨大的好处，但是，目前还没有对牛的健康有很高预测价值的算法。主要原因是：a）使用的系统多种多样，如可穿戴传感器（测量体温、活动、pH值、动物运动、位置）、非可穿戴传感器（测量环境温度和湿度）、收集生产数据的自动挤奶系统、称重平台、牛奶中的测量以及大量健康、生育和生产数据。数据类型、数量和质量的异质性给数据聚合和管理带来了严峻的挑战，需要探索、开发和评估不同的数据融合方法; b）多因素疾病，尽管可以监测到任何变量，但存在复杂且往往相关的模式，所有这些都需要考虑到有效的机器学习（ML）解决方案。下图显示了监测的奶牛的温度和活动模式，并具有这些之间的高度相关性）c）数据代表性;由于大多数模型仅在数据子集上进行训练，并且在人类遥测领域中，有几种方法集合，以收集所看到的数据的多样性，从而实现持续学习，从而提供良好和改进的性能（Fischer等人，2015年）的报告。目前，上述问题极大地限制了为牛实现基于ML的高性能健康监测解决方案的机会。研究方法：建议研究将使用现有的动物物联网数据的唯一数据集（见第5节），该数据将包括10个农场和1000多只动物。我们将使用各种数据融合技术来联合收割机从多传感器数据阵列中组合信息，以验证信号并创建特征（Dong和HE，2007）。然后，我们将使用信号预处理技术，如FFT和其他作为我们当前项目EL 4L（el4l.com）的一部分开发的技术。然后，我们将评估各种机器学习算法和集成方法（如KNN，神经网络，随机森林等）。利用Microsoft Azure Machine Learning Studio，该工作室已被各小组使用。模型将根据“新”数据进行验证。关键问题是：1。哪些方法最适合数据融合（信号级融合、特征级融合或决策级融合），用于预测牛的健康（疾病事件、高体细胞计数）和生产（产奶、增重），以及这些方法的惩罚（在性能、硬件实现、软件实现方面）？12年级。哪些特征是重要的，对疾病的早期预测具有更高的预测价值，即单一特征，融合特征？我们能多早预测牛场的健康事件？（1）第23年。使用集成方法（使用在线和离线机器学习分类器）是否为该用例提供了更高的预测值？(year 2-3）和验证研究，以测试新数据