CPS: Small: Developing a Socio-Psychological CPS for the Health and Wellness of Dairy Cows

在全国范围内，奶农极易受到各种因素的影响，包括牛的健康和健康。例如，当一头奶牛生病或有压力时，她的产奶量就会减少。随后，她可能会出现继发性疾病，因为她不想为食物或水而竞争。例如，如果一头奶牛从牛群中分离出来，这可能表明她生病了。现代农民已经使用了大量的生物特征信息来监控他们的动物，但对牛的社会和心理因素还没有得到很好的研究。拟议项目的目的是建立一个网络-物理系统，将奶牛的社会互动与其他生物识别数据整合在一起，开发预测模型，使用这些数据及早识别患病或脆弱的牛，并创建算法，为养牛农民提供适应性干预。这个项目有可能产生重大的科学和商业影响。小农场的衰落是全国众所周知的问题，任何有助于提高农民利润率的东西都是有价值的。该项目将产生关于牛群管理的一般指导方针--这可以作为新农民的某种“教程”--以及个人牛群管理技术。拟议中的项目将是首批研究国内牛群相互作用网络并试图将这些网络与生物特征数据和疾病联系起来的项目之一。在传感方面，主要贡献将包括将位置/交互数据与其他生物特征数据合并，以检测社交行为。在分析和行动方面，贡献将包括将奶牛的网络行为与其健康和健康联系起来，通过归纳逻辑编程从农民对不同牛管理场景的响应中学习推荐规则，以及设计可解释的算法以提供解决健康和健康问题的建议。实验将在美国东北部选定的奶牛场进行。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Across the country, dairy farmers are extremely vulnerable to a variety of factors, including cattle health and wellness. For example, when a cow is ill or stressed, she produces less milk. Subsequently, she might develop secondary conditions because she doesn’t want to compete for food or water. For example, if a cow has separated herself from a herd, this may indicate that she is sick. Modern farmers already use a great deal of biometric information to monitor their animals, but social and psychological factors of cattle have not been well-studied. The purpose of the proposed project is to build a cyber-physical system that integrates the social interactions of dairy cattle with other biometric data, develop predictive models that use such data to perform early identification of sick or vulnerable cattle, and create algorithms to provide adaptive interventions to the cattle farmers. This project has the potential to lead to substantial impacts, both scientific and commercial. The decline of small farms is a well-known problem across the country, and anything that helps improve farmers’ profit margins is valuable. This project will result in general guidelines about herd management- which could act as a “tutorial” of sorts to new farmers- as well as technologies for individual herd management. The proposed project will be one of the first to study the interaction networks of domestic cattle herds and attempt to tie those networks to biometric data and illness. On the sensing side, major contributions will include merging location/interaction data with other biometric data to detect social behaviors. On the analysis and action side, contributions will include connecting network behaviors of dairy cattle to their health and wellness, learning recommendation rules from farmers’ responses on different cattle management scenarios through inductive logic programming, and designing explainable algorithms to provide recommendations for addressing health and wellness problems. Experimentation will be conducted using selected dairy farms in Northeast USA.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.