CPS: Small: Developing a Socio-Psychological CPS for the Health and Wellness of Dairy Cows
Lead PI:
Sucheta Soundarajan
Co-Pi:
Abstract

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.

Sucheta Soundarajan
Sucheta Soundarajan is an Associate Professor in the Electrical Engineering & Computer Science Department at Syracuse University. Her areas of interest include algorithms for and applications of social network analysis and data mining, and her research covers topics such as structures of real-world networks, network clustering, sampling, information flow, and centrality. She received her PhD from Cornell University in 2013.
Performance Period: 10/01/2022 - 09/30/2025
Institution: Syracuse University
Sponsor: NSF
Award Number: 2148187