Integration of Expert Knowledge in Time Series Analytics as a Leverage for Improved Monitoring and Decision Support in Complex Agricultural Systems

The development of advanced analytics for monitoring and decision support is essential in the context of automation and (sensor) technology. Oftentimes, these analytics serve to extract information from time series data, in which certain measurements are taken from a target subject. In agriculture, these target processes are complex and often influenced by a multitude of internal(e.g. genetics, physiology) and external (e.g. contexts, management) factors. I hypothesize that the integration of expert knowledge in the analytics renders the interpretation more precise, accurate, timely and useful for decision support, even when different states of the target are not discrete or their severity (and thus, the required action) depends on a combination of different factors. In this project, I aim to develop a general framework for the integration of 3 types of expert knowledge into state-estimation time series analytics, and apply them on 2 agriculture-related cases. I will focus on the translation of domain expertise, historical data patterns and context measures to incorporate them in time series models targeting monitoring heat stress in dairy cows and welfare in intensive fish production systems. The general framework will provide a basis for how to consider different types of expert knowledge in biological and agricultural production systems, and via the case studies, provide leverage for more sustainable primary production.