Bayesian Case-Exclusion and Personalized Explanations for Sustainable Dairy Farming (Extended Abstract).

Smart agriculture (SmartAg) has emerged as a rich domain for AI-driven decision support systems (DSS); however, it is often challenged by user-adoption issues. This paper reports a case-based reasoning (CBR) system, PBI-CBR, that predicts grass growth for dairy farmers, combining predictive accuracy and explanations to improve user adoption. PBI-CBR's novelty lies in the use of Bayesian methods for case-base maintenance in a regression domain. Experiments report the tradeoff between predictive accuracy and explanatory capability for variants of PBI-CBR, and how updating Bayesian priors each year improves performance.