Case study on the development of a recommender for apple disease diagnosis with a knowledge-based Bayesian Network

This paper presents a case-study of a knowledge-based recommender system capable to diagnose post-harvest diseases of apples. It describes the process of knowledge elicitation and construction of a Bayesian Network reasoning system as well as its evaluation with three different types of studies involving diseased apples. The ground truth of diseased instances has been established by genome sequencing in a lab. The paper demonstrates the performance differences of knowledge-based reasoning mechanisms due to different users interacting with the system under different conditions and proposes methods for boosting the performance by likelihood evidence learned from the estimated consensus of users' and expert's interactions.