  Decision support systems have become increasingly popular in the domain of
agriculture. With the development of automated machine learning, agricultural
experts are now able to train, evaluate and make predictions using cutting edge
machine learning (ML) models without the need for much ML knowledge. Although
this automated approach has led to successful results in many scenarios, in
certain cases (e.g., when few labeled datasets are available) choosing among
different models with similar performance metrics is a difficult task.
Furthermore, these systems do not commonly allow users to incorporate their
domain knowledge that could facilitate the task of model selection, and to gain
insight into the prediction system for eventual decision making. To address
these issues, in this paper we present AHMoSe, a visual support system that
allows domain experts to better understand, diagnose and compare different
regression models, primarily by enriching model-agnostic explanations with
domain knowledge. To validate AHMoSe, we describe a use case scenario in the
viticulture domain, grape quality prediction, where the system enables users to
diagnose and select prediction models that perform better. We also discuss
feedback concerning the design of the tool from both ML and viticulture
experts.
