Making Wines Smarter: Evidence from an Interpretable Learning Paradigm

The wine industry has a significant socioeconomic impact. Recent advancements in smart agriculture have greatly influenced vine growth and wine-making processes. In this study, we evaluate wine ratings by relating them to multiple wine characteristics. A novel dataset with various features is curated, and an interpretable learning paradigm is proposed. Several different learning models are employed, leading to promising experimental results. Furthermore, through feature contribution analysis, we discover that climate-related factors have become less influential on wine ratings in recent years, highlighting the effectiveness of recent advancements in smart agriculture.