Machine vision techniques for quality control in the wine industry

The wine industry is undergoing a significant digital transformation with the integration of Machine Vision Systems (MVS) for automated, precise quality control across various production stages. Despite increasing interest in MVS applications, the literature lacks a comprehensive synthesis of how these technologies are integrated throughout the winemaking process. This systematic review addresses this gap by categorizing MVS applications according to their technological approach - Stereo Vision (SV), Remote Sensing (RS), Hyperspectral Imaging (HSI), X-ray Imaging (XRI), Thermal Imaging (TI), and Magnetic Resonance Imaging (MRI) - and mapping their deployment across distinct phases of wine production. A total of 77 studies published between 2013 and 2025 were selected based on PRISMA guidelines and clearly defined inclusion criteria. The findings reveal significant advances in vineyard monitoring, grape sorting, fermentation tracking, and bottling inspection, with MVS technologies enhancing operational efficiency, sustainability, and precision in quality assessment. Nonetheless, challenges persist, particularly in mid-stage processes such as crushing and filtration, and in transitioning laboratory innovations to industrial scales due to economic and infrastructural constraints. This review not only consolidates current knowledge but also outlines critical research gaps and future directions for the integration of MVS within a broader framework of smart and sustainable viticulture. The results are intended to inform researchers, technology developers, and policymakers engaged in the digital transformation of the agri-food sector.