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Related Experiment Video

Updated: May 31, 2026

Profiling the Bacterial Community of Fermenting Traminette Grapes during Wine Production using Metagenomic Amplicon Sequencing
07:34

Profiling the Bacterial Community of Fermenting Traminette Grapes during Wine Production using Metagenomic Amplicon Sequencing

Published on: December 1, 2023

Machine Learning-Enabled Intelligent Technologies Across the Grape and Wine Value Chain: From Vineyard Sensing to

Xian Li1, Mengmeng Bai2, Junhe Wang1

  • 1College of Enology, Northwest A&F University, Yangling, Shaanxi Province, China.

Comprehensive Reviews in Food Science and Food Safety
|May 29, 2026
PubMed
Summary

Machine learning enhances the grape and wine industry by enabling data-driven decisions from vineyard to bottle. This review synthesizes ML technologies and addresses key challenges for intelligent, sustainable winemaking.

Keywords:
data fusiondeep learningfermentation modelinginterpretabilityprecision viticulturequality prediction

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Area of Science:

  • Agricultural Science
  • Computer Science
  • Food Science

Background:

  • Traditional grape and wine production relies on experience, but faces challenges in consistency and efficiency.
  • The grape and wine industry is increasingly adopting data-driven approaches for improved decision-making.

Purpose of the Study:

  • To synthesize machine learning (ML)-enabled technologies across the entire grape and wine value chain.
  • To propose an integrated analytical framework for ML applications in viticulture and winemaking.
  • To critically examine scientific challenges and emerging directions in ML for the wine industry.

Main Methods:

  • Review of ML applications in vineyard sensing, precision viticulture, fermentation monitoring, and winemaking optimization.
  • Analysis of multimodal data integration, model interpretability, and cross-domain generalization.
  • Examination of sensing technologies (spectroscopy, chromatography-mass spectrometry, imaging, electronic sensing) and data preprocessing strategies.

Main Results:

  • An end-to-end pipeline framework linking data acquisition to intelligent decision-making is proposed.
  • Key challenges include multimodal data integration, model interpretability, generalization, decision coordination, and industrial deployment.
  • ML offers significant potential for optimizing practices from vineyard management to quality evaluation.

Conclusions:

  • Despite progress, challenges like data scarcity, heterogeneity, and implementation costs persist.
  • Emerging ML approaches like knowledge-guided learning and causal inference are expected to improve robustness.
  • This review provides a roadmap for advancing intelligent and sustainable practices in the grape and wine industry.