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Moët & Chandon uses AI-assisted optical imaging to assess grape crates as they arrive at the press. Cameras—not an electronic nose—capture the grapes, and computer-vision tools flag damage and quality indicators, including Botrytis risk, to help human experts sort the harvest. The “sniff out” in the original framing is metaphorical: the system does not smell wine or assess its aroma.
What the system examines
The inspection happens when grape crates reach the press, rather than out in the vineyard or after the grapes have become wine. LVMH describes each crate being photographed and analyzed for damage, with an assessment generated at arrival. Hiphen, the technology partner, says its robotics and deep-learning system can detect diseases such as Botrytis and produce a quality indicator for each crate.
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That makes the system an optical assessment tool: it evaluates visible grape conditions from images. It is not a machine that tastes grapes, measures wine aroma, or independently decides how the wine should be made.
How AI supports sorting
Hiphen says the crate-level indicator is intended to assist Moët Hennessy experts who sort grapes at the press. In practice, image analysis can help surface crates that merit closer attention; the published descriptions do not say that an algorithm replaces the experts or makes autonomous winemaking decisions.
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LVMH says Moët & Chandon and Hiphen developed the technology over six years, refining algorithms with more than 6,000 labeled grape images. Those are company-reported development details, not a published independent test of the model’s performance.
What deployment figures show—and what they do not
LVMH says the system was implemented at all Moët & Chandon presses in 2024. The company also reports that it assessed more than 16,000 grape pallets in less than three weeks. That figure indicates reported operational volume; it does not establish how accurately the system identified rot or damage.
The reviewed public accounts do not provide an independent benchmark, sensitivity or specificity, false-positive rate, or quantified results for wine quality, waste reduction, or cost savings. The deployment figures should therefore be read as evidence of use and scale, not proof of measured benefits.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What growers may be able to use
In material published for VivaTech 2025, LVMH said a mobile solution for Moët & Chandon’s 2,339 partner growers was in development. That describes the status at the time of publication; a later release or current availability is not established by that account.
How it fits into broader Champagne-sector AI
The University of Reims Champagne-Ardenne describes other AI and data-science work in the Champagne sector, including disease identification, yield prediction, automatic grape-quality estimation at the press, and fermentation forecasting. It also lists collaborations with Moët Hennessy Champagne Services on alcoholic-fermentation kinetics and algorithm implementation for 2023–2027.
These projects show that data tools are being applied at different points in the production chain, but they are not evidence that the crate-imaging system predicts fermentation or that the university independently validated its performance.
Quick Recap
Sources
- LVMH, VivaTech 2025 technology-partner page (Italian; deployment, development figures, and mobile-solution status).
- Hiphen’s partner account (Botrytis detection and expert-assisted sorting).
- University of Reims Champagne-Ardenne (related Champagne-sector research collaborations).
- Moët Hennessy’s public statement (corroboration of the optical-analysis description and 2024 harvest volume).
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