Deep learning based models for grapevine phenology

In Burgundy, a climat has nothing to do with the weather but accurately designates a named vine plot, often centuries-old, which produces a singular wine. This wine is the combination of history, the natural environment (relief, type of soil, exposure to the sun), a grape variety and know-how going back thousands of years. The grapes of each climat are harvested separately and the wine is made from a single grape variety and has a unique name featured on the bottle. Romanée conti, clos de vougeot, montrachet, musigny, corton…

The contemporary wine sector confronts a formidable array of challenges, including burgeoning production costs and the constricted availability of natural resources. Heightened consumer awareness regarding sustainability issues further compounds these pressures, compelling companies to adopt more judicious resource utilization strategies. In response to these imperatives, there is a growing recognition of the need to overhaul production methodologies within the wine industry with a view to minimizing inputs and eliminating waste.

Wine vinegars have a tangy flavor and are versatile in cooking. They’ve been used since the neolithic period and are now used as microbial inhibitors and acidifiers. They’re low in calories, have antioxidants, and have a long shelf life, but quality may decrease after opening. The objective of this study focuses on the physical-chemical, sensory, and emotional characterization of wine vinegar samples from the douro demarcated region and la rioja. In total, 22 samples of wine vinegar were analyzed at the time of opening.

The introduction of ai-driven tools in digital content creation represents a significant shift in the landscape of marketing, particularly for industries reliant on rich visual storytelling such as the wine sector. The development of ai models like openai’s sora, runway’s gen-2 or google’s lumiere, which can generate realistic video content from textual descriptions, offers promising new avenues for enhancing brand narrative and consumer engagement. This research explores the potential of text-to-video (t2v) ai models to revolutionize wine marketing by creating dynamic, engaging content that captures the essence of vineyards and their products without the need for traditional video production processes.

To characterize a wine, the most frequently used criteria describe its color, its origin, the grape varieties from which they come, or even for white wines its residual sugar content (dry, semi-dry, sweet). In france, the system of appellations of origin set up in 1919 was initially based solely on the notoriety and origin of the wines. But given the unfavorable consequences that this lack of details generated, the public authorities quickly integrated in 1927 into the “capus” law criteria for access to designations of origin, relating to the specific characteristics of the soils of the vineyards and the grape varieties used, in particular exclusion of interspecific hybrid varieties. In 1935 the creation of the aoc system confirmed the interest in precisely defining all the production conditions that must be implemented to be able to claim the benefit of an aoc, and grape varieties were an essential condition for acquisition.