Remote sensing and ground techniques, applied to the characterization of a new viticultural region at Pinto Bandeira, Brazil

Wine biological aging is characterized by the development of yeast strains that form a biofilm on the wine surface after alcoholic fermentation. These yeasts, known as flor yeasts, form a velum that protects the wine from oxidation during aging. Thirty-nine velums aged from 1 to 6 years were sampled from “Vin jaune” from two different cellars. We show for the first time that these velums possess various aspects in term of color and surface aspects. Surprisingly, the heterogeneous velums are mostly composed of one species, S. cerevisiae. Scanning electron microscope observations of these velums revealed unprecedented biofilm structures and various yeast morphologies formed by the sole S. cerevisiae species.

The Champagne vine-growing region has played a pioneering role in the delimitation of appellations of origin (AOC). The implementation of the Act of July, 22nd 1927 has led to drawing up lists of vine plots based on the criterion of vine cultivation antecedence.

As Vitis vinifera varieties are susceptible to fungal diseases, numerous chemical treatments are generally required to ensure the quantity and quality of the harvest. However, in the context of sustainable viticulture, there are increasing societal request, political incitation, and winegrowers’ awareness to reduce the use of pesticides.

Understanding vineyard variability to target management strategies, apply inputs efficiently and deliver consistent grape quality to the winery is essential. However, despite inherent vineyard variability, the majority are managed as if they are uniform. VitiCanopy is a simple, grower-friendly tool for precision/digital viticulture that allows users to collect and interpret objective spatial information about vineyard performance. After four years of field and market research, an upgraded VitiCanopy has been created to achieve a more streamlined, technology-assisted vine monitoring tool that provides users with a set of superior new features, which could significantly improve the way users monitor their grapevines. These new features include:
• New user interface
• User authentication
• Batch analysis of multiple images
• Ease the learning curve through enhanced help features
• Reporting via the creation of colour maps that will allow users to assess the spatial differences in canopies within a vineyard.
Use-case examples are presented to demonstrate the quantification and mapping of vineyard variability through objective canopy measurements, ground-truthing of remotely sensed measurements, monitoring of crop conditions, implementation of disease and water management decisions as well as creating a history of each site to forecast quality. This intelligent tool allows users to manage grapevines and make informed management choices to achieve the desired production targets and remain profitable.

Traditional methods for identifying downy mildew in commercial vineyards are often labour-intensive, subjective, and time-consuming.