Vine field monitoring using high resolution remote sensing images: segmentation and characterization of rows of vines

Mannoproteins (MPs) are released from the yeast cell wall during alcoholic fermentation and aging on the lees, and influence aspects of wine quality such as haze formation and colour stability. Yet, as this is a slow process with microbiological and sensory risks, the exogenous addition of extracted MPs poses an efficient alternative. While Saccharomyces cerevisiae has long been studied as a prominent source for MPs extraction, their structure and composition greatly differ between yeast species. This may influence their behaviour in the wine matrix and subsequent impact on wine properties. However, although wine yeast species other than S. cerevisiae possibly present an untapped source of MPs, they are still ill-characterised in terms of chemical composition and influence on wine.

This interaction is primarily due to an acidic attack on the ceramic by the wine. It results in (1) the dissolution of the ceramic into the wine and the release of a wide variety of elements; and (2) an increase of the wine pH. The extent of these effects depends on the mineralogical and chemical composition of the ceramic, as well as the hydraulic ratio of the ceramic-wine system (the term hydraulic ratio (ρ) defines here the volume of wine over the surface area of the ceramic in contact with the wine).

In this audio recording of the IVES science meeting 2022, Florence Fontaine (Université de Reims Champagne Ardenne) speaks about grapevine trunk disease. This presentation is based on an original article accessible for free on OENO One.

It is fundamental for wineries to know the potential yield of their vineyards as soon as possible for future planning of winery logistics. As such, non-invasive image-based methods are being investigated for early yield prediction. Many of these techniques have limitations that make it difficult to implement for practical use commercially. The aim of this study was to assess whether yield can be estimated using images taken in-field with a smartphone at different phenological stages. The accuracy of the method for predicting bunch weight at different phenological stages was assessed for seven different varieties.

Implementing artificial intelligence (AI) in viticulture and enology is a rapidly growing field of research with an essential number of potential practical applications.