Viticultural zoning using spatial analysis: characterizing terroirs over the Southern part of the Côtes-du-Rhône appellation (France)

Oxidation processes constitute a main problem in winemaking. Oxidation result in color browning and varietal aroma loss, which are key attributes of wine organoleptic quality [1]. Despite the mechanisms involved in wine oxidation have been extensively reviewed [2], the protection of wine against oxidative spoilage remains one of the main goals of winemaking.
SO2 is one of the most efficient wine antioxidants used to prevent oxidation and microbial spoilage. However, intolerances caused by SO2 have led to the reduction of its concentration in wines.

This talk presents a revision of our knowledge and understanding of the role played by the different aroma chemicals in the positive aroma attributes of wine. A systematic approach to classifying the different aroma chemicals of wine is presented .

Astringency is one of the leading quality factors in red wines.

The concept of Appellation d’Origine Contrôlée (AOC) is based on the existence of a link between the characteristics of the terroir and the quality and typicality of the production (DELAS, 2000). If for a long time, this link only appeared as the fruit of empiricism, the research undertaken recently has made it possible to scientifically establish the complex relationships between the functioning of natural environments and the ability to produce quality.

The main innovation in the VINIoT service is the joint use of two technologies that are currently used separately: vineyard monitoring using multispectral imaging and deployed terrain sensors. One part of the system is based on the development of artificial intelligence algorithms that are feed on the images of the multispectral camera and IoT sensors, high-level information on water stress, grape ripening status and the presence of diseases. In order to obtain algorithms to determine the state of ripening of the grapes and avoid losing information due to the diversity of the grape berries, it was decided to work along the first year 2020 at berry scale in the laboratory, during the second year at the cluster scale and on the last year at plot scale. Different varieties of white and red grapes were used; in the case of Galicia we worked with the white grape variety Treixadura and the red variety Mencía. During the 2020 and 2021 campaigns, multispectral images were taken in the visible and infrared range of: 1) sets of 100 grapes classifying them by means of densimetric baths, 2) individual bunches. The images taken with the laboratory analysis of the ripening stage were correlated. Technological maturity, pH, probable degree, malic acid content, tartaric acid content and parameters for assessing phenolic maturity, IPT, anthocyanin content were determined. It has been calculated for each single image the mean value of each spectral band (only taking into account the pixels of interest) and a correlation study of these values with laboratory data has been carried out. These studies are still provisional and it will be necessary to continue with them, jointly with the training of the machine learning algorithms. Processed data will allow to determine the sensitivity of the multispectral images and select bands of interest in maturation.