Étude de la flore levurienne de différents terroirs alsaciens

Vitis vinifera ‘Nebbiolo’ cultivar is a 3’-subsituted anthocyanin prevalent wine variety. It is grown in North-West Italy for the production of high quality ageing wines. In the present work berry skin anthocyanin amounts and profiles of the clones CVT 308, CVT 423 and CVT 142 were studied in 2004 and in 2005 in four environmentally different locations of North-West Italy: Donnas (steep mountain area), Monforte (hilly area, with a pH of 8.1), Vezza (hilly area, with a pH of 8.2) and Lessona (plain area, with a pH of 4.8).

The growing demand for non-alcoholic beverages, driven by health-conscious consumers and shifting social norms, has positioned dealcoholized wines as a promising alternative in the global beverage industry (Akhtar et al., 2025, in press; Kakroo, 2024).

Currently, the greatest challenge for vine growers is to improve the yield and quality of grapes by minimizing costs and environmental impacts. This goal can be achieved through a better knowledge of vineyard spatial variability. Traditional platforms such as airborne, satellite and unmanned aerial vehicles (UAVs) solutions are useful investigation tools for vineyard site specific management.

The Ribera del Duero Designation of Origin (DO) has acquired great recognition during the last decades, being considered one of the highest quality wine producing regions in the world. This DO has grown from 6,460 ha of vineyards officially registered in 1985 to approximately 21,500 ha in 2013. The total grape production stands at around 90 million kg, with an average yield that approaches nearly 4,500 kg/ha. Most vineyards are cultivated under rainfed conditions.

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.