Genetic determinism of grapevine development stages as a tool for the adaptation to climate change

Pomace and grape stems are the main solid organic waste from winery industries, resulting from the pressing and/or fermentation processes it is generated in large amounts in many parts of the world

The Precision Viticulture (PV) is defined “as a management system that is information and technology based, is site specific and uses one or more of the following sources of data: soils, vigour, nutrients, pests, moisture, and yield among others, for optimum profitability, sustainability, and protection of the environment” (OIV, 2018, in process). The elements mentioned in the definition are an important part of the terroir components. The terroir is a tool In Viticulture, it is the analysis and study unit, and the variability of a certain situation can be due to any difference in every element or property of each factor that constitutes it, including the management.The soil and its management are those that bring the most variability to terroir.

This work began as an intellectual game, in order to discuss the notion of wine quality in terms of terroir and territory spatial structure. Vine and wine quality has long been questioned by scientists. Each discipline approaching it with his own tools.

A survey has been conducted on 32 students and 25 former students of 28 nationalities of an international master course training executives of the international Wine sector.

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.