Phenological stage dependency of Cabernet Sauvignon and Grenache response to water and nutrient limitation 

The Japanese beetle, Popillia japonica Newman, is considered one of the most harmful organisms due to its ability to feed on more than 300 plant species. Symptoms indicative of adult beetles include feeding holes in host plants extending to skeletonization of leaves when population numbers are high. The vine is one of the species most affected by this beetle. However, the damaged plants, even if with difficulty, manage to recover, bringing the bunches of grapes to ripeness.

In several cultivars, such as Gewürztraminer and Riesling, grape and wine aromas are determined by volatile terpenoids.

Le comportement de la vigne est étroitement lié aux propriétés hydriques des sols surtout dans leurs parties profondes.

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.

Context and Purpose of the Study. A deeper understanding of the relationship between weather conditions and wine quality is essential for assessing the impact of climate change and developing effective adaptation strategies.