Fertilization with Seaweed (Laminaria japonica) on the characteristics of the bunch and the quality of the grape must of ‘Cabernet Sauvignon’

The ripening of grapevine berries encompasses complex morphological and physiological processes, especially at veraison. Berry shrivel (BS) is a ripening physiological disorder affecting grape berries with visible symptoms appearing short after veraison. The main symptoms of BS are a strong reduction in sugar accumulation, inhibited anthocyanin biosynthesis and high pH values. The most popular red grape cultivar in Austria “Blauer Zweigelt” (Vitis vinifera L.) is specifically prone to develop the BS ripening disorder and up to date a no specific cause or causes could be identified. Recently omics approaches have identified and characterized key processes during grapevine ripening. Among them a small subset of genes, called SWITCH, have been described as markers for the onset of the ripening process in fruits.

In most viticultural regions, grapevines are cultivated grafted, employing either hybrid or pure species of various American Vitis spp., such as V. berlandieri, V. rupestris, and V. riparia, as grapevine rootstocks. These rootstocks play a crucial role in providing resistance to the Phylloxera insect pest. Beyond Phylloxera resistance, it is desirable for grapevine rootstocks to exhibit resistance to other soil-borne pathogens and adaptability to abiotic stress conditions. The introduction of new rootstocks holds promise for adapting agriculture to climate change without altering the characteristics of the final harvested product.

The use of new fungus disease-tolerant varieties is a promising long-term solution to better manage chemical input in viticulture, but unfortunately little is known regarding these new hybrids fruit development and metabolites accumulation in front of abiotic stresses such as water deficit (WD). Thus, prior to the adoption of such varieties by the wine industry in Mediterranean regions, there is a need to consider their suitability to WD.

Between 2002 and 2004 we studied the behaviour of two red grape varieties – Merlot and Cabernet Sauvignon – within the scope of an experimental protocol encompassing 14 plots, 7 of which had not been cultivated, situated in geographically distant locations representing different terroirs of Castilla-La Mancha.

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.