Cabernet Sauvignon and Petite Sirah are the top red wine cultivars in CA, however, the hot climate in Fresno is not ideal for red Vitis Vinifera, particularly for berry color development. Mechanical pruning and irrigation were studied previously to significantly affect grapevine yield performance and berry quality. But there is lack of studies on cordon height and irrigation on mechanical pruned vineyard system.

Climate changes have important consequences in viticulture, heat waves accompanied by periods of drought are encountered more and more frequently. This study aims to evaluate the single and combined effect of water deficit and high temperatures on the thiol precursors biosynthesis in Sauvignon blanc grapes. For this purpose, a protocol has been developed for the cultivation of berries on a solid substrate. The berries, collected at three different times starting from veraison and grown in vitro, were subjected to 4 different treatments: control (C), water stress (WS), heat stress (HS), combined water and heat stress (WSHS). Water stress was simulated by adding abscisic acid to the culture medium, while different temperatures, respectively 25°C and 35°C, were managed with two illuminated climatic chambers.

Wine colour is the first quality characteristic to be assessed, especially regarding red wines. Anthocyanins are very well known to be the main responsible compounds for red wine colour. Red cultivars can synthesize and accumulate anthocyanins in berry skin to express their colour. However, anthocyanin accumulation is often influenced by a series of factors, such as genetic regulation, phytohormones, environmental conditions and viticultural management.

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.

Glutathione is a tripeptide that is mainly found in reduced form in grapes. It generates during the maturation of the grape, increasing significantly after veraison [1].