Green berries on Gewürztraminer (Vitis vinifera L.) in South Tyrol (Italy)

Two treatments were studied in vines of cv. Tempranillo blanco (Vitis vinifera L.) during the 2012-2018 period in an experimental plot located in Rincón de Soto (La Rioja, Spain). Rainfed treatment (R0) was compared with respect to an irrigation treatment (R2) equivalent to 30% of the crop evapotranspiration (ET0) from fruitset to harvest phenological stages. Pre-veraison irrigation ranged from 43 (2014) to 66 mm/m2 (2018) while post-veraison irrigation ranged from 37 (2017) to 115 mm/m2 (2012).The normalized difference vegetation index (NDVI) was assessed by measures of reflectance, nutrients were determined by analysis of petioles sampled at veraison, grape production was determined at harvest as well as renewable wood weight was assessed at pruning time.

The above-ground parts of plants, which constitute the phyllosphere, have long been considered devoid of bacteria and fungi, at least in their internal tissues and microbial presence there was long considered a sign of disease. However, recent studies have shown that plants harbour complex bacterial communities, the so-called “microbiome”[1]. We are only beginning to unravel the origin of these bacterial plant inhabitants, their community structure and their roles, which in analogy to the gut microbiome, are likely to be of essential nature. Among their multifaceted metabolic possibilities, bacteria have been recently demonstrated to emit a wide range of volatile organic compounds (VOCs), which can greatly impact the growth and development of both the plant and its disease-causing agents.

The quality of any alcoholic beverage depends on many parameters, such as cultivars, harvesting time, fermentation, distillation technology used, quality and type of wooden barrels (in case of matured drinks), etc.; however, the most important factor in their classification is content of volatile compounds.

This paper describes a zoning study performed on a vast territory of around 86,500 hectares, situated in the countryside area of La Mancha Central (Castilla-La Mancha). The aim of the study was to classify the environment according to a small number of ecological criteria, establish the relevant territorial units and generate thematic maps with the different levels of criteria employed and synthetic maps by crossing these criteria.

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.