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.

Assessing grape composition is critical in vineyard management. It is required to decide the harvest date and to optimize cultural practices toward the achievement of production goals. The grape composition is variable in time and space, as it is affected by the ripening process and depends on soil and climate conditions.

Trunk diseases and esca in particular, represent a major threat to the sustainability of the vineyards. The percentages of unproductive vines in a plot could vary from 4% to over 20 % depending on local conditions and vintages.

CropManage (CM) is an online decision support service (DSS) developed by the University of California, Division of Agriculture and Natural Resources for assisting farmers with efficiently managing water and nitrogen fertilizer to match the site-specific needs of their crops.

The seasonal’s climatic conditions determine the composition of grapes at harvest as they affect the vine’s physiology and development. High temperatures during the grape ripening period cause a high accumulation of sugars and degradation of fruit acidity ,and alter the synthesis of polyphenols. Therefore, some vineyard management can be applied in order to modify grapevine impact on climate variability. One example is the pre-pruning at the beginning of grape ripening, which can delay the ripening period and modify the composition of the grapes at harvest. This work aims to evaluate the pre-pruning field technique on yield components and alcohol content in wines of Tannat and Merlot varieties.