Combination of NIR multispectral information acquired from a ground moving vehicle with AI methods to assess the vine water status in a Tempranillo (Vitis vinifera L.) commercial vineyard

The ability, and the underlying mechanisms of grapevines to cope with and adapt to recurring water constraints, are the focuses of this study.

Lately several works highlighted the capacity of grape cell-wall material (CWM) to interact with proanthocyanidins (PA), indicating its potential use as fining agent for red wines.1–4 However, those studies were performed by using purified PAs and very high doses of CWM (almost ten-fold higher than those used in wine industry for other commercial fining agents). The present study focuses on the applicability of CWM from Cabernet sauvignon pomace as fining agent for red wines under real winery conditions. Grapes of cultivar Cabernet sauvignon were harvested at three different maturity levels
(unripe, mature, and overripe) and used for red winemaking. The pomace of such vinifications were used as source of CWM, and applied into red wines at two different concentrations: 0.2 g/L and 2.5 g/L.

The quality of bottled white wines is highly influenced by their storage conditions, mainly temperature, and exposure to light and oxygen (1, 2).

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.

In 1979 an enlightened and farsighted business owner in an area and in an activity unknown to him and in 120 hectares of land cultivated with corn and wheat expressed to one of us that he wanted to start a business in the wine sector. The first innovative “Vigna Dogarina Scientific Applicative Project” has become famous and harmoniously inserted in and with the “Territoir” of eastern Veneto in northeastern Italy. The revolutionary project allowed one of us: 1. to put into practice results of research related to the applied philosophy, vision, methodology of the “Great MetaEthic Chain 4.1C®” algorithm of the “Conegliano Campus 5.1C®” that considers all material, immaterial, spiritual, technical, economic, environmental, social, existential, relational, ethical, MetaEthical factors with basic indexing in a harmonious chain “ 4.1C®” and application “5.1C®”, 2. to implement: