A.O.C. taureau de Camargue

The grapevine is an important economical crop that is very sensitive to climate changes and microclimate. The observations made during the last decades at a vineyard scale all concur to show the impact of climate change on vine physiology, resulting in accelerated phenology and earlier harvest (Jones and Davis 2000). It is well-known that berry content is affected by the ambient temperature. While the first experiences were primarily conducted on the impact of temperature on anthocyanin accumulation in the grape, few studies have focused on others component of phenolic metabolism, such as tannins.

The reflectance spectra of vegetation carry potentially useful information that can be used to determine chemical composition and discriminate between vegetation classes. If compared with analytical methods such as conventional chemical analysis, reflectance measurement provides non-destructive, economic, near real-time data. 

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.

Drought and heat stress will pose challenges for the future of viticulture and wine quality, as grapevine biological processes are pushed beyond their optimum conditions. Efforts are increasing to study and predict the effects of drought spells and heatwaves on grapevine physiology and resulting harvest quality. This calls for the development of adequate systems to induce and manipulate the required stress, especially in open field trials where conditions are more difficult to control. We present a semi-controlled system for studying drought and heat stress in grapevine in the field.

Understanding vineyard site contribution to elemental composition of wines has, historically, been limited due to lack of continuity across multiple vintages, as well as lack of uniformity in scion clone and lack of controlled pilot-scale winemaking conditions.  We recently completed our fifth vintage, and have elemental composition characterizing wines from four vintages (2015–2018)