A comprehensive and accurate annotation for the grapevine T2T genome 

Smoke impact is an ongoing and growing issue for vintners across the globe, with the west coast of the U.S. and Australia being two of the largest wine industries impacted. Wine has shown to be especially sensitive to smoke exposure, often acquiring off-flavor sensory characteristics, such as “burnt rubber”, “ashy”, or other medicinal off-flavors.1 While several studies have examined the chemical composition of smoke influences on wine, some studies disagree on what compounds are having the largest impact on smell and flavor.2 This study is designed as a bottom-up approach to inventory the chemical compounds derived from smoke from a grassland-like fire that are potentially influencing wine chemical composition.

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.

L’activité photosynthétique et les relations hydriques de plantes de Syrah sur R99 un mois après la véraison ont été étudiées dans un vignoble de la région de Stellenbosch. Le vignoble, planté à 2,75 entre rangs et 1,5 m sur le rang, sur un sol de type Glenrosa, était en pente et exposé a l’ouest: pour les rangs on avait adopté une orientation nord

Remote sensing, using Near Infra Red wavelength, can characterize within-vineyard variability using vegetation index. Between 2007 and 2009, a study was led on the vineyards of a cooperative winery, in Fitou area (France) aiming at characterizing vineyard oenological potential. A vegetation index, green leaf cover, developed on crops (wheat, rice, corn…) was implemented on vineyards.

Grapevine is one of the most extensively cultivated fruit crops, playing a crucial role in the economies of many grape-growing regions around the world.