Application of Hyper Spectral Imaging for early detection of rachis browning in table grapes

Grapevine vigour, defined as the propensity to assimilate, store and/or use non-structural sugars for allowing fast growth of shoots and producing large canopies[1], is crucial to optimize vineyard management. Recently, a model has been proposed for predicting the vigor of young grapevines through the measurement of the vegetative growth and physiological parameters, such as water status and gas exchange[2]. Our objectives were (1) to explore the influence of the association of two grapevine varieties (Tempranillo and Cabernet Sauvignon, grafted onto R110 rootstocks) with arbuscular mycorrhizal fungi (AMF) on the vegetative vigour of young plants; and (2) to assess the effect of environmental factors linked to climate change on the vegetative vigour of Cabernet Sauvignon.

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.

Vitis vinifera L. derivatives are susceptible to contamination by biological agents (e.g., bacteria, viruses, fungi), and chemical agents (e.g., heavy metals, persistent organic pollutants).

Issues that can arise during the alcoholic fermentation are frequently attributed to imbalances or deficiencies in the nutrient composition of the fermentation medium.

Hanseniaspora vineae (Hv) is a fermenting non-Saccharomyces yeast that compared to Saccharomyces cerevisiae (Sc) present some peculiar features on its metabolism that make it attractive for its use in wine production. Among them, it has been reported a faster yeast lysis and release of polysaccharides, as well as increased ß-glucosidase activity. Hv also produces distinctive aroma compounds, including elevated levels of fermentative compounds such as ß-phenylethyl acetate and norisoprenoids like safranal. However, it is known for its high nutritional requirements, resulting in prolonged and sluggish fermentations, even when complemented with Sc strain and nutrients.