A 4D high resolution vineyard soil assessment for soil-hydrological interpretation in combination with automated data analysis and visualization to manage site-specific grape and wine quality

The production of organically grown crops developed exponentially in the last few decades based on consumer demands for healthy food

Vine cane extracts are a valuable byproduct due to their rich content of polyphenols, vitamins, and other beneficial compounds, which can affect and benefit the vine and the grapes. This study aims to evaluate the response of grapevine plants to irrigation with water supplemented with a vine cane extract, both at physiology response and phenolic composition in different parts of the plant (root, trunk, shoot, leaf, and berry).
Cane extract was obtained by macerating crushed pruning residues with warm water (5:1) and pectolytic enzymes. Two-year-old potted plants were irrigated with water (Control) while others were irrigated with cane extracts, either at 1:4 (w/v, cane extract/water; T 1:4) or at 1:8 (w/v, cane extract/water; T 1:8).

In winemaking, the use of wood products alternative to barrels, has become a useful tool for the achievement of numerous oenological objectives, including the fast release of desirable volatile and polyphenolic compounds, colour stabilization, and important economic advantages if compared to the traditional barrel production. Among a huge array of variables, the wood format, the vinification protocol, especially the moment of the infusion of the woods and the exposed surface area of the alternative woods are of relevant significance, since they may influence the speed and intensity of the aroma transfer from the wood to the wine defining different sensory profiles.

The quality of red wine depends on the composition of polyphenols influencing wine color and taste. The question is, how much we must fear over extraction, especially of seed tannins, under cool climate conditions. The extraction of polyphenols from grape skins and grape seeds were investigated for the grape varieties Cabernet Sauvignon and Pinot noir

Aim: Pests have a high impact on yield and grape quality in viticulture. An objective and rapid detection of pests under field conditions is needed. New sensing technologies and artificial intelligence could be used for pests detection in digital viticulture. The aim of this work was to apply computer vision and deep learning techniques for automatic detection of spider mite symptoms in grapevine under field conditions.