Phenological stage dependency of Cabernet Sauvignon and Grenache response to water and nutrient limitation 

Vineyard canopy features such canopy porosity and fruit exposure influenced microclimate, fungal disease incidence and grape composition. An objective, rapid and non-invasive method to assess and map the canopy status is needed to apply in precision viticulture. A new method for canopy status assessment and mapping based on non-invasive machine vision was applied in commercial vineyards in this work.

The phenolic profile of a wine plays an essential role in its oxidative capacity and in both white and red wines it defines its shelf life[1]. The study of minority varieties to produce wines with peculiar characteristics necessarily includes the phenolic and oxidative characterization of the wines produced. This paper presents the study of wines made from 24 minority and majority white and red grape varieties, focusing on phenolic characteristics (total phenols, slightly polymerized phenols, highly polymerized phenols, anthocyanins…), color, as well as parameters related to the oxidability of the wines and their capacity to consume oxygen [2].

Grenache Noir and Syrah are the predominant grape varieties in the French Rhone valley vineyard, and produce wines with well differentiated aromatic notes. This study aimed at investigating the contribution of glycoconjugated precursors to these aromatic specificities, through their analytical profiles and the sensory influence of the odorant compounds they release during wine aging. The aglycones released by enzymatic hydrolysis of glycosidic extracts

Calcium-induced instabilities present a major challenge in bottled wines, with calcium tartrate (CaT) precipitation becoming increasingly common due to rising calcium levels in grape must, largely driven by climate change. Although CaT is an insoluble salt, its instability— although less frequent than potassium hydrogen tartrate (KHT) precipitation—is more difficult to predict and control, as it develops gradually over time.

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.