Red Grenache variety in Rhône Valley : impact of “terroir” and vintages on the aromatic potential of the grapes

Artificial intelligence (AI) for winegrowers refers to robotics, smart sensor technology, and machine learning applied to solve climate change problems. Our research group has developed novel technology based on AI in the vineyard to monitor vineyard growth using computer vision analysis (VitiCanopy App) and grape maturity based on berry cell death to predict flavor and aroma profiles of berries and final wines.

Full understanding of grapevine responses to variable soil resources requires
assessing the grapevine root system. Grapevine root systems are expansive and examining deep roots (i.e., >40 cm)
is particularly important in conditions where grapevines increase reliance on deep soil resources, such as drought
or plant competition. Traditional methods of assessing roots rely on morphological traits associated specific
functions (e.g., root color, diameter, length), while recent methodological advances allow for estimating root
function more directly (e.g., omics). Yet, the potential of applying refined methods remains underexplored for roots
at deep depths.

Row orientation and canopy management are essential for high quality grapevine production. Microclimatic conditions of the leaves and fruits can be influenced by the canopy geometry. Remote sensing is a very promising tool to describe vegetative growth and physiological behavior of vineyards. However, the correlation between remotely sensed data and in situ field measurements has been described scarcely in the scientific literature so far. The aim of the study was to correlate remotely sensed data obtained with Unmanned Aerial Vehicle (UAV) with in situ field measurements to describe canopy structure.

Grapevine cultivation is threatened by the global warming, which combines high temperatures and reduced rainfall, impacting in wine quality and even plant survival. Breeding for varieties resilient to these challenges must address plant traits such as tolerance to supraoptimal temperatures and optimized water use efficiency while minimizing productivity and quality losses. Stomatal abundance (SA) determines the maximum leaf potential for transpiration and thus water loss and cooling. Since SA results from a developmental process during leaf emergence and growth, knowledge on the genetic control of this process would provide specific targets for modification.

Light-induced reactions can be responsible for detrimental changes of white and rosé wines. This is associated to the photo-degradation of riboflavin (RF) and of methionine (Met) causing the appearance of light-struck taste (LST).