Utilisation de données historiques pour caractériser le millésime en cours

A major topic in viticultural research is the analysis of the relationships between climate on one side, and grape and wine quality on the other. It is well known that climatic conditions

Over the past two decades, climate change has contributed to an increase in sugar content in grape must, and consequently, in the ethanol levels of wines.

The ‘fresh mushroom off-flavour’ has been recognized by the wine industry as an emerging defect since the 2000s. For many years, this off-flavour was not specifically characterized and rather grouped under ‘earthy’ and ‘musty’ taints. However, it has become increasingly problematic due to its rising prevalence. In some vineyards, incidents of this off-flavour now occur as frequently as once every five years. This trend may be associated with climatic changes affecting regions that are more prone to warm and wet seasons.

Grapevine nitrogen (N) monitoring is essential for efficient N management plans that optimize fruit yield and quality while reducing fertilizer costs and the risk of environmental contamination. Unlike traditional vegetative-tissue sampling methods, remote sensing technologies, including hyperspectral imaging, have the potential to allow monitoring of the N status of entire vineyards at a per-vine resolution. However, differential N partitioning, variable spectral properties, and complex canopy structures hinder the development of a robust N retrieval algorithm. The present study aimed to establish a solid understanding of vine spectroscopic response at leaf and canopy levels by evaluating the different nitrogen retrieval approaches, including the radiative transfer model.

Unmanned Aerial Vehicles (UAVs) are increasingly used to monitor canopy structure and vineyard performance. Compared with traditional remote sensing platforms (e.g. aircraft and satellite), UAVs offer a higher operational flexibility and can acquire ultra-high resolution images in formats such as true color red, green and blue (RGB) and multispectral. Using photogrammetry, 3D vineyard models and normalized difference vegetation index (NDVI) maps can be created from UAV images and used to study the structure and health of grapevine canopies. However, there is a lack of comparison between UAV-based images and ground-based measurements, such as leaf area index (LAI) and canopy porosity.