Les paysages viticoles des régions Vale Dos Vinhedos et Monte Belo (Brésil), un lien avec l’Etrurie

La Région Piemonte a commencé en 1994 un projet de caractérisation des productions vitivinicoles des terroirs du Barolo (Piemonte, Italie) par une équipe pluridisciplinaire avec la participation de 6 Instituts de recherche qui travaillent dans la Région et la collaboration de 2 Associations des producteurs viticoles et des organismes de valorisation du vin Barolo.

Viticultural management regimes influence the soil elemental profile of a vineyard, determining the microbial community distribution, insect life, and plant biochemistry and physiology

Aims: The aims of this study were to i) characterize the impact of geographical origin and viticulture treatments on Chilean Cabernet-Sauvignon, and ii) develop machine learning models to predict its quality. 

Usually the winemaker consider the grapeberry maturity as an actor of the wine quality. Grape seed are frequently used as a marker to assess the grape maturity. The first aim of this study is to obtain a better understanding of the impact of grape seed maturity on the grape seed and grape berry composition.

The project VINIoT pursues the creation of a new technological vineyard monitoring service, which will allow companies in the wine sector in the SUDOE space to monitor plantations in real time and remotely at various levels of precision. The system is based on spectral images and an IoT architecture that allows assessing parameters of interest viticulture and the collection of data at a precise scale (level of grape, plant, plot or vineyard) will be designed. In France, three subjects were specifically developed: evaluation of maturity, of water stress, and detection of flavescence dorée. For the evaluation of maturity, it has been decided first to work at the berry scale in the laboratory, then at the bunch scale and finally in the vineyard. The acquisition of the spectral hyperstal image as well as the reference analyzes to measure the maturity, were carried out in the laboratory after harvesting the berries in a maturity monitoring context. This work focuses on a case study to predict sugar content of three different grape varieties: Syrah, Fer Servadou and Mauzac. A robust method called Roboost-PLSR, developed in the framework of this work (Courand et al., 2022), to improve prediction model performance was applied on spectra after the acquirement of hyperspectral images. Regarding the evaluation of water stress, to work with a significant variability in terms of water status, it has been worked first with potted plants under 2 different water regimes. The facilities have allowed the supervision of irrigation and micro-climatic conditions. The regression models on agronomic variables (stomatal conductance, water potential, …) are studied. To detect flavescence dorée, the experimental plan has consisted of work at leaf scale in the laboratory first, and then in the field. To detect the disease from hyper-spectral imaging, a combination of multivariate curve resolution-alternating least squares (MCR-ALS) and factorial discriminant analysis (FDA) was proposed. This strategy proved the potential towards the discrimination of healthy and infected leaves by flavescence dorée based on the use of hyperspectral images (Mas Garcia et al., 2021).