Cultivation site effect on the quality of Moscato di Pantelleria

The contemporary wine sector confronts a formidable array of challenges, including burgeoning production costs and the constricted availability of natural resources. Heightened consumer awareness regarding sustainability issues further compounds these pressures, compelling companies to adopt more judicious resource utilization strategies. In response to these imperatives, there is a growing recognition of the need to overhaul production methodologies within the wine industry with a view to minimizing inputs and eliminating waste.

Le mot “terroir” dérive du latin “terra”, mais déjà les Romains l’indiquaient comme “locus” ou”loci”, c’est-à-dire un lieu ayant le “genius”destiné à la production d’un produit d’excellente qualité.

The increase in the temperature and the more severe water stress conditions, trends observed in recent years as a consequence of climate change, are leading a mismatch between the technological and phenolic maturity of grapes

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).

The market of non-alcoholic wine has notably increased in recent years, driven by growing health awareness and regulatory trends aimed at reducing alcohol consumption.