Microclimatic differences in fruit zone of vineyards on different elevations of ‘nagy-eged hill’ in eger wine region, Hungary

Concepts such as typicality are difficult to demonstrate using the limited set of samples that can be subjected to sensory evaluation. This is due both to the complexity of the concept and to the limitations of traditional sensory evaluation (number of samples per session, panel fatigue, the need for multiple sessions and methods, etc.). On the other hand, there is a large amount of data already available, accumulated through many years of consistent evaluation. These data are held in repositories (such as Platter’s Wine Guide in the case of South Africa Wine, wineonaplatter.com) and in technical notes provided by the producers.

Wine storage ensuring the quality and correct aging is one of the issues that wineries, wine traders and consumers encounter after wine bottling.

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).

In mid to late ripening, sugar and potassium (K+) accumulation into the berry slows and is eventually completed1. K+ is the most abundant cation in the berry, undertaking important physiological roles.

This study examines the growing season climates of selected wine regions worldwide that have significant areas under Pinot noir.