Data deluge: Opportunities, challenges, and lessons of big data in a multidisciplinary project

Sparkling wine (SW) production entails a two-steps process where grape must undergoes a primary fermentation to produce a base wine (BW) which is then refermented to become a SW. This process allows for the development of a new physicochemical profile characterized by the presence of foam and a different organoleptic profile.

Dans le vignoble champenois, le risque thermique associé au gel des bourgeons au printemps et en hiver est très mal connu et ne peut être envisagé qu’à l’échelle locale, en raison d’une variabilité spatiale forte. L’objectif de l’étude est d’appréhender ce risque de façon fiable et pluri locale en utilisant le réseau de stations météos récemment implanté.

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).

Contamination problems arising from the use of pesticides in viticulture have raised concerns. One of the alternatives to reduce contamination is the use of elicitors, molecules capable of stimulating the natural defences of plants, promoting the production of phenolic compounds (PC) that offer protection against biotic and abiotic stress. Previous studies on Cabernet-Sauvignon seedlings demonstrated that foliar application of elicitors methyl jasmonate (MeJ) and benzothiadiazole (BTH) increased proteins and PC involved in grapevine defence mechanisms. However, no trials had been conducted on Monastrell seedlings, a major winegrape variety in Spain.

Two transmittance-based chlorophyll meters (SPAD-502 and CCM-200) were evaluated in estimating chlorophyll (Chl) and nitrogen (N) levels in grapevine leaves.