Decline of rootstock-mediated physiological responses in Tempranillo grapevines by prolonged extreme conditions

Artificial intelligence (AI) for winegrowers refers to robotics, smart sensor technology, and machine learning applied to solve climate change problems. Our research group has developed novel technology based on AI in the vineyard to monitor vineyard growth using computer vision analysis (VitiCanopy App) and grape maturity based on berry cell death to predict flavor and aroma profiles of berries and final wines.

The typicality of a wine, as well as its aromatic identity, are attributes that are highly sought after and requested by the current market. It is therefore of considerable technological interest to investigate the aromatic aspects of specific wines and to identify the odorous substances involved.In this thesis work, the characterization of the aromatic composition of Prosecco wines available on the market with a price range between 7 and 13 euros was carried out. These wines came from three different areas of origin such as Valdobbiadene, Asolo and Treviso.

We performed a DC resistivity monitoring experiment during eight months in 2003. Low, medium and high resolution measurements have been carried out at various locations of a vineyard. General apparent resistivity mapping evidences the spatial variations of the summer drying of the subsurface.

Botrytis cinerea causes grey mold that results in severe problems for wine makers worldwide. Infected grapes lead to quality deterioration including formation of off-flavors or browning. The latter is caused by the enzyme laccase which is capable of oxidizing a wide range of phenolic compounds. Since the use of conventional pesticides is associated with many concerns of consumers and authorities regarding environmental and health related issues and may result in fungicide resistance, the development of green alternatives is gaining more attention.

The application of remote and proximal sensing in viticulture have been demonstrated as a fast and efficient method to monitor vegetative and physiological parameters of grapevines. The collection of these parameters could be highly valuable to derive information on associated yield and quality traits in the vineyard. However, to leverage the informative potential of the sensing systems, a series of preliminary evaluations should be carried out to standardize working protocols for the specific features of a winegrowing area (e.g., pedoclimate, topography, cultivar, training system). This work aims at evaluating remote and proximal sensing systems for their performance and suitability to provide information on the vegetative, physiological, yield and qualitative aspects of vines and grapes as a function of different training systems in the Valpolicella wine region (Verona, Italy).