Climate change will increasingly challenge future viticulture due to long-enduring and extreme weather conditions, jeopardizing yield and wine quality in various ways.

The cost-effectiveness of a winery requires constant cost control in order to ensure competitiveness on the wine market.

Grape harvest time is one of the most fundamental aspects that affect grape quality and thus wine quality. Many factors influence the decision of harvest; among them technological and phenolic maturity of grape. Technological ripeness is mainly related to sugar concentration, titratable acidity and pH. Conventional methods for chemical analysis of grapes are normally sample-destructive, time-consuming, include laborious sample preparation steps, and generate chemical waste, thereby limiting their utility in online/in-line quality monitoring. Moreover, destructive analyses can be performed only on a limited number of fruit pieces and, thus, their statistical relevance could be limited. This study evaluated the ability of a lab-scale hyperspectral imaging (HYP-IM) technique to predict titratable acidity, organic acid and sugar content of grapes. Samples of Cabernet franc and Chenin blanc grapes were consecutively collected six times at weekly intervals after veraison. The images were recorded thanks to the hyperspectral imaging camera Pica L (Resonon) in a spectral range from 400 to 1000 nm. Statistics were performed using Microsoft Xlstat software. Successively, the berries were analyzed for their sugar (glucose and fructose) and organic acid (malic and tartaric acid) content and titratable acidity according to usual methods.

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.

Botrytis cinerea, a well-known grapevine pathogen, has more than 1200 host plants causing grey rot in grapevine berries. However, it can also result in a desirable phenomenon called noble rot under specific microclimate conditions. An extraordinary demonstration of this natural process can be observed in the creation of aszú wines within Hungary’s Tokaj wine region. Beside B. cinerea other fungi and yeasts are involved in the secondary metabolic development of the grape berry which contributes to the sensory and analytical characterization of noble rot wines.