Enoforum 2021
IVES 9 IVES Conference Series 9 Enoforum Web 9 Enoforum Web Conference 2021 9 How artificial intelligence (AI) is helping winegrowers to deal with adversity from climate change

How artificial intelligence (AI) is helping winegrowers to deal with adversity from climate change

Abstract

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. Smart sensor technology, such as low-cost electronic noses, has been developed and tested to monitor in the vineyard and the winery effects of smoke contamination and smoke taint, respectively, by analyzing in real-time samples and detecting taint levels and smoke-related compounds in berries, must and wines. AI has also been applied to big data collected by vineyards and on vertical vintage libraries of wines to develop specific models based on machine learning to predict wines’ aroma profiles based on weather and management information. Our ground-breaking developments on sensory analysis and biometrics from consumers include emotional response and physiological response, such as heart rate, blood pressure, skin temperature, and gesture changes. These parameters have been used to develop AI-based models to assess back viticultural and winemaking management throughout the grape and wine production chain. Information from this integrated AI system (smart sensor and sensory/biometrics) can be used to modify vineyard management strategies, such as canopy management and irrigation scheduling, to target specific consumer preference or wine styles uniformity. The same technology can also be applied for traceability, authentication, and counterfeiting measures using blockchain.     

DOI:

Publication date: April 23, 2021

Issue: Enoforum 2021

Type: Article

Authors

Sigfredo Fuentes1*, Eden Tongson1 and Claudia Gonzalez Viejo1

1Digital Agriculture, Food and Wine Research Group. School of Agriculture and Food. Faculty of Veterinary and Agricultural Sciences. The University of Melbourne. Royal Parade. 3010. Victoria. Australia.

Contact the author

Tags

Enoforum 2021 | IVES Conference Series

Citation

Related articles…

Assessing the impact of defoliation on grape volatiles profile and wine odor characteristics in four Greek red varieties (Vitis vinifera L.) using multivariate chemometrics

Context and purpose of the study. Cultivation techniques are widely recognized for their significant impact on the aroma profile of grapes and wines.

Evaluation of intravarietal variability and selection for tolerance to downy mildew: The case of Antão Vaz variety in Portugal 

Antão Vaz is a Portuguese white grapevine variety grown mainly in the wine-growing regions of Southern Portugal, particularly in the Alentejo, Lisbon and Setúbal peninsula regions. It is a very vigorous and productive variety, giving the wines a strong identity. It needs heat and sunlight and prefers deep and dry soils, which makes it tolerant to scald caused by the high summer temperatures of Southern Portugal. However, this variety is very susceptible to downy mildew, caused by plasmopara viticola, a very destructive disease in years with rainy springs.

Proteomic profiling of grape berry presenting early loss of mesocarp cell vitality

From fruit set to ripening, the grape berry mesocarp experiences a wide range of dynamic physical, physiological, and biochemical changes, such as mesocarp cell death (MCD) and hydraulic isolation. The premature occurrence of such events is a characteristic of the Niagara Rosada (NR) variety, utilised as table grapes and winemaking. In our opinion, the onset of ripening would not cause MCD, but a down-regulation of respiratory enzymes during the early loss of cell viability, while maintaining membrane integrity. For this, we investigated three distinct developmental stages (green (E-L33), veraison (E-L35), and ripe (E-L39)) of NR berries by label-free proteomics, enzymatic respiratory activity and outer mesocarp imaging. Cell wall-modifying proteins were found to accumulate differently throughout ripening, while cytoplasmic membranes continue intact.

New disease-resistant grapevine varieties response to drought under a semi-arid climate

In many regions, climate change leads to an increase in air temperature combined with a reduction of rainfall, intensifying climatic demand and water deficits (WD) (Cardell et al. 2019), which in turn may negatively impact grapevine development, yield and grape composition (Santos et al. 2020). In addition, climate change may also increase disease pressure, leading to further yield and quality losses, besides increasing costs due to increased vineyard spraying (Santos et al. 2020) and reducing viticulture acceptability by consumers (Guichard et al. 2017). Adopting new resistant varieties appears as a promising long-term solution to better manage vine protection, but unfortunately little is known regarding their behavior in front of WD.

Coming of age: do old vines actually produce berries with higher enological potential than young vines? A case study on the Riesling cultivar

Consumers and the wine industry tend to agree on the ability of old vines to produce fruit that allows the production of wine of superior character. However, despite past and ongoing research, objective evidence of this point of view is still debated and studies on robust, specifically dedicated plots are scarce. Thus the impact of grapevine age on berry oenological potential and wine quality remains an open question. To try to objectively address the issue, a unique vineyard was established at Geisenheim University, Germany. It was planted in 1971 with cv. Riesling grafted on 5C Teleki. In 1995 and 2012, several rows were uprooted and replanted with the same rootstock/scion combination, resulting in a vineyard with alternate rows of identical plant material, but with different planting dates. The parameters of technical maturity and grape composition at harvest were analyzed during seasons 2014, 2015, 2016 and 2017 combining HPLC and enzymatic methods. Separate micro-vinifications were made for each age group and wine composition was analyzed by a combination of 1H-NMR and SPE-GC-MS.