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A Machine Learning Pipeline for Predicting Pinot Noir Wine Quality from Viticulture Data: Development and
Don Kulasiri1, Sarawoot Somin1, Samantha Kumara Pathirannahalage1
1Centre for Advanced Computational Solutions (C-fACS), Lincoln University, Lincoln 7647, New Zealand.
Foods (Basel, Switzerland)
|October 16, 2024
Summary
Predicting Pinot Noir wine quality before harvest is possible using machine learning. This study developed a pipeline linking vineyard characteristics and grape composition to expert-judged wine quality, enabling early quality assessments for growers.
Area of Science:
- Viticulture and Enology
- Agricultural Machine Learning
- Data Science in Winemaking
Background:
- Wine quality is intrinsically linked to grape quality, influenced by viticulture practices and climate.
- Expert assessment of wine quality is time-consuming and costly.
- Predicting wine quality pre-harvest allows for proactive management of grape quality.
Purpose of the Study:
- To investigate machine learning for predicting Pinot Noir wine quality.
- To develop a predictive pipeline from vineyard to wine quality indices.
- To relate viticulture parameters to grape and wine composition and expert-assessed quality.
Main Methods:
- Developed a machine learning pipeline integrating vineyard data, grape composition, and wine chemical analysis.
- Utilized expert judgments as the gold standard for wine quality.
- Created a web-based application for yield and quality prediction.
Main Results:
- Successfully predicted Pinot Noir wine quality using viticulture and grape composition data.
- Demonstrated that vineyard characteristics can predict grape yield.
- Established a link between grape/wine composition and expert-evaluated quality.
Conclusions:
- Machine learning offers a viable approach for predicting wine quality early in the process.
- The developed pipeline provides valuable tools for vineyard owners to forecast wine quality.
- Early prediction empowers strategic decisions for optimizing grape production and wine quality.
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