Version 1
: Received: 4 September 2024 / Approved: 4 September 2024 / Online: 4 September 2024 (13:12:26 CEST)
How to cite:
Kulasiri, D.; Somin, S.; KumaraPathirannahalage, S. A Machine Learning Pipeline for Predicting Wine Quality from Viticulture Data: Development and Implementation. Preprints2024, 2024090366. https://doi.org/10.20944/preprints202409.0366.v1
Kulasiri, D.; Somin, S.; KumaraPathirannahalage, S. A Machine Learning Pipeline for Predicting Wine Quality from Viticulture Data: Development and Implementation. Preprints 2024, 2024090366. https://doi.org/10.20944/preprints202409.0366.v1
Kulasiri, D.; Somin, S.; KumaraPathirannahalage, S. A Machine Learning Pipeline for Predicting Wine Quality from Viticulture Data: Development and Implementation. Preprints2024, 2024090366. https://doi.org/10.20944/preprints202409.0366.v1
APA Style
Kulasiri, D., Somin, S., & KumaraPathirannahalage, S. (2024). A Machine Learning Pipeline for Predicting Wine Quality from Viticulture Data: Development and Implementation. Preprints. https://doi.org/10.20944/preprints202409.0366.v1
Chicago/Turabian Style
Kulasiri, D., Sarawoot Somin and Samantha KumaraPathirannahalage. 2024 "A Machine Learning Pipeline for Predicting Wine Quality from Viticulture Data: Development and Implementation" Preprints. https://doi.org/10.20944/preprints202409.0366.v1
Abstract
The quality of wine depends upon the quality of the grapes, which, in turn, are affected by different viticulture aspects and the climate during the grape-growing season. Obtaining wine professionals’ judgments of the intrinsic qualities of selected wine products is a time-consuming task. It is also expensive. Instead of waiting for the wine to be produced, it is better to have an idea of the quality before harvesting, so that wine growers and wine manufacturers can use high-quality grapes. The main aim of the present study was to investigate the use of machine learning aspects in predicting wine quality and to develop a pipeline which represents the major steps from vineyards to wine quality indices. This pipeline outputs the predicted yield, values for basic parameters of grape juice composition, values for basic parameters of the wine composition, and quality. We also found that the yield could be predicted because of input data related to the characteristics of the vineyards. Finally, through the creation of a web-based application, we have investigated the balance berry yield and wine quality. Using these tools further developed, vineyard owners should be able to predict the quality of the wine they intend to produce from their vineyards before the grapes are even harvested.
Biology and Life Sciences, Food Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
