Predicting wine properties based on weather conditions using machine learning techniques

Miovska, Sijce and Martinovska Bande, Cveta and Stojkovic, Natasa (2024) Predicting wine properties based on weather conditions using machine learning techniques. In: 47 ICT and Electronics Convention, Artificial Intelligence Systems, 20-24 May 2024, Opatija, Croatia. (In Press)

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Abstract

Wine quality depends on different factors from cultivation to production. The main factors affecting the quality are weather and climate, growing practices of the vineyard and techniques used by winemakers. This paper explores the effectiveness of various machine learning algorithms to predict the quality based on various features. The wine dataset is prepared from a certification and quality assessment laboratory, containing various physicochemical characteristics such as alcohol content, volatile acids, total extracts, residual sugar, among others. Weather conditions, including precipitation levels, daily average temperature, temperatures exceeding 10°C, and relative air humidity, exhibit varying impacts on vineyards during different growth stages, thus are analyzed in a phenologically relevant manner. Our analysis shows that AdaBoost outperforms all other classifiers with 75% accuracy. Furthermore, we investigate how weather conditions impact the characteristics of white and red wines from diverse regions in North Macedonia, each with its own unique climate and soil conditions. Results indicate that high temperatures without precipitation during the ripening period positively affect wine quality. The analysis yielded a Pearson coefficient of -0.11 for the correlation between air humidity and alcohol content, and 0.19 for the correlation between average temperature and residual sugar levels.

Item Type:	Conference or Workshop Item (Paper)
Subjects:	Natural sciences > Computer and information sciences Engineering and Technology > Electrical engineering, electronic engineering, information engineering
Divisions:	Faculty of Computer Science
Depositing User:	Cveta Martinovska Bande
Date Deposited:	01 Jul 2024 07:25
Last Modified:	01 Jul 2024 07:25
URI:	https://eprints.ugd.edu.mk/id/eprint/34382

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