Unsupervised learning to automatically determine microclimates in a vineyard

M. Everett; D. Mommen; J. Shovic

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Unsupervised learning to automatically determine microclimates in a vineyard

In: Precision agriculture '25

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M. Everett

M. Everett
meverett@uidaho.edu

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D. Mommen

D. Mommen

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J. Shovic

J. Shovic

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Type:: Chapter

Pages:: 783–791

DOI:: https://doi.org/10.1163/9789004725232_103

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Dar Hadith al Hassania

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Site-specific management aims to effectively control grower inputs and outputs. It is especially important in vineyards where variability should be controlled to increase wine quality. However, determining what sites should be managed differently is a non-trivial task. In this study, the researchers used 3 years of data from 2 weather stations and 41 temperature/humidity beacons located in a 3.2 ha growing area in Virginia. Two variants of the K-Means unsupervised learning algorithm (Euclidean distance and soft dynamic time warping) grouped the sensors based on temperature and humidity. By combining the different clustering methods, a hybrid cluster was created that was able to segment the vineyard into three areas with different climate characteristics. Based on these results, it is believed that these unsupervised learning methods used in concert with one another are able to automatically create effective microclimate designations for use in grower planning management decisions.

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Precision agriculture '25

E-Book ISBN:: 9789004725232

Publisher:: Wageningen Academic

Print Publication Date:: 18 Jun 2025

Subjects
- Life Sciences
  - General
  - Agriculture & Environment

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Unsupervised learning to automatically determine microclimates in a vineyard

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Precision agriculture '25

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