<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Estimating the current stage of grape ripeness is a crucial step in wine making and becomes especially important during harvesting. Visual inspection of grape seeds is one method to achieve this goal without performing chemical analysis, however this method is prone to failure. In this paper, we propose an unsupervised visual inspection system for grape ripeness estimation using the Dirichlet Mixture Model (DMM). Experimental analysis using real world data demonstrates that our approach can be used to estimate different ripeness stages from unlabeled grape seeds catalogs.</p>
</abstract>

Estimating the current stage of grape ripeness is a crucial step in wine making and becomes especially important during harvesting. Visual inspection of grape seeds is one method to achieve this goal without performing chemical analysis, however this method is prone to failure. In this paper, we propose an unsupervised visual inspection system for grape ripeness estimation using the Dirichlet Mixture Model (DMM). Experimental analysis using real world data demonstrates that our approach can be used to estimate different ripeness stages from unlabeled grape seeds catalogs.

UNSUPERVISED LEARNING FOR RIPENESS ESTIMATION FROM GRAPE SEEDS IMAGES

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"UNSUPERVISED LEARNING FOR RIPENESS ESTIMATION FROM GRAPE SEEDS IMAGES"}

Estimating the current stage of grape ripeness is a crucial step in wine making and becomes especially important during harvesting. Visual inspection of grape...

UNSUPERVISED LEARNING FOR RIPENESS ESTIMATION FROM GRAPE SEEDS IMAGES

Online veröffentlicht: 01. März 2017

Seitenbereich: 1 - 19

Eingereicht: 29. Mai 2017

Akzeptiert: 25. Juli 2017

DOI: https://doi.org/10.21307/ijssis-2017-225

Schlüsselwörter
Mixture model, grape ripening, computer vision

© 2017 S. Hernández et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

UNSUPERVISED LEARNING FOR RIPENESS ESTIMATION FROM GRAPE SEEDS IMAGES

Online veröffentlicht: 01. März 2017

Seitenbereich: 1 - 19

Eingereicht: 29. Mai 2017

Akzeptiert: 25. Juli 2017

DOI: https://doi.org/10.21307/ijssis-2017-225

SchlüsselwörterMixture model, grape ripening, computer vision

© 2017 S. Hernández et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Mixture model, grape ripening, computer vision