article

A method to estimate Grape Phenolic Maturity based on seed images

Authors:

Claudio FredesAuthors Info & Claims

Computers and Electronics in Agriculture, Volume 101

Pages 76 - 83

https://doi.org/10.1016/j.compag.2013.12.006

Published: 01 February 2014 Publication History

Abstract

The timing of the grape harvest has a strong impact on wine quality. A recent line of studies proposes visual seed inspection by a trained expert to determine Phenolic Maturity. In this paper a method is presented to estimate Grape Phenolic Maturity based on seed images. The acquired images present problems such as shadows, highlights and low contrast. Two classes of seed are defined (mature and immature) by the expert (enologist) involved in the research. The method consists of three stages: segmentation, feature extraction and classification. Segmentation was performed by a hybrid method combining supervised and unsupervised learning, feature extraction by the Sequential Forward Selection algorithm, and classification by a Simple Perceptron. The results for each stage are presented. The method as a whole proved to be simple and effective in the classification of seeds. Therefore, it is possible to visualize the implementation of the method in real conditions.

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Cited By

Villalobos JSoto-Silva WGonzález-Araya MGonzález–Ramirez R(2019)Research directions in technology development to support real-time decisions of fresh produce logisticsComputers and Electronics in Agriculture10.1016/j.compag.2019.105092167:COnline publication date: 1-Dec-2019
https://dl.acm.org/doi/10.1016/j.compag.2019.105092
Celorrio RMartínez ESaenz-Díez JJiménez EBlanco J(2015)Methodology to decrease the energy demands in wine production using cold pre-fermentationComputers and Electronics in Agriculture10.1016/j.compag.2015.08.009117:C(177-185)Online publication date: 1-Sep-2015
https://dl.acm.org/doi/10.1016/j.compag.2015.08.009

A method to estimate Grape Phenolic Maturity based on seed images
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
  2. Machine learning

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Published In

cover image Computers and Electronics in Agriculture

Computers and Electronics in Agriculture Volume 101, Issue

February, 2014

164 pages

ISSN:0168-1699

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Copyright © Elsevier B.V. © 2013.

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 February 2014

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Cited By

Villalobos JSoto-Silva WGonzález-Araya MGonzález–Ramirez R(2019)Research directions in technology development to support real-time decisions of fresh produce logisticsComputers and Electronics in Agriculture10.1016/j.compag.2019.105092167:COnline publication date: 1-Dec-2019
https://dl.acm.org/doi/10.1016/j.compag.2019.105092
Celorrio RMartínez ESaenz-Díez JJiménez EBlanco J(2015)Methodology to decrease the energy demands in wine production using cold pre-fermentationComputers and Electronics in Agriculture10.1016/j.compag.2015.08.009117:C(177-185)Online publication date: 1-Sep-2015
https://dl.acm.org/doi/10.1016/j.compag.2015.08.009

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