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Olive-Fruit Mass and Size Estimation Using Image Analysis and Feature Modeling
This paper presents a new methodology for the estimation of olive-fruit mass and size, characterized by its major and minor axis length, by using image analysis techniques. First, different sets of olives from the varieties Picual and Arbequina were photographed in the laboratory. An original algori...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6163441/ https://www.ncbi.nlm.nih.gov/pubmed/30177667 http://dx.doi.org/10.3390/s18092930 |
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author | Ponce, Juan Manuel Aquino, Arturo Millán, Borja Andújar, José Manuel |
author_facet | Ponce, Juan Manuel Aquino, Arturo Millán, Borja Andújar, José Manuel |
author_sort | Ponce, Juan Manuel |
collection | PubMed |
description | This paper presents a new methodology for the estimation of olive-fruit mass and size, characterized by its major and minor axis length, by using image analysis techniques. First, different sets of olives from the varieties Picual and Arbequina were photographed in the laboratory. An original algorithm based on mathematical morphology and statistical thresholding was developed for segmenting the acquired images. The estimation models for the three targeted features, specifically for each variety, were established by linearly correlating the information extracted from the segmentations to objective reference measurement. The performance of the models was evaluated on external validation sets, giving relative errors of 0.86% for the major axis, 0.09% for the minor axis and 0.78% for mass in the case of the Arbequina variety; analogously, relative errors of 0.03%, 0.29% and 2.39% were annotated for Picual. Additionally, global feature estimation models, applicable to both varieties, were also tried, providing comparable or even better performance than the variety-specific ones. Attending to the achieved accuracy, it can be concluded that the proposed method represents a first step in the development of a low-cost, automated and non-invasive system for olive-fruit characterization in industrial processing chains. |
format | Online Article Text |
id | pubmed-6163441 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-61634412018-10-10 Olive-Fruit Mass and Size Estimation Using Image Analysis and Feature Modeling Ponce, Juan Manuel Aquino, Arturo Millán, Borja Andújar, José Manuel Sensors (Basel) Article This paper presents a new methodology for the estimation of olive-fruit mass and size, characterized by its major and minor axis length, by using image analysis techniques. First, different sets of olives from the varieties Picual and Arbequina were photographed in the laboratory. An original algorithm based on mathematical morphology and statistical thresholding was developed for segmenting the acquired images. The estimation models for the three targeted features, specifically for each variety, were established by linearly correlating the information extracted from the segmentations to objective reference measurement. The performance of the models was evaluated on external validation sets, giving relative errors of 0.86% for the major axis, 0.09% for the minor axis and 0.78% for mass in the case of the Arbequina variety; analogously, relative errors of 0.03%, 0.29% and 2.39% were annotated for Picual. Additionally, global feature estimation models, applicable to both varieties, were also tried, providing comparable or even better performance than the variety-specific ones. Attending to the achieved accuracy, it can be concluded that the proposed method represents a first step in the development of a low-cost, automated and non-invasive system for olive-fruit characterization in industrial processing chains. MDPI 2018-09-03 /pmc/articles/PMC6163441/ /pubmed/30177667 http://dx.doi.org/10.3390/s18092930 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ponce, Juan Manuel Aquino, Arturo Millán, Borja Andújar, José Manuel Olive-Fruit Mass and Size Estimation Using Image Analysis and Feature Modeling |
title | Olive-Fruit Mass and Size Estimation Using Image Analysis and Feature Modeling |
title_full | Olive-Fruit Mass and Size Estimation Using Image Analysis and Feature Modeling |
title_fullStr | Olive-Fruit Mass and Size Estimation Using Image Analysis and Feature Modeling |
title_full_unstemmed | Olive-Fruit Mass and Size Estimation Using Image Analysis and Feature Modeling |
title_short | Olive-Fruit Mass and Size Estimation Using Image Analysis and Feature Modeling |
title_sort | olive-fruit mass and size estimation using image analysis and feature modeling |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6163441/ https://www.ncbi.nlm.nih.gov/pubmed/30177667 http://dx.doi.org/10.3390/s18092930 |
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