Automatic classification of oranges using image processing and data mining techniques

Data mining is the discovery of patterns and regularities from large amounts of data using machine learning algorithms. This can be applied to object recognition using image processing techniques. In fruits and vegetables production lines, the quality assurance is done by trained people who inspect...

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Detalles Bibliográficos
Autores principales: Mercol, Juan Pablo, Gambini, María Juliana, Santos, Juan Miguel
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2008
Materias:
Ciencias Informáticas
Image processing software
Data mining
Neural nets
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/21692
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-21692
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Image processing software
Data mining
Neural nets
spellingShingle Ciencias Informáticas
Image processing software
Data mining
Neural nets
Mercol, Juan Pablo
Gambini, María Juliana
Santos, Juan Miguel
Automatic classification of oranges using image processing and data mining techniques
topic_facet Ciencias Informáticas
Image processing software
Data mining
Neural nets
description Data mining is the discovery of patterns and regularities from large amounts of data using machine learning algorithms. This can be applied to object recognition using image processing techniques. In fruits and vegetables production lines, the quality assurance is done by trained people who inspect the fruits while they move in a conveyor belt, and classify them in several categories based on visual features. In this paper we present an automatic orange’s classification system, which uses visual inspection to extract features from images captured with a digital camera. With these features train several data mining algorithms which should classify the fruits in one of the three pre-established categories. The data mining algorithms used are five different decision trees (J48, Classification and Regression Tree (CART), Best First Tree, Logistic Model Tree (LMT) and Random For- est), three artificial neural networks (Multilayer Perceptron with Backpropagation, Radial Basis Function Network (RBF Network), Sequential Minimal Optimization for Support Vector Machine (SMO)) and a classification rule (1Rule). The obtained results are encouraging because of the good accuracy achieved by the clas- sifiers and the low computational costs.
format Objeto de conferencia
Objeto de conferencia
author Mercol, Juan Pablo
Gambini, María Juliana
Santos, Juan Miguel
author_facet Mercol, Juan Pablo
Gambini, María Juliana
Santos, Juan Miguel
author_sort Mercol, Juan Pablo
title Automatic classification of oranges using image processing and data mining techniques
title_short Automatic classification of oranges using image processing and data mining techniques
title_full Automatic classification of oranges using image processing and data mining techniques
title_fullStr Automatic classification of oranges using image processing and data mining techniques
title_full_unstemmed Automatic classification of oranges using image processing and data mining techniques
title_sort automatic classification of oranges using image processing and data mining techniques
publishDate 2008
url http://sedici.unlp.edu.ar/handle/10915/21692
work_keys_str_mv AT mercoljuanpablo automaticclassificationoforangesusingimageprocessinganddataminingtechniques
AT gambinimariajuliana automaticclassificationoforangesusingimageprocessinganddataminingtechniques
AT santosjuanmiguel automaticclassificationoforangesusingimageprocessinganddataminingtechniques
bdutipo_str Repositorios
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