Expert system based on computer vision to estimate the content of impurities in olive oil samples

Marchal, Pablo Cano; Gila, Diego Manuel Martínez; García, Javier Gámez; Ortega, Juan Gómez

doi:10.1016/j.jfoodeng.2013.05.032

Cited by 50 publications

(21 citation statements)

References 24 publications

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“…Afterwards, principal component analysis (PCA) and hierarchical cluster analysis (HCA) were selected to recognize eight different citrus fruits. Cano Marchal et al (2013) [8] created an expert system based on machine learning and computer vision, with the aim of estimating the content of impurities in a particular olive oil sample. Breijo et al (2013) [9] used an odor sampling system (electronic nose) to classify the aroma of Diospyros kaki, whose working parameters can possess variable configurations making the system flexible.…”

Section: State-of-the-artmentioning

confidence: 99%

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Fruit Classification by Wavelet-Entropy and Feedforward Neural Network Trained by Fitness-Scaled Chaotic ABC and Biogeography-Based Optimization

Wang

Zhang

et al. 2015

Entropy

146

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Abstract:Fruit classification is quite difficult because of the various categories and similar shapes and features of fruit. In this work, we proposed two novel machine-learning based classification methods. The developed system consists of wavelet entropy (WE), principal component analysis (PCA), feedforward neural network (FNN) trained by fitness-scaled chaotic artificial bee colony (FSCABC) and biogeography-based optimization (BBO), respectively. The K-fold stratified cross validation (SCV) was utilized for statistical analysis. The classification performance for 1653 fruit images from 18 categories showed that the proposed "WE + PCA + FSCABC-FNN" and "WE + PCA + BBO-FNN" methods achieve the same accuracy of 89.5%, higher than state-of-the-art approaches: "(CH + MP + US) + PCA + GA-FNN " of 84.8%, "(CH + MP + US) + PCA + PSO-FNN" of 87.9%, "(CH + MP + US) + PCA + ABC-FNN" of 85.4%, "(CH + MP + US) + PCA + kSVM" of 88.2%, and "(CH + MP + US) + PCA + FSCABC-FNN" of 89.1%. Besides, our methods used only 12 features, less than the number of features used by other methods. Therefore, the proposed methods are effective for fruit classification. OPEN ACCESSEntropy 2015, 17 5712

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Section: State-of-the-artmentioning

confidence: 99%

“…Some external quality descriptors, such as color, texture, size, and shape, are commonly used in their studies [3][4][5][6][7][8][9][10][11][12][13][14][15]. However, the developed classifiers either were limited to a specific category, or made predictions with considerable misclassifications.…”

Section: Introductionmentioning

confidence: 99%

Fruit Classification by Wavelet-Entropy and Feedforward Neural Network Trained by Fitness-Scaled Chaotic ABC and Biogeography-Based Optimization

Wang

Zhang

et al. 2015

Entropy

146

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“…Both, Support Vector Machines (SVMs) and Neural Networks (NNs) were selected for this work as they have been successfully applied to wide range of problems in previous studies (Cano Marchal et al, 2013;Donis-González et al, 2013).…”

Section: Design Of the Classifiermentioning

confidence: 99%

“…In quality assessment of foodstuffs, several pattern recognition techniques have been used for quality evaluation. For example, Cano Marchal et al (2013) used Support Vector Machines and Neural Networks for the evaluation of olive oil impurities in virgin oil using CIA. However, no studies have been found in the literature available which applies CIA for the classification of dry-cured ham slices based on marbling.…”

Section: Introductionmentioning

confidence: 99%

Computer image analysis as a tool for classifying marbling: A case study in dry-cured ham

Muñoz

Rubio-Celorio

García-Gil

et al. 2015

Journal of Food Engineering

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“…Afterwards, PCA and hierarchical cluster analysis (HCA) were employed to classify eight different citrus fruits. Cano Marchal (2013) [6] established an expert system on the strength of computer vision to estimate the content of impurities in olive oil samples. Breijo (2013) [7] used an odor sampling system (electronic nose) for classification of the aroma of Diospyros kaki.…”

Section: Introductionmentioning

confidence: 99%

A Fruit Sensing and Classification System by Fractional Fourier Entropy and Improved Hybrid Genetic Algorithm

Lu²,

Wang³

et al. 2017

The Proceedings of the 5th International Conference on Industrial Application Engineering 2017

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It remains a challenge to classify different categories of fruits because of the similarities of shape, color, and texture among them. We presented a novel approach in order to classify fruits accurately and efficiently based on computer vision techniques. We obtained the coefficients using fractional Fourier transform. The entropies extracted from the coefficients were fed into the classifier as the features. A multilayer perceptron optimized by an improved hybrid genetic algorithm was used as the classifier. The experiment results on 1653 fruit images demonstrated that the proposed method achieved an overall accuracy of 89.59%, which was superior to the state-of-art approaches. Our method is efficientive in identifying fruit categories.

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Expert system based on computer vision to estimate the content of impurities in olive oil samples

Cited by 50 publications

References 24 publications

Fruit Classification by Wavelet-Entropy and Feedforward Neural Network Trained by Fitness-Scaled Chaotic ABC and Biogeography-Based Optimization

Fruit Classification by Wavelet-Entropy and Feedforward Neural Network Trained by Fitness-Scaled Chaotic ABC and Biogeography-Based Optimization

Computer image analysis as a tool for classifying marbling: A case study in dry-cured ham

A Fruit Sensing and Classification System by Fractional Fourier Entropy and Improved Hybrid Genetic Algorithm

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