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Sorting Olive Batches for the Milling Process Using Image Processing.

Aguilera Puerto D, Martínez Gila DM, Gámez García J, Gómez Ortega J - Sensors (Basel) (2015)

Bottom Line: The feature vector of the samples has been obtained on the basis of the olive image histograms.Moreover, different image preprocessing has been employed, and two classification techniques have been used: these are discriminant analysis and neural networks.The proposed methodology has been validated successfully, obtaining good classification results.

View Article: PubMed Central - PubMed

Affiliation: ANDALTEC, Plastic Technological Center, Martos, Jaén 23600, Spain. aguilera@andaltec.org.

ABSTRACT
The quality of virgin olive oil obtained in the milling process is directly bound to the characteristics of the olives. Hence, the correct classification of the different incoming olive batches is crucial to reach the maximum quality of the oil. The aim of this work is to provide an automatic inspection system, based on computer vision, and to classify automatically different batches of olives entering the milling process. The classification is based on the differentiation between ground and tree olives. For this purpose, three different species have been studied (Picudo, Picual and Hojiblanco). The samples have been obtained by picking the olives directly from the tree or from the ground. The feature vector of the samples has been obtained on the basis of the olive image histograms. Moreover, different image preprocessing has been employed, and two classification techniques have been used: these are discriminant analysis and neural networks. The proposed methodology has been validated successfully, obtaining good classification results.

No MeSH data available.


All of the dataset is projected in the new 3D space, which is created by three principal components, PC1, PC2 and PC3.
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f10-sensors-15-15738: All of the dataset is projected in the new 3D space, which is created by three principal components, PC1, PC2 and PC3.

Mentions: The next step was to eliminate co-linearity and to reduce the amount of components in the feature vector. The clustering among classes is evidenced from the results obtained by PCA applied to the matrix of the entire set of scaled histogram data. Figure 10 shows the samples projected in the three firsts components. In this case, the explained variance was about 75%.


Sorting Olive Batches for the Milling Process Using Image Processing.

Aguilera Puerto D, Martínez Gila DM, Gámez García J, Gómez Ortega J - Sensors (Basel) (2015)

All of the dataset is projected in the new 3D space, which is created by three principal components, PC1, PC2 and PC3.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541852&req=5

f10-sensors-15-15738: All of the dataset is projected in the new 3D space, which is created by three principal components, PC1, PC2 and PC3.
Mentions: The next step was to eliminate co-linearity and to reduce the amount of components in the feature vector. The clustering among classes is evidenced from the results obtained by PCA applied to the matrix of the entire set of scaled histogram data. Figure 10 shows the samples projected in the three firsts components. In this case, the explained variance was about 75%.

Bottom Line: The feature vector of the samples has been obtained on the basis of the olive image histograms.Moreover, different image preprocessing has been employed, and two classification techniques have been used: these are discriminant analysis and neural networks.The proposed methodology has been validated successfully, obtaining good classification results.

View Article: PubMed Central - PubMed

Affiliation: ANDALTEC, Plastic Technological Center, Martos, Jaén 23600, Spain. aguilera@andaltec.org.

ABSTRACT
The quality of virgin olive oil obtained in the milling process is directly bound to the characteristics of the olives. Hence, the correct classification of the different incoming olive batches is crucial to reach the maximum quality of the oil. The aim of this work is to provide an automatic inspection system, based on computer vision, and to classify automatically different batches of olives entering the milling process. The classification is based on the differentiation between ground and tree olives. For this purpose, three different species have been studied (Picudo, Picual and Hojiblanco). The samples have been obtained by picking the olives directly from the tree or from the ground. The feature vector of the samples has been obtained on the basis of the olive image histograms. Moreover, different image preprocessing has been employed, and two classification techniques have been used: these are discriminant analysis and neural networks. The proposed methodology has been validated successfully, obtaining good classification results.

No MeSH data available.