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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.


This figure shows the normalized image histograms after applying the textural filter (a), channel R minus channel G operation (b) and channel R minus channel B operation (c).
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f7-sensors-15-15738: This figure shows the normalized image histograms after applying the textural filter (a), channel R minus channel G operation (b) and channel R minus channel B operation (c).

Mentions: The histograms of the processed images for the whole dataset can be seen in Figure 7a–c. The average values for each component and their standard deviation appear in this figures. The difference between two olive classes is more evident in the textural image histograms (Figure 7a) than in the other ones. This fact proves that the wrinkling of the olive surface is an important factor to arrange classes. However, in the other histograms (Figure 7b,c), several components appear that could be interesting in order to build the feature vector.


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)

This figure shows the normalized image histograms after applying the textural filter (a), channel R minus channel G operation (b) and channel R minus channel B operation (c).
© Copyright Policy
Related In: Results  -  Collection

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

f7-sensors-15-15738: This figure shows the normalized image histograms after applying the textural filter (a), channel R minus channel G operation (b) and channel R minus channel B operation (c).
Mentions: The histograms of the processed images for the whole dataset can be seen in Figure 7a–c. The average values for each component and their standard deviation appear in this figures. The difference between two olive classes is more evident in the textural image histograms (Figure 7a) than in the other ones. This fact proves that the wrinkling of the olive surface is an important factor to arrange classes. However, in the other histograms (Figure 7b,c), several components appear that could be interesting in order to build the feature vector.

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.