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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.


Olives coming from different locations and different image analyses. The first row shows the original image of the samples (RGB); the second row presents the result after applying the textural filter (I), and the third and fourth rows show the same samples where a brownish color has been detected (Crg and Crb, respectively).
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f4-sensors-15-15738: Olives coming from different locations and different image analyses. The first row shows the original image of the samples (RGB); the second row presents the result after applying the textural filter (I), and the third and fourth rows show the same samples where a brownish color has been detected (Crg and Crb, respectively).

Mentions: Two image processing methods were applied: gradient image and differences in the RGB channels [29]. The first one was focused on the detection of sudden changes in the intensity profile of images. This fact is related to detecting olives with wrinkled skin. We can see the results of applying this filter in the second row, as shown in Figure 4, where abrupt changes in the olive skin have been contrasted. The darker they are, the more wrinkled they are. By employing this filter, the differences between tree and soil samples are more obvious.


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)

Olives coming from different locations and different image analyses. The first row shows the original image of the samples (RGB); the second row presents the result after applying the textural filter (I), and the third and fourth rows show the same samples where a brownish color has been detected (Crg and Crb, respectively).
© Copyright Policy
Related In: Results  -  Collection

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

f4-sensors-15-15738: Olives coming from different locations and different image analyses. The first row shows the original image of the samples (RGB); the second row presents the result after applying the textural filter (I), and the third and fourth rows show the same samples where a brownish color has been detected (Crg and Crb, respectively).
Mentions: Two image processing methods were applied: gradient image and differences in the RGB channels [29]. The first one was focused on the detection of sudden changes in the intensity profile of images. This fact is related to detecting olives with wrinkled skin. We can see the results of applying this filter in the second row, as shown in Figure 4, where abrupt changes in the olive skin have been contrasted. The darker they are, the more wrinkled they are. By employing this filter, the differences between tree and soil samples are more obvious.

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.