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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.


At-line acquisition system setup. (a) The setup as a sketch and (b) the real setup.
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f1-sensors-15-15738: At-line acquisition system setup. (a) The setup as a sketch and (b) the real setup.

Mentions: The camera is rigidly attached, as well as the LED ring. A white plastic tray, where the olives are placed before for the acquisition, is placed under the camera on the illumination system. The distance between the camera and the olives can be varied manually. The image processing and the statistical analysis software have been programmed in MATLAB® on Windows SO. Figure 1 shows the prototype used to acquire the images of olives.


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)

At-line acquisition system setup. (a) The setup as a sketch and (b) the real setup.
© Copyright Policy
Related In: Results  -  Collection

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

f1-sensors-15-15738: At-line acquisition system setup. (a) The setup as a sketch and (b) the real setup.
Mentions: The camera is rigidly attached, as well as the LED ring. A white plastic tray, where the olives are placed before for the acquisition, is placed under the camera on the illumination system. The distance between the camera and the olives can be varied manually. The image processing and the statistical analysis software have been programmed in MATLAB® on Windows SO. Figure 1 shows the prototype used to acquire the images of olives.

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.