Limits...
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.


Difference in brightness with different voltages.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4541852&req=5

f2-sensors-15-15738: Difference in brightness with different voltages.

Mentions: We tried to solve the problem in different ways. Firstly, we tested different light sources, two LED rings and a multipoint square light source. We detected that the rings were more suitable for this application. The smaller LED rings 17 cm in diameter were chosen. After this, the light intensity was tested. As a result, the DC power supply was connected to the illumination system in order to change the voltage and the current. That is why the intensity of the light can be modified. Then, the supplied voltage to the illumination system was adjusted in order to avoid the saturation of the pixels over the green olives, because these ones reflect more light than the rest of the olive colors. We supplied a constant voltage of 15.9 V and 0.04 A to the LED ring. This voltage is lower than the nominal voltage of 24 V. Figure 2 shows 4 pictures of the same olives with different light intensities.


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)

Difference in brightness with different voltages.
© Copyright Policy
Related In: Results  -  Collection

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

f2-sensors-15-15738: Difference in brightness with different voltages.
Mentions: We tried to solve the problem in different ways. Firstly, we tested different light sources, two LED rings and a multipoint square light source. We detected that the rings were more suitable for this application. The smaller LED rings 17 cm in diameter were chosen. After this, the light intensity was tested. As a result, the DC power supply was connected to the illumination system in order to change the voltage and the current. That is why the intensity of the light can be modified. Then, the supplied voltage to the illumination system was adjusted in order to avoid the saturation of the pixels over the green olives, because these ones reflect more light than the rest of the olive colors. We supplied a constant voltage of 15.9 V and 0.04 A to the LED ring. This voltage is lower than the nominal voltage of 24 V. Figure 2 shows 4 pictures of the same olives with different light intensities.

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.