| dc.creator |
ÜNLÜTÜRK, MEHMET SÜLEYMAN |
|
| dc.creator |
KUŞÇU, Alper |
|
| dc.creator |
PAZIR, FİKRET |
|
| dc.creator |
ÜNLÜTÜRK, SEVCAN |
|
| dc.date |
2014-03-31T21:00:00Z |
|
| dc.date.accessioned |
2020-10-06T10:49:57Z |
|
| dc.date.available |
2020-10-06T10:49:57Z |
|
| dc.identifier |
9ae27b51-3ee7-4d60-8453-694953334e5b |
|
| dc.identifier |
10.1007/s00521-013-1346-6 |
|
| dc.identifier |
https://avesis.sdu.edu.tr/publication/details/9ae27b51-3ee7-4d60-8453-694953334e5b/oai |
|
| dc.identifier.uri |
http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/67319 |
|
| dc.description |
This study utilized a unique neural network model for texture image analysis to differentiate the crystallograms from pairs of fresh red pepper fruits from conventional and organic farms. The differences in visually analyzed samples are defined as the distribution of crystals on the circular glass underlay, the thin or thick structure of crystal needles, the angles between branches and side needles, etc. However, the visual description and definition of bio-crystallogram images has major disadvantages. A novel methodology called an image neural network (INN) has been developed to overcome these shortcomings. The 1,488 x 2,240 pixel bio-crystallogram images were acquired in a lab and cropped to 425 x 1,025 pixel images. These depicted either a conventional sweet red pepper or an organic sweet red pepper. A set of 19 images was utilized to train the image neural network. A new set of 4 images was then prepared to test the INN performance. Overall, the INN achieved an average recognition performance of 100 %. This high level of recognition suggests that the INN is a promising method for the discrimination of bio-crystallogram images. In addition, Hinton diagrams were utilized to display the optimality of the INN weights. |
|
| dc.language |
eng |
|
| dc.rights |
info:eu-repo/semantics/openAccess |
|
| dc.title |
Discrimination of bio-crystallogram images using neural networks |
|
| dc.type |
info:eu-repo/semantics/article |
|