| dc.creator |
Kalkan, Habil |
|
| dc.creator |
Gunes, Ali |
|
| dc.creator |
Durmus, Efkan |
|
| dc.date |
2013-01-01T01:00:00Z |
|
| dc.date.accessioned |
2021-12-03T11:15:53Z |
|
| dc.date.available |
2021-12-03T11:15:53Z |
|
| dc.identifier |
1b9e206f-c053-4e71-bf46-bc6d714d08b9 |
|
| dc.identifier |
10.1109/siu.2013.6531575 |
|
| dc.identifier |
https://avesis.sdu.edu.tr/publication/details/1b9e206f-c053-4e71-bf46-bc6d714d08b9/oai |
|
| dc.identifier.uri |
http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/90252 |
|
| dc.description |
Turkey produces major of the figs in world wide. Figs like other agricultural products (e. g hazelnuts, pistachio nuts, corn, etc.) may include cancerogenic aflatoxin. A majority of these aflatoxin contaminated figs may expose bright greenish-yellow fluorescence (BGYF) under UV illumination. These BGYF figs are manually detected and eliminated by workers in dark rooms. However, manual selection is tedious, subjective and the working condition threatens the worker's healthy. In this study, a machine vision based non-destructive method is proposed for detecting the BGYF figs under UV illumination. Using the proposed methods, the BGYF and non-BGYF figs are classified with 0.93 area under curve value. |
|
| dc.language |
tur |
|
| dc.rights |
info:eu-repo/semantics/closedAccess |
|
| dc.title |
Detection of High Aflatoxin Risk Figs with Computer Vision |
|
| dc.type |
info:eu-repo/semantics/conferenceObject |
|