Deep Learning Based Egg Fertility Detection

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dc.authoridCEVIK, Kerim Kursat/0000-0002-2921-506X
dc.authoridBOGA, MUSTAFA/0000-0001-8277-9262
dc.authoridKocer, Hasan Erdinc/0000-0002-0799-2140
dc.contributor.authorCevik, Kerim Kursat
dc.contributor.authorKocer, Hasan Erdinc
dc.contributor.authorBoga, Mustafa
dc.date.accessioned2024-11-07T13:32:48Z
dc.date.available2024-11-07T13:32:48Z
dc.date.issued2022
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractSimple Summary This study employs a Mask R-CNN technique along with the transfer learning model to accurately detect fertile and infertile eggs. It is a novel study that uses a single DL model to carry out detection, classification and segmentation of fertile and infertile eggs based on incubator images. This study investigates the implementation of deep learning (DL) approaches to the fertile egg-recognition problem, based on incubator images. In this study, we aimed to classify chicken eggs according to both segmentation and fertility status with a Mask R-CNN-based approach. In this manner, images can be handled by a single DL model to successfully perform detection, classification and segmentation of fertile and infertile eggs. Two different test processes were used in this study. In the first test application, a data set containing five fertile eggs was used. In the second, testing was carried out on the data set containing 18 fertile eggs. For evaluating this study, we used AP, one of the most important metrics for evaluating object detection and segmentation models in computer vision. When the results obtained were examined, the optimum threshold value (IoU) value was determined as 0.7. According to the IoU of 0.7, it was observed that all fertile eggs in the incubator were determined correctly on the third day of both test periods. Considering the methods used and the ease of the designed system, it can be said that a very successful system has been designed according to the studies in the literature. In order to increase the segmentation performance, it is necessary to carry out an experimental study to improve the camera and lighting setup prepared for taking the images.
dc.description.sponsorshipAdministration of Scientific Research Projects of Akdeniz University [FBA-2019-4898]
dc.description.sponsorshipThis research was funded by Administration of Scientific Research Projects of Akdeniz University, grant number FBA-2019-4898 and The APC was not funded. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
dc.identifier.doi10.3390/vetsci9100574
dc.identifier.issn2306-7381
dc.identifier.issue10
dc.identifier.pmid36288187
dc.identifier.scopus2-s2.0-85140603078
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/vetsci9100574
dc.identifier.urihttps://hdl.handle.net/11480/15622
dc.identifier.volume9
dc.identifier.wosWOS:000873854800001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofVeterinary Sciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241106
dc.subjectdeep learning
dc.subjectegg fertility
dc.subjectMask R-CNN
dc.subjectincubator images
dc.titleDeep Learning Based Egg Fertility Detection
dc.typeArticle

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