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- Title
e-ksper: A Convolutional Neural Network Based System for Seedless Raisin Quality Grading.
- Authors
Gülsoylu, Emre; Yildiz, Zeynep Cipiloglu
- Abstract
Seedless raisins are graded according to their quality which is determined based on several features such as color, size, texture, and humidity. Currently, most of the raisin grading process is performed by human experts manually, which is laborious and subjective work. Therefore, an automated system that enables objective evaluation of the raisins would be helpful for both producers and experts during this process. In this study, we propose a simple machinery prototype that takes images of raisins under standard background and illumination conditions and an automated system that performs quality grading of raisins using convolutional neural networks. The proposed model not only targets color classes but also aims to identify foreign matters and defected raisins. The model achieves about 88.2% average classification accuracy on five classes including four color classes and a defected raisin class; whereas the model's accuracy becomes 98.6% if defected raisins are excluded. Hence, the proposed model is very successful in differentiating color classes and has also considerable success in detecting foreign matters and defected raisins. We provide a comprehensive analysis and discussion on these results.
- Subjects
CONVOLUTIONAL neural networks; RAISINS; AUTOMATION; HUMIDITY; EVALUATION
- Publication
Gazi Journal of Engineering Sciences (GJES) / Gazi Mühendislik Bilimleri Dergisi, 2023, Vol 9, Issue 3, p453
- ISSN
2149-4916
- Publication type
Article
- DOI
10.30855/gmbd.0705079