Classification of Hazelnuts with CNN based Deep Learning System
| dc.contributor.author | Gunes, Engin | |
| dc.contributor.author | Ulku, Eyup Emre | |
| dc.contributor.author | Yildiz, Kazim | |
| dc.date.accessioned | 2023-03-18T13:01:15Z | |
| dc.date.available | 2023-03-18T13:01:15Z | |
| dc.date.issued | 2022 | en_US |
| dc.department | Başka Kurum | en_US |
| dc.description.abstract | The rapid development of technology leads to the emergence of technology-based systems in many different areas. In recent years, agriculture has been one of these areas. We come across technological systems in agricultural applications for many different purposes such as growing healthier products, increasing the yield of products, and predicting product productivity. Today, technology-based systems are used more and more widely in agricultural applications. Classification of products quickly and with high accuracy is a very important process in predicting product yield. In this study, it is suggested to use the CNN-based deep learning model VGG16 in order to classify the hazelnut fruit, which is an important agricultural product. The main purpose is to classify hazelnuts according to their quality with a deep learning approach. For that, a new data set was created. There are 15770 images in the created data set. In the study, the data set was used by dividing it into different parts. The classification of hazelnut images was carried out using the VGG16 deep learning model, which is a powerful model for classifying images. As a result of the experiments on the data set created, the classification process of hazelnuts was realized with 0,9873 F1 score. The detection rate of quality hazelnut is 0.9848, the rate of detection of kernel hazelnut is 0.9891 and the rate of detection of damaged hazelnut is 0.9882. In addition, the classification process was carried out with deep learning using 50%, 25% and 10% of the data set in the study. It was observed that the 98.73 %, 95.46 %, 92.62 %, and 88.42 % accuracy rates were achieved when the whole, 50 %, 25 %, and 10 % data sets were used, respectively. | en_US |
| dc.identifier.citation | Gunes, E., Ulku, E, E., Yildiz, K., (2022) Classification of Hazelnuts with CNN based Deep Learning System. Selcuk University Journal of Engineering Sciences, 21 (3) 111-120. | en_US |
| dc.identifier.endpage | 120 | en_US |
| dc.identifier.issn | 2757-8828 | en_US |
| dc.identifier.issue | 3 | en_US |
| dc.identifier.startpage | 111 | en_US |
| dc.identifier.uri | https://hdl.handle.net/20.500.12395/46132 | |
| dc.identifier.volume | 21 | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Selçuk Üniversitesi | en_US |
| dc.relation.ispartof | Selcuk University Journal of Engineering Sciences | en_US |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Başka Kurum Yazarı | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.selcuk | 20240510_oaig | en_US |
| dc.subject | Deep learning | en_US |
| dc.subject | Classification | en_US |
| dc.subject | Hazelnuts dataset | en_US |
| dc.subject | Hazelnuts classification | en_US |
| dc.title | Classification of Hazelnuts with CNN based Deep Learning System | en_US |
| dc.type | Article | en_US |
