Automatic detection of grape varieties with the newly proposed CNN model using ampelographic characteristics
Yazarlar (3)
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Ankara Üniversitesi, Türkiye
Prof. Dr. Adem YAĞCI
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | Scientia Horticulturae |
| Dergi ISSN | 0304-4238 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q1 |
| Makale Dili | Türkçe |
| Basım Tarihi | 08-2024 |
| Cilt No | 334 |
| Sayı | 1 |
| DOI Numarası | 10.1016/j.scienta.2024.113340 |
| Makale Linki | http://dx.doi.org/10.1016/j.scienta.2024.113340 |
| Özet |
| In this study, leaves, bunches and fruits of fifty grape varieties were classified using deep learning techniques using ampelographic features. A new and unique CNN model has been proposed for the classification process. In addition, GoogleNet and AlexNet models adapted to the data set with the transfer learning method were also used. The dataset was divided into two groups: leaf and cluster/fruit. A total of 27,320 images of 227 × 227 × 3 size, including 9854 leaves, 8745 bunches and 8721 fruits, were used in the data set. The dataset was randomly divided 80 % (21,856 images) for training and 20 % (5464 images) for testing. Grape varieties were classified in a total of nine different categories: five different categories in the leaf group and four different categories in the cluster/fruit group. Each class in the categories represents a grape variety. Each category of the leaf group consists of ten classes, and each category of the cluster/fruit group consists of eleven classes. The results were obtained by calculating the Accuracy, Sensitivity, Recall and F1 Score values of the categories separately for the three models. In the newly developed CNN model, the highest accuracies were determined as 94.10 % in the leaf group and 97.20 % in the cluster/fruit group in Category 4. The accuracies of GoogleNet and AlexNet models were determined as 84.39 % and 92.31 %, respectively. According to the experimental results obtained, it was determined that the proposed model showed successful performance in the classification of grape varieties. Thus, it was demonstrated that deep learning models can be used successfully in the automatic classification of ampelographic characteristics of grape varieties. |
| Anahtar Kelimeler |
| AlexNet | Ampelography | CNN | Deep learning | Googlenet | Grape |
Science Direct
Automatic detection of grape varieties with the newly proposed CNN model using ampelographic characteristics
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| WoS | 6 |
| SCOPUS | 10 |
| Google Scholar | 11 |