Identification of Leaf Diseases from Figs Using Deep Learning Methods
Yazarlar (8)
Yilmaz Karatas
Talha Alperen Cengel
Selçuk Üniversitesi, Türkiye
Selçuk Üniversitesi, Türkiye
Bunyamin Gencturk
Selçuk Üniversitesi, Türkiye
Selçuk Üniversitesi, Türkiye
Muslume Beyza Yildiz
Selçuk Üniversitesi, Türkiye
Selçuk Üniversitesi, Türkiye
Doç. Dr. Yavuz Selim Taspinar Selçuk Üniversitesi, Türkiye
Doç. Dr. Osman Ozbek Selçuk Üniversitesi, Türkiye
Murat Koklu Selçuk Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(Diğer hakemli uluslarası dergilerde yayınlanan tam makale)
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| Dergi Adı | Selcuk Journal of Agriculture and Food Sciences | ||
| Dergi ISSN | 2458-8377 | ||
| Dergi Tarandığı Indeksler | TR DİZİN | ||
| Makale Dili | İngilizce | Basım Tarihi | 01-2024 |
| Kabul Tarihi | 16-12-2024 | Yayınlanma Tarihi | 16-12-2024 |
| Cilt / Sayı / Sayfa | 38 / 3 / 414–426 | DOI | 10.15316/SJAFS.2024.037 |
| Makale Linki | https://dergipark.org.tr/tr/pub/selcukjafsci/issue/88545/1507472 | ||
| UAK Araştırma Alanları |
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| Özet |
| Early detection of plant diseases is of great importance for agricultural production and plant health. Early detection is important to prevent the spread of diseases and reduce agricultural losses. The aim of this study is to use artificial intelligence technologies for the early detection of diseased fig plants and reduce agricultural losses. The fig leaf dataset used in the study has two classes: healthy and diseased leaves. There are a total of 2321 images in the dataset. Among these images, there are 1350 images representing diseased leaves and 971 images representing healthy leaves. The dataset is divided into 80% training data and 20% test data. DarkNet-19, ResNet50, VGG-19, VGG-16, ShuffleNet, GoogLeNet, MobileNet-v2, EfficientNet-b0, and DarkNet-53 algorithms were used to analyze the fig leaves dataset using a MATLAB graphical user interface (GUI). The classification accuracy values of each algorithm are as follows: DarkNet-19 90.3%, ResNet50 90.95%, VGG-19 93.32%, VGG-16 92.89%, ShuffleNet 89.44%, GoogLeNet 87.5%, MobileNet-v2 87.5%, EfficientNet-b0 85.56%, and DarkNet53 91.59%. These results evaluate the usability and performance of different algorithms for the early detection of plant diseases. The research emphasizes the importance of the effective use of artificial intelligence technologies in the agricultural industry. |
| Anahtar Kelimeler |
| Data Analysis | Deep Learning Methods | Disease Detection | Image Classification | Fig Leaves Diseases |
| Atıf Sayıları | |
| Google Scholar | 3 |