Plant leaf disease classification using EfficientNet deep learning model
Yazarlar (4)
Doç. Dr. Ümit Atila Karabük Üniversitesi, Türkiye
Murat Uçar İskenderun Teknik Üniversitesi, Türkiye
Prof. Dr. Kemal AKYOL Kastamonu Üniversitesi, Türkiye
Emine Uçar İskenderun Teknik Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Ecological Informatics (Q2) | ||
| Dergi ISSN | 1574-9541 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 03-2021 |
| Kabul Tarihi | – | Yayınlanma Tarihi | 01-03-2021 |
| Cilt / Sayı / Sayfa | 61 / 1 / 1–13 | DOI | 10.1016/j.ecoinf.2020.101182 |
| Makale Linki | https://linkinghub.elsevier.com/retrieve/pii/S1574954120301321 | ||
| Özet |
| Most plant diseases show visible symptoms, and the technique which is accepted today is that an experienced plant pathologist diagnoses the disease through optical observation of infected plant leaves. The fact that the disease diagnosis process is slow to perform manually and another fact that the success of the diagnosis is proportional to the pathologist's capabilities makes this problem an excellent application area for computer-aided diagnostic systems. Instead of classical machine learning methods, in which manual feature extraction should be flawless to achieve successful results, there is a need for a model that does not need pre-processing and can perform a successful classification. In this study, EfficientNet deep learning architecture was proposed in plant leaf disease classification and the performance of this model was compared with other state-of-the-art deep learning models. The PlantVillage … |
| Anahtar Kelimeler |
| Deep learning | Leaf image | Plant disease | Transfer learning |
Science Direct
Plant leaf disease classification using EfficientNet deep learning model
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 860 |
| Google Scholar | 1127 |