Enhanced Lemon Leaf Disease Diagnosis via Gray Wolf Optimization and Machine Learning Techniques
Yazarlar (2)
Dr. Öğr. Üyesi Yavuz ÜNAL Sinop Üniversitesi, Türkiye
Yonis Gulzar
King Faisal University, Suudi Arabistan
King Faisal University, Suudi Arabistan
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Applied Fruit Science (Q2) | ||
| Dergi ISSN | 2948-2623 Dergi Bilgileri (2026) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Türkçe | Basım Tarihi | 04-2026 |
| Kabul Tarihi | 18-03-2026 | Yayınlanma Tarihi | 20-04-2026 |
| Cilt / Sayı / Sayfa | 68 / 3 / – | DOI | 10.1007/s10341-026-01830-3 |
| Makale Linki | https://doi.org/10.1007/s10341-026-01830-3 | ||
| UAK Araştırma Alanları |
Görüntü İşleme
Makine Öğrenmesi
Yapay Zeka
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| Özet |
| This study proposes a practical and efficient framework for lemon leaf disease classification by combining deep feature extraction with optimization-based feature selection and machine learning classifiers. Instead of relying solely on end-to-end deep learning, the work focuses on improving feature quality and reducing redundancy. DenseNet121 is used to extract 1,024 deep features from a nine-class lemon leaf disease dataset, capturing relevant visual patterns from the images. These features are then refined using Gray Wolf Optimization (GWO), a wrapper-based feature selection method that reduces the feature space to 405 features, achieving a dimensionality reduction of 60.45%. To evaluate the effectiveness of the selected features, both Random Forest and Support Vector Machine (SVM) classifiers are applied to the original and optimized feature sets. The results show that Random Forest achieves 96.97 … |
| Anahtar Kelimeler |
| Deep learning in agriculture | DenseNet121 | Feature selection optimization | Plant disease detection | Precision agriculture |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 2 |
| Scopus | 2 |
| Google Scholar | 2 |