Machine Learning-Based Classification of Infected Date Palm Leaves Caused by Dubas Insects: A Comparative Analysis of Feature Extraction Methods and Classification Algorithms
Yazarlar (3)
Öğr. Gör. Ramazan Kursun Selçuk Üniversitesi, Türkiye
Dr. Öğr. Üyesi Elham Tahsın Yasın YASIN Selçuk Üniversitesi, Türkiye
Murat Koklu Selçuk Üniversitesi, Türkiye
| Bildiri Türü | Tebliğ/Bildiri | Bildiri Dili | İngilizce |
| Bildiri Alt Türü | Tam Metin Olarak Yayınlanan Tebliğ (Uluslararası Kongre/Sempozyum) | ||
| Bildiri Niteliği | Alanında Hakemli Uluslararası Kongre/Sempozyum | ||
| DOI Numarası | 10.1109/ASYU58738.2023.10296641 | ||
| Kongre Adı | 2023 Innovations in Intelligent Systems and Applications Conference (ASYU) | ||
| Kongre Tarihi | 11-10-2023 / 13-10-2023 | ||
| Basıldığı Ülke | Türkiye | Basıldığı Şehir | Sivas |
| Bildiri Linki | https://doi.org/10.1109/asyu58738.2023.10296641 | ||
| UAK Araştırma Alanları |
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| Özet |
| This study investigates the utilization of machine learning techniques for effectively classifying infected date palm leaves caused by Dubas insects. Three distinct feature extraction methods, namely Inceptionv3, SqueezeNet, and VGG16, are combined with five diverse machine learning algorithms: K-Nearest Neighbors (KNN), Neural Network (ANN), Random Forest (RF), Artificial Support Vector Machine (SVM), and Logistic Regression (LR). The dataset comprises a collection of images depicting infected date palm leaves, and performance evaluation metrics, including accuracy, recall, precision, and F1 score, are computed for each algorithm. The results unveil varied levels of accuracy among the feature extraction methods and machine learning algorithms. Specifically, Inceptionv3 achieved an accuracy of 80.4% for KNN, while SqueezeNet attained 75.3% and VGG16 obtained 76.6% accuracy. For SVM, the … |
| Anahtar Kelimeler |
| classification | date palm leave | deep learning | insect infected | machine learning |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 5 |
| Google Scholar | 22 |