Utilizing Random Forests for the Classification of Pudina Leaves through Feature Extraction with InceptionV3 and VGG19
Yazarlar (2)
Dr. Öğr. Üyesi Elham Tahsın Yasın YASIN Sinop Üniversitesi, Türkiye
Doç. Dr. Murat Köklü Selçuk Üniversitesi, Türkiye
| Bildiri Türü |
Tebliğ/Bildiri
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Bildiri Dili | İngilizce |
| Bildiri Alt Türü | Tam Metin Olarak Yayınlanan Tebliğ (Ulusal Kongre/Sempozyum) | ||
| Bildiri Niteliği | Alanında Hakemli Ulusal Kongre/Sempozyum | ||
| DOI Numarası | 10.58190/icontas.2023.48 | ||
| Kongre Adı | International Conference on New Trends in Applied Sciences (ICONTAS'23) | ||
| Kongre Tarihi | 01-12-2023 / 03-12-2023 | ||
| Basıldığı Ülke | Türkiye | Basıldığı Şehir | Konya |
| Bildiri Linki | https://doi.org/10.58190/icontas.2023.48 | ||
| UAK Araştırma Alanları |
Bilgi Güvenliği ve Kriptoloji
Görüntü İşleme
Yapay Zeka
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| Özet |
| An analysis of the" Pudina Leaf Dataset: Freshness Analysis" reveals distinct classes of dried, fresh, and spoiled mint leaves. Convolutional neural networks, InceptionV3 and VGG19, were used to extract features from the dataset using advanced image processing techniques. The classification task was then performed using a Random Forest machine learning algorithm. In this study, notable results were obtained, proving the effectiveness of the selected methodologies. Mint (Pudina) leaves were classified accurately using InceptionV3-extracted features at 94.8%, demonstrating robust performance in distinguishing freshness states. This deep learning architecture was further shown to be able to capture meaningful patterns within the dataset by utilizing VGG19-extracted features, resulting in an improved accuracy of 96.8%. |
| Anahtar Kelimeler |
| Deep Learning | Image Classification | InceptionV3 | Pudina Leaf Dataset | Random Forest | VGG19 |
BM Sürdürülebilir Kalkınma Amaçları
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| Google Scholar | 13 |