Recognition of Mushroom Species Using Few-Shot Learning Method with a Siamese Network

Yıl 2025, Cilt: 9 Sayı: 2, 262 - 266, 30.11.2025

Göktürk Öztürk , Köksal Erentürk

Öz

Mushrooms, as nutritionally and medicinally valuable macrofungi, require accurate recognition due to the presence of toxic species causing severe health risks. Traditional methods based on morphology are time-consuming and prone to human error, which makes automated solutions essential. In this study, a siamese neural network with a ResNet18 backbone was applied to mushroom species recognition under a 7-way 3-shot learning setting. The dataset, derived from Kaggle, was pre-processed with background removal, resizing, normalization, and augmentation to ensure reliable feature extraction. The model was trained with cosine embedding loss and evaluated using accuracy, precision, recall, F1-score, and confusion matrix analysis. Results demonstrated a high classification accuracy of 90.48%, showing that the model effectively distinguishes mushroom species despite a small number of confusions. These findings confirm the effectiveness of siamese networks for mushroom classification and suggest future improvements.

Anahtar Kelimeler

few-shot learning , siamese network , mushroom recognition , image classification , deep learning

Siamese Ağı ile Few-Shot Öğrenme Yöntemi Kullanılarak Mantar Türlerinin Tanınması

Öz

Besinsel ve tıbbi açıdan değerli makrofunguslar olan mantarlar, ciddi sağlık risklerine neden olan zehirli türlerin varlığı nedeniyle doğru bir şekilde tanınmayı gerektirir. Morfolojiye dayalı geleneksel yöntemler zaman alıcıdır ve insan hatasına açıktır, bu da otomatik çözümleri gerekli kılar. Bu çalışmada, 7-way 3-shot öğrenme düzeninde, mantar türlerinin tanınması için ResNet18 omurgasına sahip bir siamese sinir ağı uygulanmıştır. Kaggle’dan elde edilen veri kümesi, güvenilir özellik çıkarımını sağlamak amacıyla arka plan kaldırma, yeniden boyutlandırma, normalleştirme ve veri artırma işlemleriyle ön işleme tabi tutulmuştur. Model, kosinüs gömme kaybı ile eğitilmiş ve doğruluk, kesinlik, duyarlılık, F1-skoru ve karmaşıklık matrisi analizleri kullanılarak değerlendirilmiştir. Sonuçlar, %90,48 gibi yüksek bir sınıflandırma doğruluğu ortaya koymuş ve modelin az sayıda karışıklık olmasına rağmen mantar türlerini etkili bir şekilde ayırt ettiğini göstermiştir. Bu bulgular, mantar sınıflandırması için siamese ağlarının etkinliğini doğrulamakta ve gelecekteki iyileştirmelere işaret etmektedir.

Anahtar Kelimeler

few-shot öğrenme , siamese ağı , mantar tanıma , görüntü sınıflandırma , derin öğrenme

Kaynakça

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