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Journals / International Journal of Multidisciplinary Studies and Innovative Technologies / 2025 / Cilt: 9 Sayı: 2

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

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

Göktürk Öztürk(RECEP TAYYİP ERDOĞAN ÜNİVERSİTESİ)0000-0003-3529-6393
Köksal Erentürk(ATATÜRK ÜNİVERSİTESİ)0000-0001-7536-1351
Pages
262–266
Publication Date
2025-11-30
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Results demonstrated a high classification accuracy of 90.48%, showing that the model effectively distinguishes mushroom species despite a small number of confusions.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Mushrooms, as nutritionally and medicinally valuable macrofungi, require accurate recognition due to the presence of toxic species causing severe health risks.
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Key Findings & Data: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.
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Conclusion & Impact:Results demonstrated a high classification accuracy of 90.48%, showing that the model effectively distinguishes mushroom species despite a small number of confusions.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:few-shot öğrenme · siamese ağı · mantar tanıma

Abstract

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.

Özet

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.

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Cite This Article

Öztürk, G., Erentürk, K. (2025). Recognition of Mushroom Species Using Few-Shot Learning Method with a Siamese Network. International Journal of Multidisciplinary Studies and Innovative Technologies, Cilt: 9 Sayı: 2, 262–266. https://ajindex.com/articles/1496888

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