Görüntü Dönüştürücü Tabanlı Meta-Öğrenme ile Gürbüz Bir Elma Hastalığı Sınıflandırma Yaklaşımı

Abstract

Az örnekle öğrenme, etiketlenmiş verilerin kısıtlı olduğu tarımsal sınıflandırma problemleri için önemli bir çözüm olarak ortaya çıkmıştır. Bu çalışma elma yapraklarındaki hastalıkları sınıflandırmak amacıyla Görüntü Dönüştürücü modeli ile Model-Bağımsız Meta-Öğrenme algoritmalarını (MAML ve MAML++) birlikte kullanmayı önermektedir. Bu yaklaşımların performansı, 2-way 5-shot ve 4-way 1-shot senaryolarında değerlendirilmiştir. MAML algoritması, 2-way 5-shot senaryosunda en iyi performansını (96,27% doğruluk, 0,1299 kayıp) elde ederken, 4-way 1-shot senaryosunda en kötü performansını (88,80% doğruluk, 0,2884 kayıp) sergilemiş ve bu da %7,47'lik bir doğruluk farkı yaratmıştır. Buna karşılık, MAML++ algoritması, en iyi (90,73% doğruluk, 0,3580 kayıp) ve en kötü (83,60% doğruluk, 0,5401 kayıp) performansları arasında %7,13'lük daha küçük bir doğruluk farkı ile daha iyi bir tutarlılık sergilemiştir. Bu bulgular, MAML'ın daha iyi sınıflandırma performansına rağmen MAML++'ın daha tutarlı ve daha dayanıklı olduğunu göstermektedir. Bu çalışma, Görüntü Dönüştürücü modellerinin özellik çıkarım yeteneklerini meta-öğrenme algoritmalarıyla birleştirerek gerçek dünya koşullarında doğru ve güvenilir hastalık sınıflandırması için yeni bir yaklaşım sunmaktadır. Sonuçlar, geliştirilen yaklaşımın tarımsal uygulamalarda, özellikle etiketlenmiş verilerin kısıtlı olduğu senaryolarda önemli bir potansiyele sahip olduğunu göstermektedir.

Keywords

• Az sayıda örnekle öğrenme
• Meta öğrenme
• MAML
• Görüntü Dönüştürücü
• Görüntü sınıflandırma
• Derin öğrenme

A Robust Apple Disease Classification Approach Using Vision Transformer-Based Meta-Learning

Abstract

Few-shot learning has emerged as an important solution for agricultural classification problems, where labelled data is often scarce. This study proposes the integration of the Vision Transformer (ViT) model with Model-Agnostic Meta-Learning (MAML and MAML++) algorithms to classify apple leaf diseases. The performance of these approaches was evaluated in 2-way 5-shot and 4-way 1-shot scenarios. The MAML algorithm achieved its best performance in the 2-way 5-shot scenario (96.27% accuracy, 0.1299 loss), while its worst performance was observed in the 4-way 1-shot scenario (88.80% accuracy, 0.2884 loss), resulting in a 7.47% accuracy gap. In contrast, the MAML++ algorithm demonstrated better consistency, with a smaller accuracy gap of 7.13% between its best (90.73% accuracy, 0.3580 loss) and worst (83.60% accuracy, 0.5401 loss) performances. These findings indicate that, despite MAML achieving better classification performance, MAML++ is more consistent and robust. By combining the feature extraction capabilities of Vision Transformers with meta-learning algorithms, this study presents a new approach for accurate and reliable disease classification under real-world conditions. The results demonstrate that the proposed approach has significant potential in agricultural applications, particularly in scenarios with limited labelled data.

Keywords

• Few-shot learning
• Meta learning
• MAML
• Vision Transformer
• Image classification
• Deep learning

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