Arı hastalıklarının hibrit bir derin öğrenme yöntemi ile tespiti

Öz

Canlı türlerinin gelişiminde büyük bir etkisi olan arılar Dünya’da ki en eski canlı türlerinden birisidir. Besin zincirinin en altında bulunan bitkilerin devamlılığı arıların tozlaşma yapmasıyla doğrudan ilgilidir. Arılar bu özelliğinden dolayı küresel bir sigorta konumundadır. Bu nedenle arıların sağlık durumlarının kontrol edilmesi oldukça önemlidir. Günümüzde gelişen teknolojiye bağlı olarak, arıların sağlık durumlarının uzaktan gerçek zamanlı görüntü işleme uygulamaları ile kontrol edilebilmesi mümkün olabilmektedir. Gerçekleştirilen çalışmada derin öğrenmenin güçlü yanlarından olan öznitelik çıkarma yöntemleri iki farklı koldan işletilerek, görüntülerdeki agresif değişiklikler tespit edilmiştir. Sınıflandırma işleminde, olasılık hesabına dayanan ve sınıf sayısı kadar çıkış veren Softmax sınıflandırıcısı ile tek bir çıkış verebilen ve bu çıkışta da sınıf bilgisini sunabilen çok katmanlı geri beslemeli yapay sinir ağı (ÇKGB-YSA) kullanılmıştır. Yapılan deneysel çalışmalar neticesinde, aynı veri seti üzerinde altı farklı arı hastalığı için softmax sınıflandırıcısı ile %92,70 başarım oranı yakalanabilirken, geliştirilen sistem ile %94,25 başarım oranı elde edilmiştir. Bu çalışmada arıların sağlık durumlarının sınıflandırılması için derin öğrenme yöntemlerine dayalı hibrit bir yöntem önerilmiş ve başarılı sonuçlar elde edilmiştir.

Anahtar Kelimeler

• Arı hastalıkları,derin öğrenme,evrişimsel sinir ağları,sınıflandırma

Kaynakça

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