TÜRKİYE'DEKİ KELEBEK TÜRLERİNİN BASAMAKLI EVRİŞİMLİ SİNİR AĞLARI İLE SINIFLANDIRILMASI

Abstract

Kelebekler ekosistemdeki değişikliklere hızlı bir şekilde yanıt verebilme özelliğine sahiptir. Ayrıca çoğu kelebek türü larvaları, insan ve hayvanların yaşam ortamını ve gıda kaynaklarını etkileyen tarım ve orman zararlılarıdır. Bu nedenle kelebek türlerinin sınıflandırılması, tür araştırmalarının yanı sıra çevre koruma, tarım ve orman zararlılarının kontrolünde de önemlidir. Bu çalışmada Türkiye’deki 9 aile ve 416 kelebek türünü sınıflandırmak için yedi adet evrişimli sinir ağı transfer öğrenme yöntemiyle kullanılmıştır. Veri seti oluşturmak için 13528 görüntü toplanmış, veri artırma yöntemi ile görüntü sayısı 67640’a çıkarılmıştır. Eğitimde ezberlemenin önüne geçebilmek, ağların performansını ve güvenirliliğini artırmak için Stratified Shuffle Split, K fold cross validation yöntemleri kullanılmıştır. Tür sayısının fazlalığı, türlerin desen ve renk benzerliği nedeniyle ağların düşük başarı oranını artırmak için iki basamaklı ağ modeli kullanılmıştır. Modelde birinci basamakta bir, ikinci basamakta paralel bağlı dokuz ağ vardır. Birinci basamaktaki ailelere göre sınıflandırmada %95.88, ikinci basamaktaki tür sınıflandırmada ise %91.99 ile %100 arasında başarı oranı elde edilmiştir.

Keywords

• Türkiye Kelebek Türleri
• Kelebek türlerinin Tanımlanması
• Evrişimli Sinir Ağları
• Önceden Eğitilmiş Ağlar
• Transfer Öğrenme

Classification of Butterfly Species in Turkey with Cascaded Convolutional Neural Networks

Abstract

Butterflies have the ability to respond quickly to changes in the ecosystem. In addition, most butterfly species larvae are agricultural and forest pests that affect the habitats and food resources of humans and animals. Therefore, classification of butterfly species is important in environmental protection, agriculture and forest pest control as well as species research. In this study, seven convolutional neural network transfer learning methods were used to classify 9 families and 416 butterfly species in Turkey. In order to create a dataset, 13528 images were collected, and the number of images was increased to 67640 by data augmentation method. Stratified Shuffle Split, K fold cross validation methods were used to prevent memorization and increase the performance and reliability of networks. A cascaded network model was used to increase the low success rate of networks due to the excess number of species, the pattern and color similarity of species. In the model, there is one network on the first layer and nine networks connected in parallel on the second layer. A success rate of 95.88% was achieved in the classification according to families in the first layer and 91.99% to 100% in the classification of species in the second layer.

Keywords

• Turkey Butterfly Species
• Identification of butterfly species
• Convolutional Neural Network
• Pre-Trained Networks
• Transfer Learning

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