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 aswell 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.