Multiple butterflies recognition based on deep residual learning and image analyze

1 Insects recognition is crucial for taxonomy. It helps researchers to process tremendous and various ecology data. Most studies focus on fine-tuning the deep learning network or altering the algorithm to enhance the identification accuracy, and some useful tools have been generated with these methods. 2 This study focuses on the influence of image data on the recognition model. The data set source of the existing automated identification tools is relatively single, and the competition-based data set was released only focus on evaluating the model at present. For the first time, this article integrates butterfly image data sets from multiple sources, covered illustrated books, and butterfly popular science websites. The image types include standard specimen images, illustrated book scan images and camera shots. In addition, these images concluded not only fixed poses, but also various other images of butterflies in natural poses. The size of these images is also various. Testing data set is new data that is not belongs to the training set, which also verifies the generalizability of the model, indicating that in practical applications, this model can identify new images. 3 We designed different data sets using the ResNet18 network to train a classifier, which achieves a validation accuracy of 86 % in the end of analyze. By adjusting the data sets, the accuracy changes as well. This study provides a method to recognize hundreds of butterfly species and analyzes the testing progress from the point of view of data. 4 It is the first to combine butterflies from multiple countries in the world to a single data set, with a recognition accuracy that outperforms previous experiments, to the best of our knowledge. We further analyze the testing results of butterfly recognition at the family and genus level.