There are tens of thousands of butterfly species. Each butterfly species is closely related to a specific type of plants. It is significant to study butterfly species automatic identification. However, it is very challenging to study butterfly species recognition via the images taken in the field environment. One reason is that there are small number of butterfly species in the existing datasets compared to the reported species in the world. The other reason is that the number of samples (images) of each butterfly species is limited in the datasets. These situations make it challengeable to train a general system for butterfly species identification via machine learning algorithms. In addition, butterfly wings are always folded in the images taken in the field environment, which make it challengeable to learn butterfly classification features, which further make it difficult to study butterfly species recognition using machine learning techniques. Therefore, meta-learning is introduced to address the challenges, and the DL-MAML (Deep Learning Advanced Model-Agnostic Meta-learning) algorithm is proposed for identifying the butterfly species using available images taken in the field environment. It introduces a L2 regularization term to the objective function and the parameter updating of the meta-learning MAML while introducing ResNet34 deep learning model to extract butterfly features and plus two more convolution layers to the MAML model to avoid over-fitting. The extensive experiments including ablation experiments and comparison to available meta-learning models demonstrate that the initial model parameters obtained by DL-MAML algorithm are effective to identify new species butterflies. The DL-MAML is effective in identifying butterfly species using images taken in the field environments, which make it possible to construct the general and complete butterfly species identification system using available butterfly images from limited categories.