Continuous development of convolutional neural networks has shown good performance for fine-grained image classification by identifying fine features in high-resolution images. However, in the real world, many images are due to camera or environmental restrictions. Low-resolution images with fewer fine features result in a dramatic reduction in classification accuracy. This study proposes a two-phase Data Augmentation guided Decoupled Knowledge Distillation (DADKD) framework to improve the classification accuracy for low-resolution images. In the proposed DADKD, one phase is data augmentation that generates a composite image and corresponding labels. Another stage is knowledge distillation, which minimizes differences between high-resolution and low-resolution image features. The proposed DADKD was validated on three fine-grained datasets (i.e. Stanford-Cars, FGVC-Aircraft, and CUB-200-2011). Experimental results show that our proposed DADKD achieves 88.19%, 78.98% and 80.33% classification accuracy on these three datasets, state-of-the-art methods such as SnapMix and Decoupled Knowledge Distillation (DKD). The method proposes a viable solution for fine-grained classification at low resolution.