Due to the complex background of flower images taken under natural conditions and their high intra-class variability and inter-class similarity,it is difficult to achieve accurate fine-grained classification by existing popular methods relying only on the convolution module to extract local features of flowers.To address the above problems,this paper proposes a high-precision and lightweight flower classification method (ConvTrans-ResMLP).It achieves global feature extraction of flower images by combining the Transformer module and the residual multi-layer perceptron (MLP) module,and adds convolutional computation to the Transformer module so that the model still retains the ability to extract local features.Meanwhile,in order to further deploy the model to edge devices,this study achieves compression and optimization of the model based on knowledge distillation.The experimental results show that the accuracy of proposed method achieves 98.62%,97.61% and 98.40% on Oxford 17,Oxford 102 and homemade Flowers 32 datasets,respectively.The size of the lightweight model in this paper is about 1/18 of the original one after knowledge distillation, while the accuracy rate only decreases by about 2%.Therefore,this study can better improve the efficiency of flower fine-grained classification by edge equipment, which is of practical significance to promote the automation of flower cultivation.