To solve the problem that deep convolutional neural network （DCNN） models with heavy weights are difficult to be effectively applied on AI edge devices with weak computing power and high storage costs， a flower image classification system equipped with a lightweight DCNN is proposed with the help of a heavyweight DCNN during training process. First， an extended flower data set suitable for lightweight DCNN training is constructed by using a heavyweight DCNN combined with transferring learning， the crawler technology and the maximum connected region segmentation method. Then， two lightweight DCNN models， Tiny-Darknet and Darknet-Reference， oriented for devices with weak computer power are trained based on the specially built flower image gallery. Experimental results show that the two optimized models obtained can achieve 98.07% and 98.83% average classification accuracy respectively on Oxford102 flower dataset while keeping the model size as 4 MB and 28 MB， which have promising application potentials for AI edge computer devices.