Plant species detection using deep learning methods has gained massive attention in the last few years due to the promising results obtained by different research communities using the deep learning approach. However, there are numerous plant species present in real life and their accurate classification based on some digital images is a quite challenging and critical task. Therefore, research on Plant species identification is of great importance. Thus, a Convolution Neural Network Architecture (CNN) based Plant Classification (CNN-PC) Model is presented in this work to efficiently classify a large number of plantspecies and correctly detect which image belongs to which species. The proposed CNN-PC model generates pre-trained weights based on the given input plant image data using different layers, blocks, and pretrained functions and packages to handle dependencies. The Vietnam Plant (VNP-200) dataset is utilized to evaluate the classification performance of the proposed CNN-PCmodel. Multi-class classification is performed to evaluateclassification results. The obtained classification accuracy considering all 200 classes is96.42%.