Abstract: Plant identification is an essential field in the earth’s environment to keep the atmosphere in good condition. These plants involve some potent medical benefits. Nowadays, identifying a plant by its appearance is a challenge. Identifying species is essential for preserving species. Standard plant identification methods are complex, time-consuming, and frustrating for non-experts. Automated species identification is a reality because of the widespread availability of pertinent technology like digital cameras, cell phones, and sophisticated image processing and pattern recognition techniques. The creation of convolutional neural network models for recognizing plant species using simple leaf pictures is explained in this study using deep learning techniques. Using an accessible database containing 100 plant species images that had 64 different element vectors of plants in a set of 100 other classes of plant species, the models were trained. The proposed framework is superior to several modern model designs in training, with a 95.06% success rate in recognizing appropriate plant species. The model is a highly efficient identification encouraging tool because of its high success rate. The approach might be improved to offer a framework for integrated plant species identification that could provide accurate ecological services.