This research suggests a multi-layered approach to plant species classification by making use of the general deep learning paradigm in addition to selective machine learning techniques. A method is suggested which may be precipitated by the deep convolutional neural networks method called InceptionV3 for feature extraction from the pictures of plants. Using the transfer learning, the InceptionV3 model trained before is then used for determining the species level of a plant image. On the one hand, extra layers of customization are executed atop the features to train the species classification. Furthermore, the incorporation of well-known supervised machine learning algorithms which are support vector machine and k-nearest neighbors as supplemented approaches for deep learning is realized. To evaluate the proposed method, an image set of plants is used to test the performance with the help of different evaluation metrics. In overall, the proposed approach exhibits a possibility to classify the species accurately and combines in itself good results of deep learning as well as artificial intelligence techniques for the task assigned.