Abstract
Many studies are based on the study of plant classification and their identification using its leaves, and there are many studies to identify plants using its fruits. Most of these studies are based on the leaves of the plant in general as well as the fruits in general as well. In this research, we present a new tool using artificial intelligence to classify and identify wild plants through the leaves of these plants, or by using their fruits, or by using both leaves and fruits together. This tool has proven an excellent result compared to similar tools in the same field. More than one AI model was applied to three datasets, lower plants dataset (LPDS), upper plant dataset (UPDS), and fruit plant dataset (FPDS). The aim of this study is to use machine learning methods to serve in the plant taxonomy and identification. The wild plant's dataset was gathered in its natural habitat in Egypt. The developed convolution neural network model (AlexNet CNN), the Random Forest (RF), and the support vector machine (SVM) techniques were contrasted in the species classifications. The highest degree of accuracy achieved was 98.2% by using the developed CNN model.