Real-Time Identification of Medicinal Plants Using Deep Learning Techniques

A significant portion of the global population uses medicinal plants as a primary source of therapeutic chemicals and as an alternate source of pharmaceuticals. Plant identification from photos is a rapidly developing field of research at the moment due to recent developments in computer vision. High precision, accuracy, and practical application were demonstrated by several findings. Exact automatic identification of medicinal plants was the aim of this study. The illumination in the area is uneven, making it challenging to distinguish a leaf from its backdrop. We provide a procedure that uses a sample of a plant's leaves to identify the species. We provide a new collection of medicinal plant photos, which includes one and ten (10) different plant species categories. In the suggested technique, relevant information is automatically extracted from plant photos using convolutional neural networks (CNNs). Large-scale high-resolution photographs of a variety of therapeutic plant species are used to train the CNN model. Using distinguishing visual characteristics, such as leaf form, color, and texture, the trained model is able to identify several plant species. Utilizing a smartphone or other portable device, users may snap images of plants with the developed system and share them with others for real-time plant identification on a mobile or embedded platform. Following analysis of the obtained image using the trained CNN model, a precise classification result is obtained fast.