Automated Medicinal Plant Identification and Use Case Generation: A Convolutional Neural Network-Xception Approach

Abstract

Abstract: In the realm of botany, pharmacology, and herbal medicine, the identification of medicinal plants stands as a crucial yet labor-intensive endeavor. This project presents a pioneering approach to automate plant identification through the utilization of advanced deep learning techniques, specifically employing the Xception architecture within a Convolutional Neural Network (CNN). The methodology involves the acquisition of plant images, followed by preprocessing and dimension reduction to 299x299 pixels. Subsequently, the model undergoes rigorous training, achieving commendable accuracies of approximately 85% in training, 93% in validation, and 94% in testing phases. The trained model is then deployed through a user-friendly web interface, allowing seamless interaction for uploading images and receiving comprehensive plant classification results. Leveraging Flask for backend integration, this system bridges the gap between cutting-edge technology and botanical expertise, enabling researchers, botanists, pharmacologists, and herbalists to expedite plant identification processes with heightened accuracy and efficiency. Furthermore, this innovation not only streamlines identification tasks but also provides detailed insights into the diverse applications and attributes of medicinal plants, thereby fostering advancements in research, conservation, and utilization for the betterment of human health and well-being..