Automated Medicinal Plant Identification Using Deep Learning for Improved Healthcare

Abstract: With thousands of plant species present in the world, many of them possessing medicinal properties, the identification of these plants plays a crucial role in healthcare, drug manufacturing, and environmental management. Proper identification benefits various sectors, including the forest department, life scientists, physicians, medication laboratories, and the public. While manual identification by skilled practitioners has been the traditional method, it is often time-consuming and prone to misidentification, which could lead to side effects or serious health risks. To address these challenges, this project proposes an automated system for medicinal plant classification using deep learning algorithms. In recent years, the use of deep learning techniques, particularly in computer vision, has gained traction for solving complex identification problems. The proposed system employs convolutional neural networks (CNNs), which are highly effective in feature extraction and classification tasks. The model utilizes Xception-based feature extraction, followed by classification with a CNN classifier, demonstrating high accuracy and faster prediction times with real-time images. By automating the identification process, the system aims to enable faster and more reliable recognition of medicinal plant species. This advancement could significantly reduce errors, increase efficiency, and provide immediate access to valuable plant-based medicinal data for various stakeholders, ensuring safer and more effective use of medicinal plants in healthcare.