Medicinal Plant Recognition System using Deep Learning, Image Processing, and Ethnobotanical Insights

The precise identification of medicinal plants is a critical challenge in pharmacology, biodiversity conservation, and the preservation of traditional medicine. Historically, recognizing these plants has depended on the manual expertise of taxonomists and traditional healers, which is time-consuming and vulnerable to human error. Recent advancements in Artificial Intelligence (AI) offer a transformative solution. This paper proposes an automated Medicinal Plant Recognition System that integrates Convolutional Neural Networks (CNNs), advanced image processing, and ethnobotanical data. By exploring multi-scale feature extraction, hybrid architectures, and dataset bias treatment, this system achieves high accuracy in natural environments. This study presents a comprehensive literature review, methodology, and architectural design incorporating all state-of-the-art research provided.