Medicinal Plant Classification Using Convolutional Neural Networks

Medicinal plants play a crucial role in healthcare systems across the world due to their therapeutic and pharmacological properties. Accurate identification of these plants is essential for ensuring the correct use of herbal medicines and preventing adulteration. Traditional methods of plant identification rely on expert knowledge and manual examination of morphological features, which are often time consuming and error-prone. With recent advancements in Artificial Intelligence (AI), image based plant classification using Deep Learning (DL) techniques has gained significant attention. In this research, a Convolutional Neural Network (CNN) model is proposed for automatic classification of medicinal plants based on leaf images. The CNN model effectively extracts and learns hierarchical features from plant images, resulting in high classification accuracy compared to traditional machine learning methods. The system is trained and evaluated on a publicly available dataset of medicinal plant images. Experimental results demonstrate that the proposed CNN-based approach achieves robust and reliable performance, making it a promising tool for digital herbarium development, biodiversity conservation, and pharmaceutical research.