Identification of Ayurvedic Plants Using Deep Learning

Deep learning algorithms have become pivotal in the accurate identification and classification of Ayurvedic plants, leveraging advanced techniques to distinguish between various species based on leaf images. Among these algorithms, the VGG16 model, a convolutional neural network (CNN), is particularly notable for its robust performance in image recognition tasks. The Ayurvedic plant detection database serves as a comprehensive resource for training and validating these models, facilitating high accuracy in plant detection. The integration of deep learning models presents significant potential for botany and health care applications. By offering reliable and precise identification of Ayurvedic plants, the proposed system supports botanists and healthcare professionals in making informed decisions. This leads to more accurate identification and utilization of traditional medicinal knowledge. These advancements not only improve the efficiency and reliability of plant detection systems but also build confidence in AI-driven solutions within the field of traditional medicine. The potential to revolutionize plant classification and enhance the understanding of medicinal plants underscores the importance of continued research and innovation in this domain. The model proposed ensures faster and easier way to recognize the medicinal plants and give its description to the reader using deep learning methods with the accuracy of 96% identification systems.