Published Paper Details:

The demand for authentic medicinal plants is on the rise, necessitating robust methods to ensure their integrity throughout the supply chain. In this paper, we present a novel approach leveraging machinelearning, specifically YOLOv8, for the precise identification of medicinal plants. Our methodology involves the curation of a diverse dataset comprising 30 distinct species of medicinal plants, which is used for rigorous training and testing of the model. The developed web application seamlessly integrates HTML, CSS, Bootstrap, JavaScript, React.JS, and Flask, offering a user-friendly interface for plant identification. Through comprehensive evaluation, our model demonstrates commendable performance metrics, contributing significantly to the authentication and preservation of medicinal plant integrity in the supply chain. This research not only addresses existing challenges but also paves the way for future advancements in leveraging machine learning for plant identification and supply chain management