Securing Medicinal Plant Supply Chain through ML-Enhanced Image Processing

Abstract

he medicinal plant industry encounters numerous challenges, including misidentification and counterfeit goods. These issues have the potential to compromise the quality standard and, consequently, compromise the safety of medical products. By employing CNN, a subset of Python-based ML algorithms, this article presents a novel approach to the problem of pharmaceutical image recognition in the medicinal plant supply chain. The developed system will be predicated on a large database of images of medicinal plants, which will be utilized to instruct CNN models that have undergone rigorous training to accurately identify and authenticate plant materials throughout the entire supply chain. By utilizing an ML system for image processing, this system provides the most reliable assurance that medicinal plants are genuine and of high quality. The experimental results provide evidence that the proposed solution is valuable, as it holds potential for enhancing supply chain security, eliminating counterfeit products, and facilitating the identification of medicinal plants. The Python implementation of the system's solution facilitates user customization and integration. That would enable stakeholders to effectively address their requirements, consequently facilitating prompt decision-making. The purpose of this project is to present a specific ML and CNN technique that addresses the issue of counterfeit goods and contributes to the chain security of medical plants.