The authenticity and integrity of medicinal plant supply chains are essential for ensuring product quality, safety, and efficacy. However, the complexity and global nature of these supply chains pose significant challenges in maintaining transparency and accountability. In this paper, we propose a novel approach leveraging machine learning (ML)-based image processing techniques to address these challenges. Our methodology involves training ML models to recognize medicinal plant species, assess quality, detect adulteration or contamination, establish traceability, authenticate products, monitor growth conditions, and integrate data for decision support. By analyzing images of medicinal plants at various stages of the supply chain, our approach enables stakeholders to verify authenticity, identify potential issues, and make informed decisions. We demonstrate the feasibility and effectiveness of our approach through case studies and highlight its potential to enhance transparency, accountability, and trust in medicinal plant supply chains. This research contributes to the broader goal of improving global healthcare by ensuring the integrity of natural remedies derived from medicinal plants.
Article Details
Unique Paper ID: 163057
Publication Volume & Issue: Volume 0, Issue no
Page(s): 205 - 212
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