• S Bavithra
• R P Bijusha

Herbal plants are crucial to human existence for medical reasons, and they can also provide free oxygen to the environment. Many herbal plants are rich in therapeutic goods and also it includes the active elements that will benefit future generations. The sustainable sourcing of medicinal plants is crucial for the pharmaceutical and herbal medicine industries. However, the identification and traceability of these plants in the supply chain pose significant challenges. This project proposes an innovative approach that combines machine learning (ML) techniques for medicinal plant detection with supply chain management strategies to ensure transparency, quality, and sustainability. The project focuses on optimizing the entire lifecycle of medicinal plants, from cultivation to distribution, leveraging advanced image processing algorithms. By employing high-resolution imaging techniques, the system aims to enhance plant health monitoring, disease detection, and growth assessment in medicinal plant cultivation. Additionally, image processing algorithms facilitate the automation of harvesting processes, ensuring optimal timing for maximum yield and potency of medicinal compounds. The system extends its functionality to the supply chain by using image recognition to assess the quality of harvested plants, streamline sorting processes, and monitor transportation conditions.

"MACHINE LEARNING FOR MEDICINAL PLANT AUTHENTICATION AND SUPPLY CHAIN INTEGRITY", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.d334-d338, March-2025, Available :http://www.jetir.org/papers/JETIR2503336.pdf

"MACHINE LEARNING FOR MEDICINAL PLANT AUTHENTICATION AND SUPPLY CHAIN INTEGRITY", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppd334-d338, March-2025, Available at : http://www.jetir.org/papers/JETIR2503336.pdf