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@article{180065,
        author = {Shobha Chandra K and Shruthi N M and Vishwajith G Bhat and Fathima Zahara and Veditha B S},
        title = {A Survey on Identification of Medicinal Plants  using Machine Learning and Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {138-145},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180065},
        abstract = {India possesses a rich diversity of plant life 
and maintains a longstanding tradition of employing 
medicinal flora in conventional and complementary 
therapeutic approaches. Woodland environments serve 
as primary repositories for these healing botanicals, 
which demonstrate essential functions in addressing 
numerous medical ailments. Precise plant identification 
represents a critical requirement for ensuring their 
secure utilization. Traditional identification methods 
typically involve manual processes, require specialist 
knowledge, and consume considerable time. The rise of 
technologies like machine learning (ML) and deep 
learning (DL), particularly within computer vision 
applications, has facilitated substantial advancements 
in automated medicinal plant recognition. This 
research takes a close look at machine learning and 
deep learning approaches, including methods like 
Convolutional Neural Networks (CNNs), Random 
Forest classification algorithms, and integrated 
computational models employed for medicinal plant 
categorization. These technological solutions provide 
expandable, immediate identification functionalities, 
improving availability for research institutions and 
general 
users 
alike. 
This review investigates 
contemporary developments, existing obstacles, and 
future research opportunities in creating sophisticated 
medicinal plant identification frameworks.},
        keywords = {Medicinal Plant Identification, CNN, Deep  Learning.},
        month = {May},
        }