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@article{191621,
        author = {Sampath Kumar and Bala Subramanyam and Harshith Kataray and Mahin Munawar},
        title = {A Novel Classification Approach to Medicinal Plant Recognition using Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {7393-7397},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191621},
        abstract = {Identifying medicinal plants is vital for healthcare, traditional medicine, pharmaceutical research, and biodiversity conservation. Conventional identification methods are often subjective, time-consuming, and prone to errors due to morphological similarities among plant species and environmental variations. This paper presents a deep learning–based classification framework using Convolutional Neural Networks (CNNs) for automated medicinal plant recognition from leaf images. A curated image dataset containing multiple medicinal plant species was utilized, and transfer learning–based CNN architectures were trained and fine-tuned to improve classification performance. The proposed system is integrated with a web and mobile-compatible application interface to enable real-time plant identification. Experimental results demonstrate that the proposed approach achieves high accuracy and reliable generalization, making it suitable for practical deployment in healthcare, agriculture, education, and conservation domains.},
        keywords = {Medicinal Plant Recognition, Deep Learning, Convolutional Neural Networks, Image Classification, AI in Healthcare, Plant Taxonomy.},
        month = {January},
        }