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@article{174240,
        author = {Singam Navya Sri and Akula Chandradeep and GANJI VENKATA MURALI KRISHNA and Gudivada Syalini Naga Chandra and MS DUDIKI SIRISHA},
        title = {Identification of medicinal plants using Deep Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {4102-4108},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174240},
        abstract = {The identification of medicinal plants plays a critical role in various fields, including pharmaceuticals, traditional medicine, and biodiversity conservation. However, traditional methods of identifying these plants can be time-consuming, labor-intensive, and require expert knowledge. This project aims to leverage deep learning techniques to automate the identification of medicinal plants, thus providing an efficient and accurate solution. The study employs Convolutional Neural Networks (CNN), MobileNet, and a hybrid model combining MobileNet with Recurrent Neural Networks (RNN) to classify and identify different medicinal plants. The models are trained and validated using a comprehensive dataset of medicinal plant images, ensuring robust performance. This approach not only enhances identification accuracy but also reduces the dependency on expert botanists, making it accessible to a broader audience. Keywords: Medical plants, Deep learning, Convolutional Neural Networks (CNN) MobileNet Recurrent Neural Networks (RNN), Plant Identification, Image Classification.},
        keywords = {},
        month = {March},
        }