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@article{178884,
        author = {Dr Chandrakala V Patil and Shweta More and Amruta P V and Shivalingamma},
        title = {Intelligent Medicinal Leaf Classification Using Hybrid Machine Learning and Deep Learning Approaches},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {6768-6772},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178884},
        abstract = {This paper presents a comprehensive study and implementation of an automated system for the identification and classification of medicinal leaves using machine learning (ML) and recent deep learning (DL) approaches. Initially based on the Random Forest algorithm, the system has been extended with Convolutional Neural Networks (CNNs) and transfer learning techniques such as VGG16 and MobileNet for improved accuracy and scalability. A custom dataset of medicinal leaf images was used to train and evaluate the models. A Flask-based web interface and a proposed mobile application enable users to upload leaf images for identification and access associated medicinal benefits. The paper contributes to the growing field of computational botany by offering an accessible, accurate, and scalable solution for herbal identification, promoting holistic medicine and sustainable healthcare practices.},
        keywords = {Medicinal plants, leaf classification, machine learning, deep learning, Random Forest, CNN, transfer learning, Flask, mobile application},
        month = {May},
        }