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@article{206660,
        author = {Amruta Jadhav and Majharoddin Kazi and Pravin Shetiye},
        title = {Automatic Ayurvedic Leaf Classification System using Advanced Deep Learning Approaches},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {2},
        pages = {2319-2329},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206660},
        abstract = {Plants are important component in traditional medicines and are useful for different diseases and healing the wounds. Classification of Ayurvedic medicinal plants is very important in number of fields such as healthcare, agriculture and environmental conservation. Leaves provide an important source of data for studies of plant biology. Plant leaf classification has been an important task and it is difficult due to several reasons. The classification of plants using digital leaf images has nowadays become an important research area in the automatic classification of plants. Many researchers have been focusing on the usage of different deep learning models in plant classification. InceptionV3, Inception-ResNetV2 and VGG16 were used as three convolutional neural network (CNN) architectures. The model is trained and tested with a dataset of more than 1000 images of medicinal leaves. The experimental results show that InceptionV3 obtained the highest accuracy of 95.17%, Inception-ResNetV2 achieved 89.78% accuracy and VGG16 achieved 88.11% accuracy. The recommended approach is to pre-process the leaf images and classify based on specific properties like texture and venation patterns, by extracting features by deep learning models. In addition, a graphical user interface (GUI) is developed to facilitate real time identification, which allows users to forecast the type of medicinal leaf, see its image and know about its applications.},
        keywords = {Medicinal Leaf Classification, Deep Learning, CNN, Image Processing, GUI Development.},
        month = {July},
        }