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@article{178711,
        author = {Mallempati Umesh and K Talpa Sai Dhanush and K Himakar Sai and Thalla Anusha and K.V Chalma Reddy and Thabassuma Khan},
        title = {Identification of Different Medicinal Plants/Raw materials through Image Processing Using Machine Learning Algorthims},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {5841-5845},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178711},
        abstract = {Accurate identification of medicinal plants is essential for applications in pharmaceuticals, herbal medicine, and agriculture. Conventional identification methods often require expert knowledge, making them inefficient and prone to error. This paper introduces an automated system that uses deep learning—specifically, Convolutional Neural Networks (CNNs)—to classify medicinal plants and their raw materials based on image analysis. Unlike prior work that depends on publicly available datasets, our approach leverages a custom dataset gathered from diverse sources. The images were processed through a series of steps including noise reduction, segmentation, and feature extraction. Our experiments reveal that the CNN-based model achieves an impressive classification accuracy of 96.7%, surpassing traditional algorithms such as Support Vector Machines (SVMs) and Random Forest classifiers. The outcomes of this research indicate that the proposed method can serve as a robust, scalable tool for real-world plant identification tasks.},
        keywords = {},
        month = {May},
        }