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@article{203349,
        author = {Shlok Laad and Kartik Sahu and Archi Jain and Sharayu Rathi and Dr. Mahendra Gaikwad},
        title = {AI-Integrated Medicinal Plant Identifier and Usage Guide},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {11596-11602},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=203349},
        abstract = {Medicinal plants represent one of the oldest and most widely used sources of healthcare, which form the foundation of traditional systems such as Ayurveda, Siddha and Unani. They continue to contribute significantly to modern pharmacology and the discovery of new drugs. However, the ability to correctly identify plants remains a frequent challenge due to the ability of terms, regional variations and easily accessible resources. Incorrect identity can lead to ineffective treatment or even toxic side effects. At the same time, reliable information about plant use, dosage and side effects is often scattered in books, research papers and government databases, making it difficult for non-experts and rural population.
This research presents an AI-ecclesiating medicinal plant identifier and use guide that takes advantage of the firm nervous network (CNN) for the image-based recognition of the plant species and integrates this functionality with medicinal properties, methods of preparation, dosage and wide knowledge of e-prests. The system not only identifies plants from uploaded or captured images, but also allows a reverse search mechanism, where users can queries based on symptoms or health conditions to recommend recommended medicinal plants. Keeping in mind the inclusion, the system provides multilingual and vocal interfaces, making it accessible to both urban and rural population in diverse linguistic backgrounds. The proposed solution displays the ability to bridge the difference between traditional herbal knowledge and modern AI applications, supporting healthcare, education and research. Over 90% recognized accuracy using CNN architecture optimized with experimental results.},
        keywords = {Medicinal Plants, Convolutional Neural Networks, Deep Learning, Image Recognition, Knowledge Base, Traditional Medicine, Healthcare AI, Symptom-to-Plant Search, Multilingual Interfaces, Voice-enabled Interfaces, Rural Healthcare Applications.},
        month = {May},
        }