Medicinal Leaf Detection System Using Deep Learning

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@article{1715227,
      author = {Dhivya S, Vinayaganand S, Suriya S, Thangadurai N},
      title = {Medicinal Leaf Detection System Using Deep Learning},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {9},
      pages = {2418-2424},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1715227.pdf},
      abstract = {Identifying medicinal plants has traditionally required expert knowledge, which makes it difficult for common people and beginners to recognize useful plants correctly. Even though many medicinal plants are widely used in traditional medicine, distinguishing between different species can be confusing because many leaves look very similar. This is where an intelligent medicinal leaf detection system can make a real difference. The proposed system uses deep learning to automatically identify medicinal plants from leaf images. Instead of relying on manual observation, the model learns patterns from thousands of leaf images and predicts the plant species accurately. The system uses the MobileNetV2 deep learning architecture to perform efficient image classification while maintaining fast performance. Users simply upload a leaf image through a web interface, and the system analyzes the image to identify the plant. The platform also includes image preprocessing techniques such as resizing and normalization to improve prediction accuracy. Under the hood, the system is built using Python for model development, TensorFlow/Keras for deep learning, Flask for the backend, and HTML, CSS, and JavaScript for the web interface. Experimental results show that the model achieves strong classification performance and provides quick predictions, making the system suitable for real-time use.},
      keywords = {Medicinal Plants, Deep Learning, Image Classification, MobileNetV2, Artificial Intelligence, Plant Identification.},
      month = {March},
      doi = {https://doi.org/10.64388/IREV9I9-1715227}
  }