Advanced Medicinal Plant Classification and Bioactivity Identification Based on Dense Net Architecture

Plant species identification helps a wide range of stakeholders, including forestry services, botanists, taxonomists, physicians and pharmaceutical laboratories, endangered species organizations, the government, and the general public. As a result, there has been a spike in interest in developing automated plant species recognition systems. Using computer vision and deep learning approaches, this work proposes a fully automated system for finding medical plants. As a result, work is being done to classify the correct therapeutic plants based on their images. A training data set contains image data; this work uses the Indian Medicinal Plants, Photochemistry, and Therapeutics (IMPPAT) benchmark dataset. Convolutional Neural Network (CNN) with DenseNet algorithm is a classification system for medicinal plants that explains how they work and what they're efficient. This study also suggests a standard dataset for medicinal plants that can be found in various parts of Manipur, India's northwest coast state. On the IMPPAT dataset, the suggested DenseNet model has a recognition rate of 99.56% and on the Manipuri dataset; it has a recognition rate of 98.51%, suggesting that the DenseNet method is a promising technique for smart forestry.

@article{Pukhrambam2022,
  title     = {Advanced Medicinal Plant Classification and Bioactivity Identification Based on Dense Net Architecture},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {6},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Banita Pukhrambam and Arun Sahayadhas},
  doi       = {10.14569/IJACSA.2022.0130614},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130614}
}