Abstract: 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.

Banita Pukhrambam and Arun Sahayadhas, “Advanced Medicinal Plant Classification and Bioactivity Identification Based on Dense Net Architecture” International Journal of Advanced Computer Science and Applications(IJACSA), 13(6), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130614

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