@article {10.3844/jcssp.2021.1210.1221,
article_type = {journal},
title = {Medicinal Plant Identification using Gabor Filters and Deep Learning Techniques: A Paper Review},
author = {Oppong, Stephen Opoku and Twum, Frimpong and Hayfron-Acquah, James Ben and Missah, Yaw Marfo},
volume = {17},
number = {12},
year = {2021},
month = {Dec},
pages = {1210-1221},
doi = {10.3844/jcssp.2021.1210.1221},
url = {https://thescipub.com/abstract/jcssp.2021.1210.1221},
abstract = {Computer-aided identification of plants is a branch of machine learning that has become more recognized recently and proves itself as a vital tool in numerous sectors including pharmacological science, forestry and agriculture. This has essentially generated a zeal in creating automated systems for the identification of diverse species of plants. This study reviewed plant species classification relying on leaf textural features using Gabor filters and revealed that Gabor filters perform better when combined with other feature extraction methods. Therefore, this study proposes using Log-Gabor filter in the field of plant identification to improve accuracy since they overcome the drawbacks of Gabor filters which are; the maximum bandwidth of a Gabor filter is limited to approximately one octave and Gabor filters are not optimal if one is seeking broad spectral information with maximal spatial localization.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}