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Table 4: Comparison result of the proposed method on Flavia dataset with the state-of-the-art other methods.
Method Accuracy (%)
Thi-Lan Le,2014 (Nguyen et al., 2017) 97.5
Wu et al., 2007 (Wu et al., 2007) 90.3
Kadir, 2014 (Kadir, 2014)
97.2
Pierre Barr, 2017 (Barr
´
e et al., 2017) 97.9
Serestina Viriri, 2016 (Kala et al., 2016b) 95.0
G-KDES+LBP-KDES+DBoW (Our) 98.0
organs for plant species identification as they remain
available throughout the year, other organs of plant
such as stem and fruits can also be considered for dif-
ferent plant species identification. Furthermore, this
work can also be extended to achieve favorable results
by utilizing deep convolutional neural networks in or-
der to evaluate their ability to identify plant species at
a large-scale.
ACKNOWLEDGEMENTS
The authors would like to acknowledge the support
by the Centre for Computer Vision Research (C
2
VR)
and Swarm Robotics Lab-NCRA, University of Engi-
neering and Technology (UET) Taxila, Pakistan.
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