1. 425/D Use of Pattern Recognition Techniques to develop an
Automated Plant Identification System for Cryptocoryne walkeri
425/D
W A M P Ariyarathne1 and T L S Tirimanne1*
1 Department of Plant Sciences, Faculty of Science, University of Colombo, Colombo 03, Sri Lanka.
Abstract
This research is aimed at developing a partially automated system based on asexual morphological features such as leaf characteristics using artificial neural network systems (ANN). Collected data were used in an ANN embedded pattern recognition system which was trained to identify Cryptocoryne walkeri specimens from other species belonging to the genus Cryptocoryne.
Introduction
An automated system can be employed for species identification.
Genus Cryptocoryne is a member of the family Araceae with 58 known species of which 10 species are endemic to Sri Lanka. Identification of these species is solely based on morphology of the inflorescence, which makes them difficult to identify at non-flowering stages. Due to rapid removal of these plants from wild for aquarium trade, Flora and Fauna Protection Ordinance of 2009 established four Cryptocoryne species as highly protected, including C. walkeri, prohibiting the collection, transportation and exportation.
Using pattern recognition techniques an ANN was established to identify C. walkeri from other species in the genus Cryptocoryne.
Methodology
Sampling:
Specimens were collected from 8 districts.
Morphological character selection:
15 quantitative (eg. shape of the base,
shape of the apex) and 5 qualitative
(eg. Leaf length : width) characters
Character measurement & data encoding:
To organize the range of measured data an encoding table was used.
Develop the ANN & create Graphical User Interface (GUI):
Parameters of the ANN were set by using trail and error approach and a GUI was created to increase the user friendliness of the identification system.
Sampled Districts
C. walkeri
C. beckettii
C. wendtii
C. thwaitesii
C. bogneri
Results
A partially automated identification system was developed based on ANN
Discussion
Cryptocoryne species exhibited a range of variation depending on the habitat.
By selecting suitable characteristics, an automated user-friendly identification system was developed to discriminate C. walkeri specimens from other species in the genus Cryptocoryne.
Classes
Number of samples
Graphical User Interface
Behavior of the ANN
References
Lu, H., Jiang W., et al. (2012) Classification of Camellia (Theaceae) species using leaf architecture variations and pattern recognition techniques PLoS ONE, 7(1).
Swain, M., Dash, S.K., et al. (2012) An approach for Iris plant classification using neural network. International Journal on Soft Computing (IJS), (1).