Comparison of fully automated and semi-automated methods for species identification

Kalafi, Elham Yousef and Anuar, M.K. and Sakharkar, Meena Kishore and Dhillon, Sarinder Kaur (2018) Comparison of fully automated and semi-automated methods for species identification. Folia Biologica, 64 (4). pp. 137-143. ISSN 0015-5500

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Official URL: https://fb.cuni.cz/file/5878/fb2018a0017.pdf

Abstract

The process of manual species identification is a daunting task, so much so that the number of taxonomists is seen to be declining. In order to assist taxonomists, many methods and algorithms have been proposed to develop semi-automated and fully automated systems for species identification. While semi-automated tools would require manual intervention by a domain expert, fully automated tools are assumed to be not as reliable as manual or semi-automated identification tools. Hence, in this study we investigate the accuracy of fully automated and semi-automated models for species identification. We have built fully automated and semi-automated species classification models using the monogenean species image dataset. With respect to monogeneans’ morphology, they are differentiated based on the morphological characteristics of haptoral bars, anchors, marginal hooks and reproductive organs (male and female copulatory organs). Landmarks (in the semi-automated model) and shape morphometric features (in the fully automated model) were extracted from four monogenean species images, which were then classified using k-nearest neighbour and artificial neural network. In semi-automated models, a classification accuracy of 96.67 % was obtained using the k-nearest neighbour and 97.5 % using the artificial neural network, whereas in fully automated models, a classification accuracy of 90 % was obtained using the k-nearest neighbour and 98.8 % using the artificial neural network. As for the cross-validation, semi-automated models performed at 91.2 %, whereas fully automated models performed slightly higher at 93.75 %.

Item Type: Article
Uncontrolled Keywords: Artificial neural networks; Automated species identification; Classification; Image processing; K-nearest neighbour; Monogenean
Subjects: Q Science > Q Science (General)
Q Science > QH Natural history
Divisions: Faculty of Science > Institute of Biological Sciences
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 31 Jul 2019 02:25
Last Modified: 31 Jul 2019 02:25
URI: http://eprints.um.edu.my/id/eprint/21716

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