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

Title:

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

Creator:

Kalafi, E. Y., Anuar, M. K., Sakharkar, M. K., and Dhillon, S. K.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:01a5f115-efa0-438b-a53c-b22679233098
uuid:01a5f115-efa0-438b-a53c-b22679233098

Subject:

monogenean, automated species identification, image processing, classification, artificial neural networks, and k-nearest neighbour

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

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, an-chors, 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 %. and Corresponding author: Sarinder Kaur A/p Kashmir Singh

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Coverage:

[137]-143

Source:

Folia biologica | 2018 Volume:64 | Number:4

Harvested from:

CDK

Metadata only:

false