Automated identification of copepods using digital image processing and artificial neural network
| Autor: | Lee Kien Leow, Ving Ching Chong, Li Lee Chew, Sarinder Kaur Dhillon |
|---|---|
| Jazyk: | angličtina |
| Předmět: |
Artificial Neural Network
copepods Databases Factual Biology digital image processing Biochemistry Copepoda Food chain Automation Species level Structural Biology Digital image processing Image Processing Computer-Assisted Animals automated image recognition Molecular Biology Artificial neural network business.industry Applied Mathematics Research Community structure Discriminant Analysis Pattern recognition biology.organism_classification Linear discriminant analysis Computer Science Applications Identification (biology) Artificial intelligence Neural Networks Computer business Copepod |
| Zdroj: | BMC Bioinformatics |
| ISSN: | 1471-2105 |
| DOI: | 10.1186/1471-2105-16-s18-s4 |
| Popis: | Background Copepods are planktonic organisms that play a major role in the marine food chain. Studying the community structure and abundance of copepods in relation to the environment is essential to evaluate their contribution to mangrove trophodynamics and coastal fisheries. The routine identification of copepods can be very technical, requiring taxonomic expertise, experience and much effort which can be very time-consuming. Hence, there is an urgent need to introduce novel methods and approaches to automate identification and classification of copepod specimens. This study aims to apply digital image processing and machine learning methods to build an automated identification and classification technique. Results We developed an automated technique to extract morphological features of copepods' specimen from captured images using digital image processing techniques. An Artificial Neural Network (ANN) was used to classify the copepod specimens from species Acartia spinicauda, Bestiolina similis, Oithona aruensis, Oithona dissimilis, Oithona simplex, Parvocalanus crassirostris, Tortanus barbatus and Tortanus forcipatus based on the extracted features. 60% of the dataset was used for a two-layer feed-forward network training and the remaining 40% was used as testing dataset for system evaluation. Our approach demonstrated an overall classification accuracy of 93.13% (100% for A. spinicauda, B. similis and O. aruensis, 95% for T. barbatus, 90% for O. dissimilis and P. crassirostris, 85% for O. similis and T. forcipatus). Conclusions The methods presented in this study enable fast classification of copepods to the species level. Future studies should include more classes in the model, improving the selection of features, and reducing the time to capture the copepod images. |
| Databáze: | OpenAIRE |
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