Identification and Classification of Animal Kingdom using Image Processing and Artificial Neural Networks
K. Sujatha1 , V. Srividhya2, M. Aruna3
1K. Sujatha , Professor, EEE Dept., Dr.MGR Educational and Research Institute, Chennai ,Tamil Nadu, India.
2V. Srividhya, Asst. Prof., EEE Dept., Meenakshi College of Engineering, India.
3M. Aruna, Asst. Prof., EEE Dept., Meenakshi College of Engineering, Tamil Nadu, India.
Manuscript received on 01 August 2019. | Revised Manuscript received on 07 August 2019. | Manuscript published on 30 September 2019. | PP: 4645-4650 | Volume-8 Issue-3 September 2019 | Retrieval Number: C6840098319/2019©BEIESP | DOI: 10.35940/ijrte.C6840.098319
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The biological kingdom ‘Animalia’ is composed of multi cellular eukaryotic organisms. Most of the animal species exhibit bilateral symmetry. The hierarchy of biological classification has eight taxonomy ranks. The top position in the hierarchy is occupied by the ‘domain’ and ending with the lowest position occupied by ‘species’. The classification of animal kingdom includes, Porifera, Coelenterata, Platyhelminthes, Aschelminthes, Annelida, Arthropoda, Mollusca, Echinodermata and Chordata. Manual identification of Phylum or class for each and every species, is very tedious, because there exists nearly a millions of species categorized under various classes. Hence an automated system is proposed to be developed using image segmentation and Artificial Neural Networks (ANN) trained with Back Propagation Algorithm (BPA) which is capable of assisting the scientists and researchers for class identification. This system will be useful in Museums and Archeological departments, where a huge variety of species are maintained. The classification efficiency of the proposed system is 89.1%.
Keywords: Animal Kingdom, Phylum, Image processing, Artificial Neural Networks, Back Propagation Algorithm.
Scope of the Article: Classification