Identification and classification of animal kingdom using image processing and artificial neural networks

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%.