A Novel Approach to Fish Disease Diagnostic System based on Machine Learning

  • Shaveta Malik Lingaya's University,Faridabad
  • Tapas Kumar Associate Dean, School of Computer Science, Lingaya’s University, Faridabad, Haryana
  • A.K Sahoo Scientist, Biodiversity Lab, Reverie Ecology and Fisheries Division, Barrackpore, Kolkata
Keywords: Epizootic Ulcerative syndrome (EUS), Principle component analysis (PCA), Features from Accelerated Segment Test (FAST), Neural Network

Abstract

Real-Time identification automated system diagnoses fish disease i.e. Epizootic Ulcerative syndrome (EUS) which is caused by Aphanomyces invadans, a fungal pathogen. In this paper we propose a Real-Time fish disease diagnose system with better accuracy. In order to improve the accuracy we propose a combination (PCA-FAST-NN) which combine the  Principle component analysis (PCA) with Features from Accelerated Segment Test (FAST)feature detector using Machine Learning Algorithm(Neural Network) i.e. (PCA-FAST-NN) .The Experimentation has been done on the real images of  Epizootic Ulcerative syndrome (EUS) infected fish database and implemented in MATLAB environment.

Author Biography

Shaveta Malik, Lingaya's University,Faridabad
Computer Science & Engineering Department

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Published
2017-03-11