Automatic Recognition of Fish Diseases in Fish Farms

Abstract

Fish diseases are the major cause for increasing mortality in fish farms. Automatic identification of diseased fish at early stages is necessary step to prevent spreading disease. Fish disease diagnosis suffers from some limitations that need high level of expertise to be resolved. Recognition of fish abnormal behaviors helps in early prediction of fish diseases. Fish behavior is evaluated by analyzing fish trajectories in videos. Abnormalities may be due to environmental changes. This paper introduces a survey on what computer vision techniques propose in that field. A comprehensive comparison between different automatic recognition systems is included. Finally, our approach is proposed to automatically recognize and identify three different types of fish diseases. These diseases are Epizootic ulcerative syndrome (EUS), Ichthyophthirius (Ich) and Columnaris. Our approach shows the effect of different color spaces on the Convolutional Neural Networkk CNN final performance.