Abstract:
Fisheries is a prominent sector in Bangladesh. It has giant contribution in our economy.
There are many people involved in this sector for their livelihood in this country. There are
lots of principle invested in fisheries. This sector has also some barriers. Infected diseases
are the prime culprit for fish cultivation. These diseases are created havoc situation for fish
cultivation. Infected diseases apparent many problems in fish cultivation, such as decrease
of production, deficit of protein, wracked of cultivator’s investment. So, cultivators need
proper steps from heinous hands of these diseases. Traditional evaluation of these diseases
are very difficult task for cultivators according to time and proper management. Through
computer vision approach technological identification of these diseases is becoming more
effective way day by day. So in our work we expose a sophisticated way to identify the
diseases by which fish are infected. Epizootic Ulcerative Syndrome (EUS) and Tail and
Fin Rot are most common infected diseases of fish cultivation in our country. This
proposed method would be helped to the fisheries sector in Bangladesh to cultivate fish for
effectively and probably increasing fish production by measuring proper way after
technological identification of diseases. K-means clustering is being used for image
segmentation after image preprocessing system is implemented. Ten particular traits are
omitted in our thesis. Multiple classification techniques are used for classification. Random
Forest algorithm gains almost 80.62% proficiency.
