Spot Disease Identification using unsupervised Machine Learning based Image Segmentation with its Remedial Solution in Aquatic Fauna

Abstract

Spot Diseases are pre-eminent agent affecting fish mortality contribute substantial losses to the farmers. This research project is monopolizing to detect and discern different kinds of spots in aquatic fauna. This study gives the analytical data about how this spot disease has been caused and how these spots can be identified by taking fifteen species of aquatic fauna with the help of image processing method. Now a day’s fish mortality is increased due to the spread of spot diseases. Thus, it is ineluctable to develop advance techniques to discover different kinds of spots so that dwindling fish mortality. Generally, three kinds of spot diseases have been found. They are black spot diseases, white spot diseases and red spot diseases. The enactment of image processing method in the fisheries efficacious which proffer fish protection, improvement in aquaculture. Canonical detection of spot plays a major role in its treatment. Clustering plays a major role in Image object segmentation both in Gray and RGB in this paper, appraisal of spot in aquaculture is done by maneuvring various image processing techniques. This research allocates facile, sharp-witted and consummate result of detecting spot and recognition in aquaculture.