A fuzzy approach to identify fish red spot disease

Abstract

PurposeFish are considered as one of the important aquatic animals in the planet. They play a vital role in the nutrient cycle. They can be considered as one of the healthy food for human beings. They can also act as a solution for some of the human health problems. If fish are affected by several diseases, they in turn provide an adverse effect on human health. Therefore, it is very much essential to protect fish from being affected by any diseases.Design/methodology/approachThis paper is mainly focused on the identification of the red spot diseased area in fish. In this work, a fuzzy rule based method (FRBAM) and triangular membership function (TMFN) is used to identify the red spot disease (RSD) in the fish by analyzing several red spot diseased fish (RSDF) images. The canny edge detector is used for intermediate processing of RSDF images.FindingsThe proposed method is able to identify the red pixels over the fish by marking the affected area with red color by using a standard RGB model.Originality/valueThe proposed method follows FRBAM and TMFN in order to detect the RSD and canny edge detector for processing of RSDF images. Finally, it is tested using ten different image sizes and the results show its better performance in terms of detection of RSD affected regions of fish and execution time.

A fuzzy rule based machine intelligence model for cherry red spot disease detection of human eyes in IoMT

“…Fuzzy rule based mechanism can be used to predict the output by analyzing several input features. So, both MI and Fuzzy rule based approach can be used as a solution for the analysis of several patient data in the health care system which can accelerate the process of diagnosis [13][14][15].…”