FISH FRESHNESS CLASSIFICATION USING WAVELET TRANSFORMATION AND FUZZY LOGIC TECHNOLOGY
Main Article Content
Article Sidebar
Abstract
Freshness plays a vital role to measure the quality of fish by consumers. The quality of a fish may be affected by characteristic changes deeply which result in a progressive loss of food characteristic in terms of taste and a general concept of quality. The aim of this study is to classify fish freshness based on image processing by using clustering based methods and its features are extracted in the wavelet transformation domain using Haar filter. First and second level decomposition in the wavelet domain is performed and the coefficients obtained at each level have been used as input to fuzzy logic to achieve this objective. There are three types of fuzzy input methods that have been discussed which are Wavelet Mean values divided into 5 parts as per day, Median absolute deviation values are taken as the range of 3 and RGB Mean also has been taken as 3 ranges which are calculated separately. The experimental result indicates better performance based on fuzzy logic in terms of freshness percentage.
Article Details
COPYRIGHT AGREEMENT AND AUTHORSHIP RESPONSIBILITY
 All paper submissions must carry the following duly signed by all the authors:
“I certify that I have participated sufficiently in the conception and design of this work and the analysis of the data (wherever applicable), as well as the writing of the manuscript, to take public responsibility for it. I believe the manuscript represents valid work. I have reviewed the final version of the manuscript and approve it for publication. Neither has the manuscript nor one with substantially similar content under my authorship been published nor is being considered for publication elsewhere, except as described in an attachment. Furthermore I attest that I shall produce the data upon which the manuscript is based for examination by the editors or their assignees, if requested.â€