Meat color recognition using machine vision

Nai Chian, Voravit (2012) Meat color recognition using machine vision. Masters thesis, Universiti Tun Hussein Onn Malaysia.


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New technologies are being developed to give an ease to the human in a variety of different field each and every day. Food industry is the key of development that led to the rise of human civilization. The development of food industry dealt with the husbandry of domesticated animal and plants creating food surpluses that enabled the development of more densely populated and stratified societies. The study of food is very important that improves the quality of human's life. When it comes to classify and grade a meat, the color of fresh meat is a sensory indicator of which affects the consumers behavior, especially the consistency of meat color and musculature. Other factors that influence consumers purchasing include security, nutrition and taste. There has been no report that grades the meat freshness in the process of meat delivery. Most of the meat freshness is grading manually by using the human eyesight at the meat's color and quantity of fats. A parameter to show the freshness of meat has only been analyzed manually using a human's eyes. This is some kind of difficult method when making a right decision whether the meat is fresh or not. In order to overcome this problem, meat grading method has been studied to show the mathematical calculation on the change of color hue, saturation, and intensity (HSI) values. This study focuses on grading system design that helps to characterize the meat freshness according to its color. Using a MATLAB Graphical User Interface (GUI) program, it can analyzes the color of the meat that being inspected. The theory of this program includes the calculation of the mean values and histograms, and the final result. This system is capable of classifying meat freshness.

Item Type: Thesis (Masters)
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TA Engineering (General). Civil engineering (General) > TA1501-1820 Applied optics. Photonics
Divisions: Faculty of Electrical and Electronic Engineering > Department of Electrical Engineering
Depositing User: Mrs. Sabarina Che Mat
Date Deposited: 03 Feb 2022 01:49
Last Modified: 03 Feb 2022 01:49

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