Welcome To UTPedia

We would like to introduce you, the new knowledge repository product called UTPedia. The UTP Electronic and Digital Intellectual Asset. It stores digitized version of thesis, dissertation, final year project reports and past year examination questions.

Browse content of UTPedia using Year, Subject, Department and Author and Search for required document using Searching facilities included in UTPedia. UTPedia with full text are accessible for all registered users, whereas only the physical information and metadata can be retrieved by public users. UTPedia collaborating and connecting peoples with university’s intellectual works from anywhere.

Disclaimer - Universiti Teknologi PETRONAS shall not be liable for any loss or damage caused by the usage of any information obtained from this web site.Best viewed using Mozilla Firefox 3 or IE 7 with resolution 1024 x 768.

Fruit Categorization Technique by using Fuzzy Logic and Neural Network

Mohd Nordin, Siti Nooratiqah (2014) Fruit Categorization Technique by using Fuzzy Logic and Neural Network. IRC, Universiti Teknologi PETRONAS. (Unpublished)

[img]
Preview
PDF
Download (2410Kb) | Preview

Abstract

Before fruits can be issued to the consumers, the fruits will be going through thorough processes and one of the processes is grading. The fruits will be graded according to the standard. The standard is based on the fruits’ country of origin (Malaysian Standard, MS and FAMA Standard). This project is a Matlab simulation of fruits categorization (grading) using artificial intelligent (AI) technique (Fuzzy Logic and Artificial Neural Network) in order to overcome problems faced on the existing system or current method. It is also to ease, fasten the process of fruit grading, and produce consistent and accurate result. Since there are numerous types of fruits, this project will only be focusing on the grading of mangoes, papayas and starfruits or carambola. The input of the system will be the properties that needed to determine the grade of the fruits such as weight, color, shape and the exterior condition of the fruits (defect). Rather than using hardware such as scanner, camera to automatically detect or to give input to the system, the input of the system will be manually keyed in by user. The data of the input will be processed using Matlab Fuzzy logic (FL) and Neural Network (NN) toolbox. The system will process the input with the reference data programmed in the system. The output of the system will be the grade and size of the fruit.

Item Type: Final Year Project
Academic Subject : Academic Department - Electrical And Electronics - Pervasisve Systems - Digital Electronics - Design
Subject: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Engineering > Electrical and Electronic
Depositing User: Ahmad Suhairi Mohamed Lazim
Date Deposited: 24 Feb 2015 10:45
Last Modified: 25 Jan 2017 09:36
URI: http://utpedia.utp.edu.my/id/eprint/14754

Actions (login required)

View Item View Item

Document Downloads

More statistics for this item...