Mobile Application for Classifying Palm Oil Bunch using RGB and Artificial Neural Network

Sayyidatina Al Hurul Aina, Alzahati and Mohd Azwan, Mohamad (2016) Mobile Application for Classifying Palm Oil Bunch using RGB and Artificial Neural Network. In: Proceeding of International Competition and Exhibition on Computing Innovation 2016 , 6-8 December 2016 , University Sports Complex, Universiti Malaysia Pahang. pp. 292-298.. ISBN 978-967-2054-04-7


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This project presents palm oil bunch ripeness classification application based on RGB colour model using Artificial Neural Network (ANN) and developed by using MATLAB for data set training purpose using Backpropagation techniques which it is a part of ANN. An Android application is constructed to test the capability of the trained ANN model in order to classify the ripeness of the palm oil bunch correctly. The captured image of the palm oil bunch is resized and its RGB colour components are extracted to get the individual mean of Red, Green and Blue value as the data set. Further, the data set is normalized and colour conversion techniques are applied. After the conversion, the data set then trained by using ANN. A graphical user interface system is developed in MATLAB for training and Android that classifies the ripeness of the palm oil bunch. The proposed model has an accuracy of 96%.

Item Type: Conference or Workshop Item (Lecture)
Uncontrolled Keywords: Palm Oil Bunch, RGB, Artificial Neural Network, Android, MATLAB, Backpropagation
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty/Division: Faculty of Computer System And Software Engineering
Depositing User: Noorul Farina Arifin
Date Deposited: 24 Mar 2017 01:54
Last Modified: 20 Mar 2018 04:24
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