Mobile Application for Classifying Palm Oil Bunch using RGB and Artificial Neural Network
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...
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my.ump.umpir.173172018-03-20T04:24:25Z http://umpir.ump.edu.my/id/eprint/17317/ Mobile Application for Classifying Palm Oil Bunch using RGB and Artificial Neural Network Sayyidatina Al Hurul Aina, Alzahati Mohd Azwan, Mohamad QA75 Electronic computers. Computer science 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%. 2016 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/17317/1/292_298.pdf 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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QA75 Electronic computers. Computer science Sayyidatina Al Hurul Aina, Alzahati Mohd Azwan, Mohamad Mobile Application for Classifying Palm Oil Bunch using RGB and Artificial Neural Network |
description |
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%. |
format |
Conference or Workshop Item |
author |
Sayyidatina Al Hurul Aina, Alzahati Mohd Azwan, Mohamad |
author_facet |
Sayyidatina Al Hurul Aina, Alzahati Mohd Azwan, Mohamad |
author_sort |
Sayyidatina Al Hurul Aina, Alzahati |
title |
Mobile Application for Classifying Palm Oil Bunch using RGB and Artificial Neural Network |
title_short |
Mobile Application for Classifying Palm Oil Bunch using RGB and Artificial Neural Network |
title_full |
Mobile Application for Classifying Palm Oil Bunch using RGB and Artificial Neural Network |
title_fullStr |
Mobile Application for Classifying Palm Oil Bunch using RGB and Artificial Neural Network |
title_full_unstemmed |
Mobile Application for Classifying Palm Oil Bunch using RGB and Artificial Neural Network |
title_sort |
mobile application for classifying palm oil bunch using rgb and artificial neural network |
publishDate |
2016 |
url |
http://umpir.ump.edu.my/id/eprint/17317/1/292_298.pdf http://umpir.ump.edu.my/id/eprint/17317/ |
_version_ |
1643668149439037440 |
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13.18916 |