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...
| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2016
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/17317/ http://umpir.ump.edu.my/id/eprint/17317/1/292_298.pdf |
| Summary: | 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%. |
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