Intelligent technique for grading tropical fruit using magnetic resonance imaging

Recent application of modern marketing techniques coupled with intelligent agricultural systems of production has transformed small scale farming into large scale, in most part of the world. Characteristically, most of the tropical fruits, such as orange, appeared edible physically but internally su...

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Main Authors:	A. Balogun, Wasiu, Salami, Momoh Jimoh Emiyoka, J. McCarthy, Michael, Mohd Mustafah, Yasir, Aibinu, Abiodun Musa
Format:	Article
Language:	English
Published:	IJSER 2013
Subjects:	T Technology (General)
Online Access:	http://irep.iium.edu.my/35993/ http://irep.iium.edu.my/35993/ http://irep.iium.edu.my/35993/1/4._Intelligent_Technique_for_Grading_Tropical_Fruit_using_Magnetic_Resonance_Imaging.pdf

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spelling	iium-359932014-03-10T07:50:08Z http://irep.iium.edu.my/35993/ Intelligent technique for grading tropical fruit using magnetic resonance imaging A. Balogun, Wasiu Salami, Momoh Jimoh Emiyoka J. McCarthy, Michael Mohd Mustafah, Yasir Aibinu, Abiodun Musa T Technology (General) Recent application of modern marketing techniques coupled with intelligent agricultural systems of production has transformed small scale farming into large scale, in most part of the world. Characteristically, most of the tropical fruits, such as orange, appeared edible physically but internally such fruits might be defective based on their tissue and juice. Eventually, these fruits, via the market and undetected, usually get to the consumers who encounter the unfavourable status of the fruits. Our purpose, in this study, is to develop a non-destructive method to predict the status of orange fruits, based on internal quality. Graph of histogram showing the levels of different four colour intensities were acquired and analysed. The features extracted from Magnetic Resonance Imaging (MRI), using any of the two proposed methods, were applied as an input to train artificial neural network (ANN) in order to predict the orange fruit status. Different structures of multi-layer perceptron neural networks with feed-forward and back-propagation learning algorithms were developed using MATLAB. The theoretical background of MRI and artificial neural network (ANN) backpropagation were also explained. At hidden neuron value of 20, search is for backpropagation and number of neurons in the hidden layer to optimize the ANN. Levenberg-Marquardt algorithm (trainlm) gave the best performance fitness out of different types of backpropagation algorithm used with least Mean Square Error (MSE) of 0.0814 corresponding to R-value of 0.8094. This work shows that ANN and MRI have the capability of predicting the internal content and detect defect fruit based on water proton content. IJSER 2013-07 Article PeerReviewed application/pdf en http://irep.iium.edu.my/35993/1/4._Intelligent_Technique_for_Grading_Tropical_Fruit_using_Magnetic_Resonance_Imaging.pdf A. Balogun, Wasiu and Salami, Momoh Jimoh Emiyoka and J. McCarthy, Michael and Mohd Mustafah, Yasir and Aibinu, Abiodun Musa (2013) Intelligent technique for grading tropical fruit using magnetic resonance imaging. International Journal of Scientific & Engineering Research, 4 (7). pp. 216-225. ISSN 2229-5518 http://www.ijser.org/ResearchPaperPublishing_July2013_Page1.aspx
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	T Technology (General)
spellingShingle	T Technology (General) A. Balogun, Wasiu Salami, Momoh Jimoh Emiyoka J. McCarthy, Michael Mohd Mustafah, Yasir Aibinu, Abiodun Musa Intelligent technique for grading tropical fruit using magnetic resonance imaging
description	Recent application of modern marketing techniques coupled with intelligent agricultural systems of production has transformed small scale farming into large scale, in most part of the world. Characteristically, most of the tropical fruits, such as orange, appeared edible physically but internally such fruits might be defective based on their tissue and juice. Eventually, these fruits, via the market and undetected, usually get to the consumers who encounter the unfavourable status of the fruits. Our purpose, in this study, is to develop a non-destructive method to predict the status of orange fruits, based on internal quality. Graph of histogram showing the levels of different four colour intensities were acquired and analysed. The features extracted from Magnetic Resonance Imaging (MRI), using any of the two proposed methods, were applied as an input to train artificial neural network (ANN) in order to predict the orange fruit status. Different structures of multi-layer perceptron neural networks with feed-forward and back-propagation learning algorithms were developed using MATLAB. The theoretical background of MRI and artificial neural network (ANN) backpropagation were also explained. At hidden neuron value of 20, search is for backpropagation and number of neurons in the hidden layer to optimize the ANN. Levenberg-Marquardt algorithm (trainlm) gave the best performance fitness out of different types of backpropagation algorithm used with least Mean Square Error (MSE) of 0.0814 corresponding to R-value of 0.8094. This work shows that ANN and MRI have the capability of predicting the internal content and detect defect fruit based on water proton content.
format	Article
author	A. Balogun, Wasiu Salami, Momoh Jimoh Emiyoka J. McCarthy, Michael Mohd Mustafah, Yasir Aibinu, Abiodun Musa
author_facet	A. Balogun, Wasiu Salami, Momoh Jimoh Emiyoka J. McCarthy, Michael Mohd Mustafah, Yasir Aibinu, Abiodun Musa
author_sort	A. Balogun, Wasiu
title	Intelligent technique for grading tropical fruit using magnetic resonance imaging
title_short	Intelligent technique for grading tropical fruit using magnetic resonance imaging
title_full	Intelligent technique for grading tropical fruit using magnetic resonance imaging
title_fullStr	Intelligent technique for grading tropical fruit using magnetic resonance imaging
title_full_unstemmed	Intelligent technique for grading tropical fruit using magnetic resonance imaging
title_sort	intelligent technique for grading tropical fruit using magnetic resonance imaging
publisher	IJSER
publishDate	2013
url	http://irep.iium.edu.my/35993/ http://irep.iium.edu.my/35993/ http://irep.iium.edu.my/35993/1/4._Intelligent_Technique_for_Grading_Tropical_Fruit_using_Magnetic_Resonance_Imaging.pdf
first_indexed	2023-09-18T20:51:32Z
last_indexed	2023-09-18T20:51:32Z
_version_	1777410026558717952

Intelligent technique for grading tropical fruit using magnetic resonance imaging

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