Multi sensor system for classifying Harumanis mango based on external and internal quality

This thesis presents a multi sensor system for classifying Harumanis mango based on its external and internal quality. Both external and internal quality of Harumanis mango affects the consumer buying preferences. Current method of classifying Harumanis mango is done manually and destructive for its...

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Format:	Thesis
Language:	English
Subjects:	Image processing Computer vision Mango Harumanis Multi sensor system
Online Access:	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78037/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78037/2/Full%20text.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78037/3/Mohd%20Firdaus.pdf
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record_format	uketd_dc
spelling	my-unimap-780372023-03-07T02:18:04Z Multi sensor system for classifying Harumanis mango based on external and internal quality Ammar, Zakaria, Dr. This thesis presents a multi sensor system for classifying Harumanis mango based on its external and internal quality. Both external and internal quality of Harumanis mango affects the consumer buying preferences. Current method of classifying Harumanis mango is done manually and destructive for its internal quality determination. The proposed system consists of two parts. First part is the external quality classification using machine vision system which is based on its shape and mass. The second part is the internal quality classification using near infrared (NIR) spectroscopy, based on its total soluble solid (TSS) value. An image acquisition platform was built to capture the 3- Dimensional image of Harumanis mango in a single acquisition. A real-time measurement calibration technique was developed in this research. Combination of Fourier descriptor parameters and size-shape parameters was used to recognize the shape of Harumanis mango. An improved two-dimensional disk method was used to estimate the volume of Harumanis mango based on the captures image. Then a correlation between the actual volume and actual mass was derived and used to estimate the mass of Harumanis mango on inline system. The proposed method can correctly classify the Harumanis mango according to its shape and mass 94.2% of the time. NIR spectrometer was used to obtain the reflectance wavelength of the Harumanis mango. The juice from the mango was obtained and measured with a refractrometer to obtain the actual TSS value. Then, the acquired NIR wavelength was analysed and correlate with the actual TSS value using multivariate analysis. A regression value of 0.85 for calibration set was found from the analysis, which explained that there was a high correlation between the wavelength and TSS. Stepwise Discriminant analysis method was used to find the significant wavelength that can be used to determine the maturity stage in real-time system. Ten wavelength points were selected and verified on the testing set. The discriminant model can be accurately determined the maturity stage with 85.0% accuracy Universiti Malaysia Perlis (UniMAP) Thesis en http://dspace.unimap.edu.my:80/xmlui/handle/123456789/78037 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78037/4/license.txt 8a4605be74aa9ea9d79846c1fba20a33 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78037/1/Page%201-24.pdf 9fcb89426fb615df096a4174221ddcf4 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78037/2/Full%20text.pdf a5100f06ea70e071a45c66c7531cf064 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78037/3/Mohd%20Firdaus.pdf baa52ef2f3bc2d888029fbcdfbc5b991 Universiti Malaysia Perlis (UniMAP) Image processing Computer vision Mango Harumanis Multi sensor system School of Mechatronic Engineering
institution	Universiti Malaysia Perlis
collection	UniMAP Institutional Repository
language	English
advisor	Ammar, Zakaria, Dr.
topic	Image processing Computer vision Mango Harumanis Multi sensor system
spellingShingle	Image processing Computer vision Mango Harumanis Multi sensor system Multi sensor system for classifying Harumanis mango based on external and internal quality
description	This thesis presents a multi sensor system for classifying Harumanis mango based on its external and internal quality. Both external and internal quality of Harumanis mango affects the consumer buying preferences. Current method of classifying Harumanis mango is done manually and destructive for its internal quality determination. The proposed system consists of two parts. First part is the external quality classification using machine vision system which is based on its shape and mass. The second part is the internal quality classification using near infrared (NIR) spectroscopy, based on its total soluble solid (TSS) value. An image acquisition platform was built to capture the 3- Dimensional image of Harumanis mango in a single acquisition. A real-time measurement calibration technique was developed in this research. Combination of Fourier descriptor parameters and size-shape parameters was used to recognize the shape of Harumanis mango. An improved two-dimensional disk method was used to estimate the volume of Harumanis mango based on the captures image. Then a correlation between the actual volume and actual mass was derived and used to estimate the mass of Harumanis mango on inline system. The proposed method can correctly classify the Harumanis mango according to its shape and mass 94.2% of the time. NIR spectrometer was used to obtain the reflectance wavelength of the Harumanis mango. The juice from the mango was obtained and measured with a refractrometer to obtain the actual TSS value. Then, the acquired NIR wavelength was analysed and correlate with the actual TSS value using multivariate analysis. A regression value of 0.85 for calibration set was found from the analysis, which explained that there was a high correlation between the wavelength and TSS. Stepwise Discriminant analysis method was used to find the significant wavelength that can be used to determine the maturity stage in real-time system. Ten wavelength points were selected and verified on the testing set. The discriminant model can be accurately determined the maturity stage with 85.0% accuracy
format	Thesis
title	Multi sensor system for classifying Harumanis mango based on external and internal quality
title_short	Multi sensor system for classifying Harumanis mango based on external and internal quality
title_full	Multi sensor system for classifying Harumanis mango based on external and internal quality
title_fullStr	Multi sensor system for classifying Harumanis mango based on external and internal quality
title_full_unstemmed	Multi sensor system for classifying Harumanis mango based on external and internal quality
title_sort	multi sensor system for classifying harumanis mango based on external and internal quality
granting_institution	Universiti Malaysia Perlis (UniMAP)
granting_department	School of Mechatronic Engineering
url	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78037/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78037/2/Full%20text.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78037/3/Mohd%20Firdaus.pdf
_version_	1776104264802762752

Multi sensor system for classifying Harumanis mango based on external and internal quality

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