$Multilayer perceptron neural network classification on RRIM latex timber clone (LTC) series using visible-Nir optical sensing technique on latex / Noor Ezan Abdullah$

Multilayer perceptron neural network classification on RRIM latex timber clone (LTC) series using visible-Nir optical sensing technique on latex / Noor Ezan Abdullah

Since 90 decades ago rubber breeding program has been initiated by Rubber Research Institute of Malaysia in producing the best clone that able to generate high latex yielding and good as timber which is known as RRIM LTC series. The current target by the government also highlighted the focus for mai...

Full description

Saved in:

Bibliographic Details
Main Author:	Abdullah, Noor Ezan
Format:	Thesis
Language:	English
Published:	2020
Subjects:	Rubber industry
Online Access:	https://ir.uitm.edu.my/id/eprint/61062/1/61062.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-uitm-ir.61062
record_format	uketd_dc
spelling	my-uitm-ir.610622022-06-07T07:29:32Z Multilayer perceptron neural network classification on RRIM latex timber clone (LTC) series using visible-Nir optical sensing technique on latex / Noor Ezan Abdullah 2020-05 Abdullah, Noor Ezan Rubber industry Since 90 decades ago rubber breeding program has been initiated by Rubber Research Institute of Malaysia in producing the best clone that able to generate high latex yielding and good as timber which is known as RRIM LTC series. The current target by the government also highlighted the focus for maintaining the upstream sector which is in cultivation and breeding program, as stated in RMK12 and NKEA. Although RRIM had overcome the issue by introducing more than 200 clone‟s series but the hitches in identification these clones still prevailing due to lack of information in reference books and required skill from the expert person. As a parallel to this matter, a mechanism that can identify types of clones recommended for planting without assistance by the experienced worker needed crucially. Therefore, the motivation of this study is to develop a VIS-NIR prototype and an intelligent system for RRIM LTC identification. The latex samples came from five selected clones which consist of RRIM2000 and RRIM3000 series as suggested by verified clone inspectors from MRB based on their high latex yielding and good as timber. The developed sensor consists of three Visible LEDs and a NIR LED as sensing elements. The sensing element will transmit rays on the latex surface and a photodiode will receive the reflected rays from the surface. The measured output of this sensor is in Voltage which represents the reflectance index value. Then, the statistical method used to analyse to obtain particular inference analysis based on VIS-NIR optical properties for clones. The statistical analysis will provide initial findings on the behaviour of the populations based on numerical and graphical information. The second findings are via an automated system using ANN concluded that all clones can discriminate between each other with regards to the VIS-NIR optical properties with 79% accuracy and 91.6% of sensitivity. Meanwhile, the acquired performance from the best-optimized model has been inserted into the MATLAB GUI for validation purposes named Vision Interactive System. Overall, four clones show the accuracy of true prediction ranging from 73% up to 90% while only RRIM2002 able to achieve at least 60%. This infers the develop classifier system is effectively able to recognize the RRIM LTC series. Hence, it can be concluded that all LED voltages can discriminate between clones and these imply that the optical sensing is successfully in producing output voltage represented the reflectance index for VIS-NIR optical properties which can be used for discrimination between clones. The results presented here have proven that the optical properties are suitable in characterizing the clone types. Furthermore, this study may facilitate improvements in the upstream sector for rubber clone series inspection in the electrical engineering perspective. 2020-05 Thesis https://ir.uitm.edu.my/id/eprint/61062/ https://ir.uitm.edu.my/id/eprint/61062/1/61062.pdf text en public phd doctoral Universiti Teknologi MARA Faculty of Electrical Engineering Madzhi, Nina Korlina (Ir. Dr.)
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Madzhi, Nina Korlina (Ir. Dr.)
topic	Rubber industry
spellingShingle	Rubber industry Abdullah, Noor Ezan Multilayer perceptron neural network classification on RRIM latex timber clone (LTC) series using visible-Nir optical sensing technique on latex / Noor Ezan Abdullah
description	Since 90 decades ago rubber breeding program has been initiated by Rubber Research Institute of Malaysia in producing the best clone that able to generate high latex yielding and good as timber which is known as RRIM LTC series. The current target by the government also highlighted the focus for maintaining the upstream sector which is in cultivation and breeding program, as stated in RMK12 and NKEA. Although RRIM had overcome the issue by introducing more than 200 clone‟s series but the hitches in identification these clones still prevailing due to lack of information in reference books and required skill from the expert person. As a parallel to this matter, a mechanism that can identify types of clones recommended for planting without assistance by the experienced worker needed crucially. Therefore, the motivation of this study is to develop a VIS-NIR prototype and an intelligent system for RRIM LTC identification. The latex samples came from five selected clones which consist of RRIM2000 and RRIM3000 series as suggested by verified clone inspectors from MRB based on their high latex yielding and good as timber. The developed sensor consists of three Visible LEDs and a NIR LED as sensing elements. The sensing element will transmit rays on the latex surface and a photodiode will receive the reflected rays from the surface. The measured output of this sensor is in Voltage which represents the reflectance index value. Then, the statistical method used to analyse to obtain particular inference analysis based on VIS-NIR optical properties for clones. The statistical analysis will provide initial findings on the behaviour of the populations based on numerical and graphical information. The second findings are via an automated system using ANN concluded that all clones can discriminate between each other with regards to the VIS-NIR optical properties with 79% accuracy and 91.6% of sensitivity. Meanwhile, the acquired performance from the best-optimized model has been inserted into the MATLAB GUI for validation purposes named Vision Interactive System. Overall, four clones show the accuracy of true prediction ranging from 73% up to 90% while only RRIM2002 able to achieve at least 60%. This infers the develop classifier system is effectively able to recognize the RRIM LTC series. Hence, it can be concluded that all LED voltages can discriminate between clones and these imply that the optical sensing is successfully in producing output voltage represented the reflectance index for VIS-NIR optical properties which can be used for discrimination between clones. The results presented here have proven that the optical properties are suitable in characterizing the clone types. Furthermore, this study may facilitate improvements in the upstream sector for rubber clone series inspection in the electrical engineering perspective.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Abdullah, Noor Ezan
author_facet	Abdullah, Noor Ezan
author_sort	Abdullah, Noor Ezan
title	Multilayer perceptron neural network classification on RRIM latex timber clone (LTC) series using visible-Nir optical sensing technique on latex / Noor Ezan Abdullah
title_short	Multilayer perceptron neural network classification on RRIM latex timber clone (LTC) series using visible-Nir optical sensing technique on latex / Noor Ezan Abdullah
title_full	Multilayer perceptron neural network classification on RRIM latex timber clone (LTC) series using visible-Nir optical sensing technique on latex / Noor Ezan Abdullah
title_fullStr	Multilayer perceptron neural network classification on RRIM latex timber clone (LTC) series using visible-Nir optical sensing technique on latex / Noor Ezan Abdullah
title_full_unstemmed	Multilayer perceptron neural network classification on RRIM latex timber clone (LTC) series using visible-Nir optical sensing technique on latex / Noor Ezan Abdullah
title_sort	multilayer perceptron neural network classification on rrim latex timber clone (ltc) series using visible-nir optical sensing technique on latex / noor ezan abdullah
granting_institution	Universiti Teknologi MARA
granting_department	Faculty of Electrical Engineering
publishDate	2020
url	https://ir.uitm.edu.my/id/eprint/61062/1/61062.pdf
_version_	1783735206090375168

Multilayer perceptron neural network classification on RRIM latex timber clone (LTC) series using visible-Nir optical sensing technique on latex / Noor Ezan Abdullah

Similar Items