Empirical evidence on identification of RRIM latex timber clone (LTC) series based on VIS - NIR optical sensing for medium harvesting season

Rubber industry in Malaysia through MRB has been supported by the government via its fifth NKEA with the focus is maintaining the upstream sector. Hence as being parallel with that target, the aim of this research work is to achieve one of the upstream sector plan which is in cultivation and breedin...

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Published in:2017 6th International Conference on Electrical Engineering and Informatics (ICEEI) pp. 1 - 6
Main Authors: Abdullah, Noor Ezan, Madzhi, Nina Korlina, Hashim, Hadzli, Ismail, Faridatul Aima, Rosli, Anis Diyana, Abu Hassan, Muhammad Azeen, Yussuf, Ummu Raihan, Ab Rahim, A'zraa Afhzan
Format: Conference Proceeding
Language:English
Published: IEEE 01-11-2017
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Summary:Rubber industry in Malaysia through MRB has been supported by the government via its fifth NKEA with the focus is maintaining the upstream sector. Hence as being parallel with that target, the aim of this research work is to achieve one of the upstream sector plan which is in cultivation and breeding section. This paper describes the investigation on classification of RRIM LTC series based on its VIS-NIR LEDs using an optical sensing technique. There are five selected rubber clone series from RRIM2000 and RRIM3000 which have been chosen based on its high production in latex and rubber wood as recommended by MRB. There are 2400 latex samples collected from RRIM Kota Tinggi during mid yielding season. There are three visible LED - one NIR LED was used as the sensing element for measuring the reflected light projected by the samples and received by a photodiode. The output of this sensor is a reflectance index in terms of voltage. Then, the obtained measurement will be analysed using SPSS for acquiring the inference analysis based on these optical properties on the clones. Findings have concluded that all clones are able to be discriminated amongst each other with regards to the VIS-NIR Optical properties. This outcome will be a guideline for other researchers in developing a smart and intelligent system for recognizing rubber clone series in future.
ISSN:2155-6830
DOI:10.1109/ICEEI.2017.8312369