Oil Plam Fruit Classification Using Spectrometer.

Artificial neural network and linear discriminant analysis were used to detect the ripeness of oil palm fruit bunches. The proposed classification scheme categorized the oil palm fruits into three classes, namely, overripe, ripe, and under-ripe. Fruit color, presumed to be an important indicator of...

Full description

Saved in:

Bibliographic Details
Main Authors:	Mohamed Shariff, Abd Rashid, Mahmud, Ahmad Rodzi, Aouache, Mustapha
Format:	Article
Language:	English English
Published:	2013
Online Access:	http://psasir.upm.edu.my/id/eprint/28408/1/Oil%20Plam%20Fruit%20Classification%20Using%20Spectrometer.pdf http://psasir.upm.edu.my/id/eprint/28408/
Tags:	Add Tag No Tags, Be the first to tag this record!
Institution:	Universiti Putra Malaysia
Language:	English English

id	my.upm.eprints.28408
record_format	eprints
spelling	my.upm.eprints.284082015-10-08T06:35:40Z http://psasir.upm.edu.my/id/eprint/28408/ Oil Plam Fruit Classification Using Spectrometer. Mohamed Shariff, Abd Rashid Mahmud, Ahmad Rodzi Aouache, Mustapha Artificial neural network and linear discriminant analysis were used to detect the ripeness of oil palm fruit bunches. The proposed classification scheme categorized the oil palm fruits into three classes, namely, overripe, ripe, and under-ripe. Fruit color, presumed to be an important indicator of the ripeness of oil palm fruits, was measured with the aid of a FieldSpec 3 Hi-Res spectroradiometer in the wavelength range of 400 nm to 1000 nm. The results were then compared with the classifications made by a trained human grader. 2013 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/28408/1/Oil%20Plam%20Fruit%20Classification%20Using%20Spectrometer.pdf Mohamed Shariff, Abd Rashid and Mahmud, Ahmad Rodzi and Aouache, Mustapha (2013) Oil Plam Fruit Classification Using Spectrometer. Advanced Science Letters, 19 (9). pp. 2651-2653. ISSN 1936-6612 10.1166/asl.2013.5004 English
institution	Universiti Putra Malaysia
building	UPM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Putra Malaysia
content_source	UPM Institutional Repository
url_provider	http://psasir.upm.edu.my/
language	English English
description	Artificial neural network and linear discriminant analysis were used to detect the ripeness of oil palm fruit bunches. The proposed classification scheme categorized the oil palm fruits into three classes, namely, overripe, ripe, and under-ripe. Fruit color, presumed to be an important indicator of the ripeness of oil palm fruits, was measured with the aid of a FieldSpec 3 Hi-Res spectroradiometer in the wavelength range of 400 nm to 1000 nm. The results were then compared with the classifications made by a trained human grader.
format	Article
author	Mohamed Shariff, Abd Rashid Mahmud, Ahmad Rodzi Aouache, Mustapha
spellingShingle	Mohamed Shariff, Abd Rashid Mahmud, Ahmad Rodzi Aouache, Mustapha Oil Plam Fruit Classification Using Spectrometer.
author_facet	Mohamed Shariff, Abd Rashid Mahmud, Ahmad Rodzi Aouache, Mustapha
author_sort	Mohamed Shariff, Abd Rashid
title	Oil Plam Fruit Classification Using Spectrometer.
title_short	Oil Plam Fruit Classification Using Spectrometer.
title_full	Oil Plam Fruit Classification Using Spectrometer.
title_fullStr	Oil Plam Fruit Classification Using Spectrometer.
title_full_unstemmed	Oil Plam Fruit Classification Using Spectrometer.
title_sort	oil plam fruit classification using spectrometer.
publishDate	2013
url	http://psasir.upm.edu.my/id/eprint/28408/1/Oil%20Plam%20Fruit%20Classification%20Using%20Spectrometer.pdf http://psasir.upm.edu.my/id/eprint/28408/
_version_	1643829456331079680

Oil Plam Fruit Classification Using Spectrometer.

Similar Items