Improved statistical recognition algorithms for oil palm ripeness identification

Awareness for high quality crude oil is crucial in oil palm production. Proper grading process is important to ensure only the ripe fruits are taken into consideration for the maximum level of oil content. Currently, researchers focus mainly on providing an automatic grading system using various tec...

Full description

Saved in:

Bibliographic Details
Main Author:	Mohamad, Fatma Susilawati
Format:	Thesis
Language:	English
Published:	2012
Subjects:	HD Industries Land use Labor
Online Access:	http://eprints.utm.my/id/eprint/33732/1/FatmaSusilawatiMohamadPFSKSM2012.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-utm-ep.33732
record_format	uketd_dc
spelling	my-utm-ep.337322017-09-21T05:57:31Z Improved statistical recognition algorithms for oil palm ripeness identification 2012-11 Mohamad, Fatma Susilawati HD Industries. Land use. Labor Awareness for high quality crude oil is crucial in oil palm production. Proper grading process is important to ensure only the ripe fruits are taken into consideration for the maximum level of oil content. Currently, researchers focus mainly on providing an automatic grading system using various techniques such as producing digital numbers, oil palm colorimeter, photogrammetric grading, fuzzy or neuro-fuzzy technique and so on. Even though some of them have more than 85% accuracy, it is only valid in controlled environment. However, when they are applied in real situation with uncontrolled environment, the accuracy can drop to less than 50%. So far, there is limited study on suitable colour model conducted on oil palm ripeness identification. Most researchers use RGB colour model to determine an oil palm ripeness. This research looks into the suitability and performance of HSV colour model in classifying an oil palm ripeness. Distance Measurement and Linear Discriminant Analysis are chosen as methods to classify an oil palm ripeness in this study. Histogram is used as a feature vector for feature extraction method while colour as a feature to be analysed. Images of oil palm were captured by an expert in the form of JPEG images. Preprocessing is then performed to remove noise and background from the images. Subsequently, images are transformed into histogram and mean value are extracted. Selected Distance Measurement such as Euclidean Distance, Nearest Neighbour, Furthest Neighbour and Mean Distance are then used for feature matching process. An Oil Palm Ripeness Identification algorithm is proposed, wherein an elimination technique is also introduced in the process. In addition, a Multiple Features Technique is also proposed to find the best feature which brings a very good recognition rate for selected Distance Measurement. The results show that 98% accuracy have been obtained in comparison with other researchers’ work. 2012-11 Thesis http://eprints.utm.my/id/eprint/33732/ http://eprints.utm.my/id/eprint/33732/1/FatmaSusilawatiMohamadPFSKSM2012.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:69899?site_name=Restricted Repository phd doctoral Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	HD Industries Land use Labor
spellingShingle	HD Industries Land use Labor Mohamad, Fatma Susilawati Improved statistical recognition algorithms for oil palm ripeness identification
description	Awareness for high quality crude oil is crucial in oil palm production. Proper grading process is important to ensure only the ripe fruits are taken into consideration for the maximum level of oil content. Currently, researchers focus mainly on providing an automatic grading system using various techniques such as producing digital numbers, oil palm colorimeter, photogrammetric grading, fuzzy or neuro-fuzzy technique and so on. Even though some of them have more than 85% accuracy, it is only valid in controlled environment. However, when they are applied in real situation with uncontrolled environment, the accuracy can drop to less than 50%. So far, there is limited study on suitable colour model conducted on oil palm ripeness identification. Most researchers use RGB colour model to determine an oil palm ripeness. This research looks into the suitability and performance of HSV colour model in classifying an oil palm ripeness. Distance Measurement and Linear Discriminant Analysis are chosen as methods to classify an oil palm ripeness in this study. Histogram is used as a feature vector for feature extraction method while colour as a feature to be analysed. Images of oil palm were captured by an expert in the form of JPEG images. Preprocessing is then performed to remove noise and background from the images. Subsequently, images are transformed into histogram and mean value are extracted. Selected Distance Measurement such as Euclidean Distance, Nearest Neighbour, Furthest Neighbour and Mean Distance are then used for feature matching process. An Oil Palm Ripeness Identification algorithm is proposed, wherein an elimination technique is also introduced in the process. In addition, a Multiple Features Technique is also proposed to find the best feature which brings a very good recognition rate for selected Distance Measurement. The results show that 98% accuracy have been obtained in comparison with other researchers’ work.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Mohamad, Fatma Susilawati
author_facet	Mohamad, Fatma Susilawati
author_sort	Mohamad, Fatma Susilawati
title	Improved statistical recognition algorithms for oil palm ripeness identification
title_short	Improved statistical recognition algorithms for oil palm ripeness identification
title_full	Improved statistical recognition algorithms for oil palm ripeness identification
title_fullStr	Improved statistical recognition algorithms for oil palm ripeness identification
title_full_unstemmed	Improved statistical recognition algorithms for oil palm ripeness identification
title_sort	improved statistical recognition algorithms for oil palm ripeness identification
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2012
url	http://eprints.utm.my/id/eprint/33732/1/FatmaSusilawatiMohamadPFSKSM2012.pdf
_version_	1747816171569152000

Improved statistical recognition algorithms for oil palm ripeness identification

Similar Items