Weed classification using decision tree

Please use this identifier to cite or link to this item: https://ptsldigital.ukm.my/jspui/handle/123456789/394988

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DC Field	Value	Language
dc.contributor.author	Asnor Juraiza lshak	-
dc.contributor.author	Nooritawati Md Tahir	-
dc.contributor.author	Aini Hussain	-
dc.contributor.author	Mohd Marzuki Mustafa	-
dc.date.accessioned	2023-06-15T07:53:18Z	-
dc.date.available	2023-06-15T07:53:18Z	-
dc.identifier.other	ukmvital:122670	-
dc.identifier.uri	https://ptsldigital.ukm.my/jspui/handle/123456789/394988	-
dc.description.abstract	In this paper, the potential of Decision Tree specifically the CART algorithm for classification of weed into two categories namely broad and narrow is employed. Six feature vectors extracted via the Gabor wavelet along with the FFT technique serves as the CART inputs. Based on accuracy rate achieved and selection of optimal feature vectors, the CART algorithm is apt as classifier for weed recognition.	-
dc.language.iso	eng	-
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE),Piscataway, US	-
dc.subject	Weed classification	-
dc.title	Weed classification using decision tree	-
dc.type	Seminar Papers	-
dc.format.pages	5	-
dc.identifier.callno	T58.5.C634 2008 kat sem j.2	-
dc.contributor.conferencename	International Symposium on Information Technology	-
dc.coverage.conferencelocation	Kuala Lumpur Convention Centre	-
dc.date.conferencedate	26/08/2008	-
Appears in Collections:	Seminar Papers/ Proceedings / Kertas Kerja Seminar/ Prosiding

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