eprintid: 627
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/06/27
datestamp: 2023-11-09 15:48:46
lastmod: 2023-11-09 15:48:46
status_changed: 2023-11-09 15:22:50
type: article
metadata_visibility: show
creators_name: Siddiqi, M.H.
creators_name: Sulaiman, S.
creators_name: Faye, I.
creators_name: Ahmad, I.
title: A Real time specific weed discrimination system using multi-Level wavelet decomposition
ispublished: pub
keywords: Euclidia
note: cited By 17
abstract: The developed algorithm was used for the real time specific weed discrimination employing multi-level wavelet decomposition. This algorithm used four different types of wavelets i.e., Daubechies (bd4), Symlets (sym4), Biorthogonal (bior3.3) and Reverse Biorthogonal (rbio3.3) up to four levels of decomposition to classify images into broad and narrow class for real-time selective herbicide application using the Euclidian distance method. The lab, which have shown that the system to be very effective in weed identification, segmentation and discrimination. The test and analysis show that 97.26 classification accuracy over 350 sample images (broad & narrow) with 175 samples from each category of weeds and the proposed algorithm takes 29 ms as average time for the classification of the specific weeds.
date: 2009
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-76149088419&partnerID=40&md5=fb99d53e61775c2e3d08774d36df8643
full_text_status: none
publication: International Journal of Agriculture and Biology
volume: 11
number: 5
pagerange: 559-565
refereed: TRUE
issn: 15608530
citation:   Siddiqi, M.H. and Sulaiman, S. and Faye, I. and Ahmad, I.  (2009) A Real time specific weed discrimination system using multi-Level wavelet decomposition.  International Journal of Agriculture and Biology, 11 (5).  pp. 559-565.  ISSN 15608530