eprintid: 652
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/06/52
datestamp: 2023-11-09 15:48:47
lastmod: 2023-11-09 15:48:47
status_changed: 2023-11-09 15:22:53
type: conference_item
metadata_visibility: show
creators_name: Siddiqi, M.H.
creators_name: Ahmad, I.
creators_name: Bt Sulaiman, S.
title: Weed recognition based on erosion and dilation segmentation algorithm
ispublished: pub
keywords: Component; Image classifier; Ranodom Transform; Real-time weed recognition; Weed detection; Weed segmentation, Algorithms; Classifiers; Computer vision; Control systems; Herbicides; Image analysis; Image classification; Imaging systems; Learning systems; Object recognition; Robotics, Weed control
note: cited By 29; Conference of 2009 International Conference on Education Technology and Computer, ICETC 2009 ; Conference Date: 17 April 2009 Through 20 April 2009; Conference Code:78125
abstract: Farmer needs alternatives for weed control due to the desire to reduce chemicals used in farming. However, conventional mechanical cultivation cannot selectively remove weeds and there are no selective herbicides for some weed situation. Since hand labor is costly, an automated weed control system could be feasible. A robotic weed control system can also reduce or eliminate the need for chemicals. Many attempts have been made to develop efficient algorithms for recognition and classification. Currently research is going on for developing new machine vision algorithms for automatic recognition and classification of many divers object groups. In this paper an algorithm is developed for automatic spray control system. The algorithm is based on erosion followed by dilation segmentation algorithm. This algorithm can detect weeds and also classify it. Currently the algorithm is tested on two types of weeds i.e. broad and narrow. The developed algorithm has been tested on these two types of weeds in the lab, which gives a very reliable performance. The algorithm is applied on 240 images stored in a database in the lab, of which 100 images were taken from broad leaf weeds and 100 were taken from narrow leaf weeds, and the remaining 40 were taken from no or little weeds. The result showed over 89 results © 2009 IEEE.
date: 2009
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-70449587292&doi=10.1109%2fICETC.2009.62&partnerID=40&md5=7ace7006aca099397e316b15ee1cc53c
id_number: 10.1109/ICETC.2009.62
full_text_status: none
publication: 2009 International Conference on Education Technology and Computer, ICETC 2009
place_of_pub: Singapore
pagerange: 224-228
refereed: TRUE
isbn: 9780769536095
citation:   Siddiqi, M.H. and Ahmad, I. and Bt Sulaiman, S.  (2009) Weed recognition based on erosion and dilation segmentation algorithm.  In: UNSPECIFIED.