Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/21357
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dc.contributor.authorSadgrove, Edmunden
dc.contributor.authorFalzon, Gregoryen
dc.contributor.authorMiron, David Jen
dc.contributor.authorLamb, Daviden
dc.date.accessioned2017-06-14T15:19:00Z-
dc.date.issued2017-
dc.identifier.citationComputers and Electronics in Agriculture, v.139, p. 204-212en
dc.identifier.issn1872-7107en
dc.identifier.issn0168-1699en
dc.identifier.urihttps://hdl.handle.net/1959.11/21357-
dc.description.abstractObject detection is an essential function of robotics based agricultural systems and many algorithms exist for this purpose. Colour although an important characteristic is often avoided in place of faster grey-scale implementations or is only used in an rudimentary arrangement. This study presents the Colour Feature Extreme Learning Machine (CF-ELM), which is an implementation of the extreme learning machine (ELM), with a partially connected hidden layer and a fully connected output layer, taking three colour inputs instead of the standard grey-scale input. The CF-ELM was tested with three different colour systems including HSV, RGB and Y'UV and compared for time and accuracy against the standard greyscale ELM. The four implementations were tested on three different datasets including weed detection, vehicle detection and stock detection. It was found that the colour implementation performed better overall for all three datasets and the Y'UV was best performing colour system on all tested datasets. With the Y'UV delivering the highest accuracy in weed detection at 84%, 96% in vehicle detection and 86% in stock detection. Along side the CF-ELM, an algorithm is introduced for desktop based classification of objects within a pastoral landscape, with individual speeds between 0.06s and 0.18s for a single image, tested within each colour space. The algorithm is designed for use in a scenario that provides difficult and unpredictable terrain, making it ideal for use in an agricultural or pastoral landscape.en
dc.languageenen
dc.publisherElsevier BVen
dc.relation.ispartofComputers and Electronics in Agricultureen
dc.titleFast object detection in pastoral landscapes using a Colour Feature Extreme Learning Machineen
dc.typeJournal Articleen
dc.identifier.doi10.1016/j.compag.2017.05.017en
dc.subject.keywordsAgricultural Spatial Analysis and Modellingen
dc.subject.keywordsComputer Graphicsen
dc.subject.keywordsComputer Visionen
local.contributor.firstnameEdmunden
local.contributor.firstnameGregoryen
local.contributor.firstnameDavid Jen
local.contributor.firstnameDaviden
local.subject.for2008070104 Agricultural Spatial Analysis and Modellingen
local.subject.for2008080103 Computer Graphicsen
local.subject.for2008080104 Computer Visionen
local.subject.seo2008960904 Farmland, Arable Cropland and Permanent Cropland Land Managementen
local.profile.schoolSchool of Science and Technologyen
local.profile.schoolSchool of Science and Technologyen
local.profile.schoolResearch Servicesen
local.profile.schoolSchool of Science and Technologyen
local.profile.emailesadgro2@une.edu.auen
local.profile.emailgfalzon2@une.edu.auen
local.profile.emaildmiron@une.edu.auen
local.profile.emaildlamb@une.edu.auen
local.output.categoryC1en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.identifier.epublicationsrecordune-20170613-162813en
local.publisher.placeNetherlandsen
local.format.startpage204en
local.format.endpage212en
local.identifier.scopusid85019773323en
local.peerreviewedYesen
local.identifier.volume139en
local.contributor.lastnameSadgroveen
local.contributor.lastnameFalzonen
local.contributor.lastnameMironen
local.contributor.lastnameLamben
dc.identifier.staffune-id:esadgro2en
dc.identifier.staffune-id:gfalzon2en
dc.identifier.staffune-id:dmironen
dc.identifier.staffune-id:dlamben
local.profile.orcid0000-0002-8710-9900en
local.profile.orcid0000-0002-1989-9357en
local.profile.orcid0000-0003-2157-5439en
local.profile.orcid0000-0002-2917-2231en
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:21550en
local.identifier.handlehttps://hdl.handle.net/1959.11/21357en
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleFast object detection in pastoral landscapes using a Colour Feature Extreme Learning Machineen
local.output.categorydescriptionC1 Refereed Article in a Scholarly Journalen
local.search.authorSadgrove, Edmunden
local.search.authorFalzon, Gregoryen
local.search.authorMiron, David Jen
local.search.authorLamb, Daviden
local.uneassociationUnknownen
local.identifier.wosid000404320100019en
local.year.published2017en
local.fileurl.closedpublishedhttps://rune.une.edu.au/web/retrieve/32d56a41-92d9-473e-b80e-057ee8d3588een
local.subject.for2020300206 Agricultural spatial analysis and modellingen
local.subject.for2020460702 Computer graphicsen
local.subject.for2020460304 Computer visionen
local.subject.seo2020180603 Evaluation, allocation, and impacts of land useen
local.subject.seo2020180607 Terrestrial erosionen
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School of Science and Technology
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