Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/27084
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dc.contributor.authorStover, Joshuaen
dc.contributor.authorFalzon, Gregen
dc.contributor.authorJensen, Troyen
dc.contributor.authorSchroeder, Bernarden
dc.contributor.authorLamb, David Wen
local.source.editorEditor(s): Warrick Nelsonen
dc.date.accessioned2019-06-04T00:07:27Z-
dc.date.available2019-06-04T00:07:27Z-
dc.date.issued2017-10-16-
dc.identifier.citationProceedings of the 7th Asian-Australasian Conference on Precision Agriculture, p. 1-8en
dc.identifier.urihttps://hdl.handle.net/1959.11/27084-
dc.description.abstractEfficient use of fertilisers, in particular the use of Nitrogen (N), is one of the rate-limiting factors in meeting global food production requirements. While N is a key driver in increasing crop yields, overuse can also lead to negative environmental and health impacts. It has been suggested that Variable Rate Fertiliser (VRF) techniques may help to reduce excessive N applications. VRF seeks to spatially vary fertiliser input based on estimated crop requirements, however a major challenge in the operational deployment of VRF systems is the automated processing of large amounts of sensor data in real-time. Machine learning techniques have shown promise in their ability to process these large, high-velocity data streams, and to produce accurate predictions. This paper will use a simulation testing methodology on real hardware to compare two existing machine learning algorithms and a prototype implementation of a newly developed algorithm for their applicability to VRF application.en
dc.languageenen
dc.publisherNew Zealand Institute for Plant & Food Research Ltden
dc.relation.ispartofProceedings of the 7th Asian-Australasian Conference on Precision Agricultureen
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleHardware and embedded algorithms for real time variable rate fertiliser applicationsen
dc.typeConference Publicationen
dc.relation.conferenceACPA 2017: 7th Asian-Australasian Conference on Precision Agricultureen
dc.identifier.doi10.5281/zenodo.895528en
dcterms.accessRightsGolden
local.contributor.firstnameJoshuaen
local.contributor.firstnameGregen
local.contributor.firstnameTroyen
local.contributor.firstnameBernarden
local.contributor.firstnameDavid Wen
local.subject.for2008070106 Farm Management, Rural Management and Agribusinessen
local.subject.for2008079902 Fertilisers and Agrochemicals (incl. Application)en
local.subject.for2008170203 Knowledge Representation and Machine Learningen
local.subject.seo2008960904 Farmland, Arable Cropland and Permanent Cropland Land Managementen
local.subject.seo2008890201 Application Software Packages (excl. Computer Games)en
local.profile.schoolSchool of Science and Technologyen
local.profile.schoolSchool of Science and Technologyen
local.profile.schoolSchool of Science and Technologyen
local.profile.emailjstover2@une.edu.auen
local.profile.emailgfalzon2@une.edu.auen
local.profile.emailtroy.jensen@usq.edu.auen
local.profile.emailbernard.schroeder@usq.edu.auen
local.profile.emaildlamb@une.edu.auen
local.output.categoryE2en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.date.conference16th - 18th October, 2017en
local.conference.placeHamilton, New Zealanden
local.publisher.placeHamilton, New Zealanden
local.format.startpage1en
local.format.endpage8en
local.peerreviewedYesen
local.access.fulltextYesen
local.contributor.lastnameStoveren
local.contributor.lastnameFalzonen
local.contributor.lastnameJensenen
local.contributor.lastnameSchroederen
local.contributor.lastnameLamben
dc.identifier.staffune-id:jstover2en
dc.identifier.staffune-id:gfalzon2en
dc.identifier.staffune-id:dlamben
local.profile.orcid0000-0002-1989-9357en
local.profile.orcid0000-0002-2917-2231en
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:1959.11/27084en
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleHardware and embedded algorithms for real time variable rate fertiliser applicationsen
local.relation.fundingsourcenoteRegional University Network Precision Agriculture Flagship (RUNPAF) scholarshipen
local.output.categorydescriptionE2 Non-Refereed Scholarly Conference Publicationen
local.relation.urlhttps://precisionagriculture.org.nz/events/pa17-the-international-tri-conference-for-precision-agriculture-in-2017/en
local.relation.urlhttps://zenodo.org/record/1012620en
local.conference.detailsACPA 2017: 7th Asian-Australasian Conference on Precision Agriculture, Hamilton, New Zealand, 16th - 18th October, 2017en
local.search.authorStover, Joshuaen
local.search.authorFalzon, Gregen
local.search.authorJensen, Troyen
local.search.authorSchroeder, Bernarden
local.search.authorLamb, David Wen
local.uneassociationUnknownen
local.year.published2017en
local.fileurl.closedpublishedhttps://rune.une.edu.au/web/retrieve/352a4f6b-9279-4c9a-b210-53221bd1f989en
local.subject.for2020300411 Fertilisers (incl. application)en
local.subject.for2020461106 Semi- and unsupervised learningen
local.subject.for2020300208 Farm management, rural management and agribusinessen
local.subject.seo2020220401 Application software packagesen
local.subject.seo2020180603 Evaluation, allocation, and impacts of land useen
dc.notification.tokenab6330fa-930b-40cd-a959-4121a718f5c4en
local.codeupdate.date2022-02-14T08:44:44.071en
local.codeupdate.epersonrtobler@une.edu.auen
local.codeupdate.finalisedtrueen
local.original.for2020300208 Farm management, rural management and agribusinessen
local.original.for2020461105 Reinforcement learningen
local.original.for2020300411 Fertilisers (incl. application)en
local.original.for2020300410 Crop and pasture waste water useen
local.original.for2020300401 Agrochemicals and biocides (incl. application)en
local.original.for2020461106 Semi- and unsupervised learningen
local.original.seo2020180607 Terrestrial erosionen
local.original.seo2020180603 Evaluation, allocation, and impacts of land useen
local.original.seo2020220401 Application software packagesen
local.date.start2017-10-16-
local.date.end2017-10-18-
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School of Science and Technology
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