Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/27346
Full metadata record
DC FieldValueLanguage
dc.contributor.authorMuir, Jasmineen
dc.contributor.authorRobson, Andrewen
dc.contributor.authorRahman, M Men
dc.date.accessioned2019-07-21T23:23:18Z-
dc.date.available2019-07-21T23:23:18Z-
dc.date.issued2018-
dc.identifier.citation40th Conference of the Australian Society of Sugar Cane Technologists (ASSCT 2018), p. 33-40en
dc.identifier.isbn9781510862166en
dc.identifier.urihttps://hdl.handle.net/1959.11/27346-
dc.description.abstractSatellite imagery has been demonstrated to be an effective technology for producing accurate pre-harvest estimates in many agricultural crops. For Australian sugarcane, yield forecasting models have been developed from a single date SPOT satellite image acquired around peak crop growth. However, a failure to acquire a SPOT image at this critical growth stage, from continued cloud cover or from competition for the satellite, can prevent an image being captured and therefore a forecast being made for that season. In order to reduce the reliance on a single image capture and to improve the accuracies of the forecasts themselves, time series yield prediction models have been developed for eight sugarcane growing regions using multiple years of free Landsat satellite images. In addition to the forecasting of average regional yield, an automated computational and programming procedure enabling the derivation of crop vigour variability (GNDVI) maps from the freely available Sentinel 2 satellite imagery was developed. These maps, produced for 15 sugarcane growing regions during the 2017 growing season, identify both variations in crop vigour across regions and within every individual crop. These outputs were made available to collaborating mills within each growing region. This paper presents the accuracies achieved from the time series yield forecasting models versus actual 2017 yields for the respective regions, as well as provides an example of the derived mapping outputs.en
dc.languageenen
dc.publisherAustralian Society of Sugar Cane Technologistsen
dc.relation.ispartof40th Conference of the Australian Society of Sugar Cane Technologists (ASSCT 2018)en
dc.title'Sugar from Space': Using Satellite Imagery to Predict Cane Yield and Variabilityen
dc.typeConference Publicationen
dc.relation.conferenceASSCT 2018: 40th Annual Conference of the Australian Society of Sugar Cane Technologistsen
dcterms.accessRightsUNE Greenen
dc.subject.keywordsSustainable Agricultural Developmenten
dc.subject.keywordsAgricultural Land Managementen
dc.subject.keywordsAgricultural Spatial Analysis and Modellingen
local.contributor.firstnameJasmineen
local.contributor.firstnameAndrewen
local.contributor.firstnameM Men
local.subject.for2008070104 Agricultural Spatial Analysis and Modellingen
local.subject.for2008070101 Agricultural Land Managementen
local.subject.for2008070108 Sustainable Agricultural Developmenten
local.subject.seo2008820603 Sugar Cane (Cut for Crushing)en
local.subject.seo2008820304 Sugaren
local.subject.seo2008829805 Management of Water Consumption by Plant Productionen
local.profile.schoolSchool of Science and Technologyen
local.profile.schoolSchool of Science and Technologyen
local.profile.schoolSchool of Science and Technologyen
local.profile.emailjmuir6@une.edu.auen
local.profile.emailarobson7@une.edu.auen
local.profile.emailmrahma37@une.edu.auen
local.output.categoryE1en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.identifier.epublicationsrecordune-20180622-093216en
local.date.conference18th - 20th April, 2018en
local.conference.placeMackay, Australiaen
local.publisher.placeMackay, Australiaen
local.format.startpage33en
local.format.endpage40en
local.peerreviewedYesen
local.title.subtitleUsing Satellite Imagery to Predict Cane Yield and Variabilityen
local.access.fulltextYesen
local.contributor.lastnameMuiren
local.contributor.lastnameRobsonen
local.contributor.lastnameRahmanen
dc.identifier.staffune-id:jmuir6en
dc.identifier.staffune-id:arobson7en
dc.identifier.staffune-id:mrahma37en
local.profile.orcid0000-0001-6114-0670en
local.profile.orcid0000-0001-5762-8980en
local.profile.orcid0000-0001-6430-0588en
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:-20180622-093216en
local.identifier.unepublicationidune:-20180622-093216en
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitle'Sugar from Space'en
local.relation.fundingsourcenoteSugar Research Australiaen
local.output.categorydescriptionE1 Refereed Scholarly Conference Publicationen
local.conference.detailsASSCT 2018: 40th Annual Conference of the Australian Society of Sugar Cane Technologists, Mackay, Australia, 18th - 20th April, 2018en
local.search.authorMuir, Jasmineen
local.search.authorRobson, Andrewen
local.search.authorRahman, M Men
local.open.fileurlhttps://rune.une.edu.au/web/retrieve/ba65d984-04b2-44d2-bba2-5cff4884c48den
local.uneassociationUnknownen
local.year.published2018en
local.fileurl.openhttps://rune.une.edu.au/web/retrieve/ba65d984-04b2-44d2-bba2-5cff4884c48den
local.fileurl.closedpublishedhttps://rune.une.edu.au/web/retrieve/267f776a-7e5e-4e44-b527-b66aafb8ca9ben
local.subject.for2020300206 Agricultural spatial analysis and modellingen
local.subject.for2020300202 Agricultural land managementen
local.subject.for2020300210 Sustainable agricultural developmenten
local.subject.seo2020260403 Sugar cane (cut for crushing)en
local.subject.seo2020260607 Sugaren
local.subject.seo2020260104 Management of water consumption by plant productionen
local.date.start2018-04-18-
local.date.end2018-04-20-
Appears in Collections:Conference Publication
School of Science and Technology
Files in This Item:
6 files
File Description SizeFormat 
open/SugarMuirRobsonRahman2018ConferencePublicationPostPeerReview.pdfOpen access version792.31 kBAdobe PDF
Download Adobe
View/Open
Show simple item record

Page view(s)

2,360
checked on Mar 24, 2024

Download(s)

388
checked on Mar 24, 2024
Google Media

Google ScholarTM

Check


Items in Research UNE are protected by copyright, with all rights reserved, unless otherwise indicated.