Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/23135
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dc.contributor.authorRahman, Muhammad Moshiuren
dc.contributor.authorMuir, Jasmineen
dc.contributor.authorRobson, Andrewen
local.source.editorEditor(s): Warrick Nelsonen
dc.date.accessioned2018-05-30T08:52:00Z-
dc.date.issued2017-
dc.identifier.citationProceedings of the 7th Asian-Australasian Conference on Precision Agriculture, p. 1-6en
dc.identifier.urihttps://hdl.handle.net/1959.11/23135-
dc.description.abstractAccurate with-in season yield prediction is important for the Australian sugarcane industry as it supports crop management and decision making processes, including those associated with harvest scheduling, storage, milling, and forward selling. In a recent study, a quadratic model was developed from multi-temporal Landsat imagery (30 m spatial resolution) acquired between 2001-2014 (15th November to 31st July) for the prediction of sugarcane yield grown in the Bundaberg region of Queensland, Australia. The resultant high accuracy of prediction achieved from the Bundaberg model for the 2015 and 2016 seasons inspired the development of similar models for the Tully and Mackay growing regions. As with the Bundaberg model, historical Landsat imagery was acquired over a 12 year (Tully) and 10 year (Mackay) period with the capture window again specified to be between 1st November to 30th June to coincide with the sugarcane growing season. All Landsat images were downloaded and processed using Python programing to automate image processing and data extraction. This allowed the model to be applied rapidly over large areas. For each region, the average green normalized difference vegetation index (GNDVI) for all sugarcane crops was extracted from each image and overlayed onto one time scale 1st November to 30th June. Using the quadratic model derived from each regional data set, the maximum GNDVI achieved for each season was calculated and regressed against the corresponding annual average regional sugarcane yield producing strong correlation for both Tully (R2 = 0.89 and RMSE = 5.5 t/ha) and Mackay (R2 = 0.63 and RMSE = 5.3 t/ha). Moreover, the establishment of an annual crop growth profile from each quadratic model has enabled a benchmark of historic crop development to be derived. Any deviation of future crops from this benchmark can be used as an indicator of widespread abiotic or biotic constraints. As well as regional forecasts, the yield algorithms can also be applied at the pixel level to allow individual yield maps to be derived and delivered near real time to all Australian growers and millers.en
dc.languageenen
dc.publisherPrecision Agriculture Association New Zealanden
dc.relation.ispartofProceedings of the 7th Asian-Australasian Conference on Precision Agricultureen
dc.titleMulti-temporal landsat algorithms for the yield prediction of sugarcane crops in Australiaen
dc.typeConference Publicationen
dc.relation.conferenceACPA 2017: 7th Asian-Australasian Conference on Precision Agricultureen
dc.identifier.doi10.5281/zenodo.891091en
dcterms.accessRightsGolden
dc.subject.keywordsFarming Systems Researchen
dc.subject.keywordsSustainable Agricultural Developmenten
dc.subject.keywordsAgricultural Spatial Analysis and Modellingen
local.contributor.firstnameMuhammad Moshiuren
local.contributor.firstnameJasmineen
local.contributor.firstnameAndrewen
local.subject.for2008070107 Farming Systems Researchen
local.subject.for2008070104 Agricultural Spatial Analysis and Modellingen
local.subject.for2008070108 Sustainable Agricultural Developmenten
local.subject.seo2008820304 Sugaren
local.subject.seo2008960604 Environmental Management Systemsen
local.profile.schoolSchool of Science and Technologyen
local.profile.schoolSchool of Science and Technologyen
local.profile.schoolSchool of Science and Technologyen
local.profile.emailmrahma37@une.edu.auen
local.profile.emailjmuir6@une.edu.auen
local.profile.emailarobson7@une.edu.auen
local.output.categoryE2en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.identifier.epublicationsrecordune-20180301-095139en
local.date.conference16th - 18th October, 2017en
local.conference.placeHamilton, New Zealanden
local.publisher.placeHamilton, New Zealanden
local.format.startpage1en
local.format.endpage6en
local.access.fulltextYesen
local.contributor.lastnameRahmanen
local.contributor.lastnameMuiren
local.contributor.lastnameRobsonen
dc.identifier.staffune-id:mrahma37en
dc.identifier.staffune-id:jmuir6en
dc.identifier.staffune-id:arobson7en
local.profile.orcid0000-0001-6430-0588en
local.profile.orcid0000-0001-6114-0670en
local.profile.orcid0000-0001-5762-8980en
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:23320en
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleMulti-temporal landsat algorithms for the yield prediction of sugarcane crops in Australiaen
local.output.categorydescriptionE2 Non-Refereed Scholarly Conference Publicationen
local.conference.detailsACPA 2017: 7th Asian-Australasian Conference on Precision Agriculture, Hamilton, New Zealand, 16th - 18th October, 2017en
local.search.authorRahman, Muhammad Moshiuren
local.search.authorMuir, Jasmineen
local.search.authorRobson, Andrewen
local.uneassociationUnknownen
local.year.published2017en
local.subject.for2020300210 Sustainable agricultural developmenten
local.subject.for2020300206 Agricultural spatial analysis and modellingen
local.subject.seo2020260607 Sugaren
local.subject.seo2020189999 Other environmental management not elsewhere classifieden
dc.notification.token29879ab6-daa3-4e2a-82b1-0300b64dcf2cen
local.codeupdate.date2022-02-09T16:52:13.884en
local.codeupdate.epersonmrahma37@une.edu.auen
local.codeupdate.finalisedtrueen
local.original.for2020300210 Sustainable agricultural developmenten
local.original.for2020300206 Agricultural spatial analysis and modellingen
local.original.for2020undefineden
local.original.seo2020260607 Sugaren
local.original.seo2020189999 Other environmental management not elsewhere classifieden
local.date.start2017-10-16-
local.date.end2017-10-18-
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