Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/19946
Full metadata record
DC FieldValueLanguage
dc.contributor.authorRahman, Muhammad Moshiuren
dc.contributor.authorRobson, Andrewen
dc.date.accessioned2017-02-06T15:18:00Z-
dc.date.issued2016-
dc.identifier.citation19th Precision Agriculture Symposium Proceedings, p. 24-29en
dc.identifier.urihttps://hdl.handle.net/1959.11/19946-
dc.description.abstractSugarcane yield prediction is critical for in season crop management and decision making processes such as harvest scheduling, storage and milling, and forward selling. This presentation reports on a recently published method of predicting sugarcane yield in the Bundaberg region (Qld) using time series Landsat data. From the freely available Landsat archive, 98 cloud free (<40%) Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) images, acquired between November 15th to July 31st (2001-2015), were sourced for this study. The images were masked using the field boundary layer vector files of each year and the green normalized difference vegetation index (GNDVI), an indicator of crop vigour was calculated. An analysis of average GNDVI values from all sugarcane crops grown within the Bundaberg region over the 15 year period using a quadratic model identified the beginning of April as the peak growth stage and, therefore, the decisive time of image capture for a single satellite image based yield forecasting. The model derived maximum GNDVI was regressed against historical sugarcane yield data obtained from the mill. The coefficient of determination showed a significant relation between the predicted and actual sugarcane yield (t/ha) with R2 = 0.69 and RMSE 4.2 t/ha. Results showed that the model derived maximum GNDVI from Landsat imagery would be a feasible technique to predict sugarcane yield in Bundaberg region. This research, however, warrants further investigation to improve and develop accurate operational sugarcane yield prediction model across other domestic and global growing regions, as the influence of environmental conditions and cropping practices will likely vary the relationship between GNDVI and yield (t/ha).en
dc.languageenen
dc.publisherSociety of Precision Agriculture Australia (SPAA)en
dc.relation.ispartof19th Precision Agriculture Symposium Proceedingsen
dc.titleMulti-temporal remote sensing for yield prediction in sugarcane cropsen
dc.typeConference Publicationen
dc.relation.conferencePrecision Agriculture 2016: 19th Symposium on Precision Agriculture in Australasiaen
dc.subject.keywordsAgricultural Spatial Analysis and Modellingen
local.contributor.firstnameMuhammad Moshiuren
local.contributor.firstnameAndrewen
local.subject.for2008070104 Agricultural Spatial Analysis and Modellingen
local.subject.seo2008820304 Sugaren
local.profile.schoolSchool of Science and Technologyen
local.profile.schoolSchool of Science and Technologyen
local.profile.emailmrahma37@une.edu.auen
local.profile.emailarobson7@une.edu.auen
local.output.categoryE2en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.identifier.epublicationsrecordune-20170112-160153en
local.date.conference12th - 13th September, 2016en
local.conference.placeToowoomba, Australiaen
local.publisher.placeToowoomba, Australiaen
local.format.startpage24en
local.format.endpage29en
local.contributor.lastnameRahmanen
local.contributor.lastnameRobsonen
dc.identifier.staffune-id:mrahma37en
dc.identifier.staffune-id:arobson7en
local.profile.orcid0000-0001-6430-0588en
local.profile.orcid0000-0001-5762-8980en
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:20144en
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleMulti-temporal remote sensing for yield prediction in sugarcane cropsen
local.output.categorydescriptionE2 Non-Refereed Scholarly Conference Publicationen
local.relation.urlhttp://www.spaa.com.au/pdf/429_PA_Symposium_16_LR.pdfen
local.conference.detailsPrecision Agriculture 2016: 19th Symposium on Precision Agriculture in Australasia, Toowoomba, Australia, 12th - 13th September, 2016en
local.search.authorRahman, Muhammad Moshiuren
local.search.authorRobson, Andrewen
local.uneassociationUnknownen
local.year.published2016en
local.subject.for2020300206 Agricultural spatial analysis and modellingen
local.subject.seo2020260607 Sugaren
local.date.start2016-09-12-
local.date.end2016-09-13-
Appears in Collections:Conference Publication
Files in This Item:
3 files
File Description SizeFormat 
Show simple item record

Page view(s)

1,732
checked on May 19, 2024
Google Media

Google ScholarTM

Check


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