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dc.contributor.authorScott, J
dc.contributor.authorBusch, A
dc.date.accessioned2021-03-24T05:39:44Z
dc.date.available2021-03-24T05:39:44Z
dc.date.issued2020
dc.identifier.isbn9781728191089
dc.identifier.doi10.1109/DICTA51227.2020.9363394
dc.identifier.urihttp://hdl.handle.net/10072/403386
dc.description.abstractAustralia's sugar industry is currently undergoing significant hardships, due to global market contractions from COVID-19, increased crop forecasts from larger global producers, and falling oil prices. Current planting practices utilize inefficient mass-flow planting techniques, and no attempt to map the seed using machine vision has been made, to date, in order to understand the underlying problems. This paper investigates the plausibility of creating a labeled sugarcane billet dataset using a readily-available camera positioned beneath a planter and analysing this using a YOLOv3 network. This network resulted in a high mean average precision at intersect over union of 0.5 (mAP50) of 0.852 on test images, and was used to provide planting metrics by generating a furrow map.
dc.description.peerreviewedYes
dc.publisherIEEE
dc.relation.ispartofconferencename2020 Digital Image Computing: Techniques and Applications (DICTA)
dc.relation.ispartofconferencetitle2020 Digital Image Computing: Techniques and Applications, DICTA 2020
dc.relation.ispartofdatefrom2020-11-29
dc.relation.ispartofdateto2020-12-02
dc.relation.ispartoflocationMelbourne, Australia
dc.relation.urihttp://purl.org/au-research/grants/ARC/IH180100002
dc.relation.grantIDIH180100002
dc.relation.fundersARC
dc.subject.fieldofresearchComputer vision
dc.subject.fieldofresearchAgriculture, land and farm management
dc.subject.fieldofresearchcode460304
dc.subject.fieldofresearchcode3002
dc.titleFurrow Mapping of Sugarcane Billet Density Using Deep Learning and Object Detection
dc.typeConference output
dc.type.descriptionE1 - Conferences
dcterms.bibliographicCitationScott, J; Busch, A, Furrow Mapping of Sugarcane Billet Density Using Deep Learning and Object Detection, 2020 Digital Image Computing: Techniques and Applications, DICTA 2020, 2020
dc.date.updated2021-03-24T04:07:22Z
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
gro.hasfulltextFull Text
gro.griffith.authorBusch, Andrew W.


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    Contains papers delivered by Griffith authors at national and international conferences.

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