dc.contributor.author | Scott, J | |
dc.contributor.author | Busch, A | |
dc.date.accessioned | 2021-03-24T05:39:44Z | |
dc.date.available | 2021-03-24T05:39:44Z | |
dc.date.issued | 2020 | |
dc.identifier.isbn | 9781728191089 | |
dc.identifier.doi | 10.1109/DICTA51227.2020.9363394 | |
dc.identifier.uri | http://hdl.handle.net/10072/403386 | |
dc.description.abstract | Australia'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.peerreviewed | Yes | |
dc.publisher | IEEE | |
dc.relation.ispartofconferencename | 2020 Digital Image Computing: Techniques and Applications (DICTA) | |
dc.relation.ispartofconferencetitle | 2020 Digital Image Computing: Techniques and Applications, DICTA 2020 | |
dc.relation.ispartofdatefrom | 2020-11-29 | |
dc.relation.ispartofdateto | 2020-12-02 | |
dc.relation.ispartoflocation | Melbourne, Australia | |
dc.relation.uri | http://purl.org/au-research/grants/ARC/IH180100002 | |
dc.relation.grantID | IH180100002 | |
dc.relation.funders | ARC | |
dc.subject.fieldofresearch | Computer vision | |
dc.subject.fieldofresearch | Agriculture, land and farm management | |
dc.subject.fieldofresearchcode | 460304 | |
dc.subject.fieldofresearchcode | 3002 | |
dc.title | Furrow Mapping of Sugarcane Billet Density Using Deep Learning and Object Detection | |
dc.type | Conference output | |
dc.type.description | E1 - Conferences | |
dcterms.bibliographicCitation | Scott, 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.updated | 2021-03-24T04:07:22Z | |
dc.description.version | Accepted 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.hasfulltext | Full Text | |
gro.griffith.author | Busch, Andrew W. | |