dc.contributor.author |
Williams, Henry |
en |
dc.contributor.author |
Mark H. Jones |
en |
dc.contributor.author |
Mahla Nejati |
en |
dc.contributor.author |
Matthew J. Seabright |
en |
dc.contributor.author |
Jamie Bell |
en |
dc.contributor.author |
Nicky D. Penhall |
en |
dc.contributor.author |
Josh J. Barnett |
en |
dc.contributor.author |
Mike D. Duke |
en |
dc.contributor.author |
Alistair J. Scarfe |
en |
dc.contributor.author |
Ho Seok Ahn |
en |
dc.contributor.author |
JongYoon Lim |
en |
dc.contributor.author |
Bruce A. MacDonald |
en |
dc.date.accessioned |
2019-10-02T00:47:21Z |
en |
dc.date.issued |
2019-05 |
en |
dc.identifier.issn |
1537-5110 |
en |
dc.identifier.uri |
http://hdl.handle.net/2292/48338 |
en |
dc.description.abstract |
As labour requirements in horticultural become more challenging, automated solutions are becoming an effective approach to maintain productivity and quality. This paper presents the design and performance evaluation of a novel multi-arm kiwifruit harvesting robot designed to operate autonomously in pergola style orchards. The harvester consists of four robotic arms that have been designed specifically for kiwifruit harvesting, each with a novel end-effector developed to enable safe harvesting of the kiwifruit. The vision system leverages recent advances in deep neural networks and stereo matching for reliably detecting and locating kiwifruit in real-world lighting conditions. Furthermore, a novel dynamic fruit scheduling system is presented that has been developed to coordinate the four arms throughout the harvesting process. The performance of the harvester has been measured through a comprehensive and realistic field-trial in a commercial orchard environment. The results show that the presented harvester is capable of successfully harvesting 51.0% of the total number of kiwifruit within the orchard with an average cycle time of 5.5s/fruit. |
en |
dc.format.medium |
Undetermined |
en |
dc.language |
eng |
en |
dc.relation.ispartofseries |
Biosystems engineering. |
en |
dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. |
en |
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
en |
dc.title |
Robotic kiwifruit harvesting using machine vision, convolutional neural networks, and robotic arms |
en |
dc.type |
Journal Article |
en |
dc.identifier.doi |
10.1016/j.biosystemseng.2019.03.007 |
en |
pubs.begin-page |
140 |
en |
pubs.volume |
181 |
en |
dc.rights.holder |
Copyright: The author |
en |
pubs.end-page |
156 |
en |
pubs.publication-status |
Published |
en |
dc.rights.accessrights |
http://purl.org/eprint/accessRights/RestrictedAccess |
en |
pubs.subtype |
Journal Article |
en |
pubs.elements-id |
768236 |
en |
pubs.org-id |
Engineering |
en |
pubs.org-id |
Department of Electrical, Computer and Software Engineering |
en |
dc.identifier.eissn |
1537-5129 |
en |
pubs.record-created-at-source-date |
2019-05-16 |
en |