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dc.contributor.authorWilliams, Henry A.M.en_NZ
dc.contributor.authorJones, Mark Hedleyen_NZ
dc.contributor.authorNejati, Mahlaen_NZ
dc.contributor.authorSeabright, Matthewen_NZ
dc.contributor.authorBell, Jamieen_NZ
dc.contributor.authorPenhall, Nicky D.en_NZ
dc.contributor.authorBarnett, Joshen_NZ
dc.contributor.authorDuke, Mikeen_NZ
dc.contributor.authorScarfe, Alastair J.en_NZ
dc.contributor.authorAhn, Ho Seoken_NZ
dc.contributor.authorLim, JongYoonen_NZ
dc.contributor.authorMacDonald, Bruce A.en_NZ
dc.date.accessioned2020-01-08T00:45:50Z
dc.date.available2019en_NZ
dc.date.available2020-01-08T00:45:50Z
dc.date.issued2019en_NZ
dc.identifier.citationWilliams, H., Jones, M. H., Nejati, M., Seabright, M., Bell, J., Penhall, N. D., … MacDonald, B. A. (2019). Robotic kiwifruit harvesting using machine vision, convolutional neural networks, and robotic arms. Biosystems Engineering, 181, 140–156. https://doi.org/10.1016/j.biosystemseng.2019.03.007en
dc.identifier.issn1537-5110en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/13355
dc.description.abstractAs 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.
dc.format.mimetypeapplication/pdf
dc.language.isoenen_NZ
dc.publisherElsevieren_NZ
dc.rightsThis is an author's pre-print of an article published in Biosystems Engineering. © 2019 IAgrE.
dc.subjectScience & Technologyen_NZ
dc.subjectLife Sciences & Biomedicineen_NZ
dc.subjectAgricultural Engineeringen_NZ
dc.subjectAgriculture, Multidisciplinaryen_NZ
dc.subjectAgricultureen_NZ
dc.subjectHorticultureen_NZ
dc.subjectRoboticsen_NZ
dc.subjectNeural Networkingen_NZ
dc.subjectMachine Visionen_NZ
dc.subjectHarvestingen_NZ
dc.subjectConvolution Neural Networksen_NZ
dc.subjectOrcharden_NZ
dc.subjectFruit detectionen_NZ
dc.subjectRecognitionen_NZ
dc.titleRobotic kiwifruit harvesting using machine vision, convolutional neural networks, and robotic armsen_NZ
dc.typeJournal Article
dc.identifier.doi10.1016/j.biosystemseng.2019.03.007en_NZ
dc.relation.isPartOfBiosystems engineeringen_NZ
pubs.begin-page140
pubs.elements-id236648
pubs.end-page156
pubs.publication-statusPublisheden_NZ
pubs.volume181en_NZ
dc.identifier.eissn1537-5129en_NZ


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