dc.contributor.author | Williams, Henry A.M. | en_NZ |
dc.contributor.author | Jones, Mark Hedley | en_NZ |
dc.contributor.author | Nejati, Mahla | en_NZ |
dc.contributor.author | Seabright, Matthew | en_NZ |
dc.contributor.author | Bell, Jamie | en_NZ |
dc.contributor.author | Penhall, Nicky D. | en_NZ |
dc.contributor.author | Barnett, Josh | en_NZ |
dc.contributor.author | Duke, Mike | en_NZ |
dc.contributor.author | Scarfe, Alastair J. | en_NZ |
dc.contributor.author | Ahn, Ho Seok | en_NZ |
dc.contributor.author | Lim, JongYoon | en_NZ |
dc.contributor.author | MacDonald, Bruce A. | en_NZ |
dc.date.accessioned | 2020-01-08T00:45:50Z | |
dc.date.available | 2019 | en_NZ |
dc.date.available | 2020-01-08T00:45:50Z | |
dc.date.issued | 2019 | en_NZ |
dc.identifier.citation | Williams, 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.2019.03.007 | en |
dc.identifier.issn | 1537-5110 | en_NZ |
dc.identifier.uri | https://hdl.handle.net/10289/13355 | |
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. | |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | en_NZ |
dc.publisher | Elsevier | en_NZ |
dc.rights | This is an author's pre-print of an article published in Biosystems Engineering. © 2019 IAgrE. | |
dc.subject | Science & Technology | en_NZ |
dc.subject | Life Sciences & Biomedicine | en_NZ |
dc.subject | Agricultural Engineering | en_NZ |
dc.subject | Agriculture, Multidisciplinary | en_NZ |
dc.subject | Agriculture | en_NZ |
dc.subject | Horticulture | en_NZ |
dc.subject | Robotics | en_NZ |
dc.subject | Neural Networking | en_NZ |
dc.subject | Machine Vision | en_NZ |
dc.subject | Harvesting | en_NZ |
dc.subject | Convolution Neural Networks | en_NZ |
dc.subject | Orchard | en_NZ |
dc.subject | Fruit detection | en_NZ |
dc.subject | Recognition | en_NZ |
dc.title | Robotic kiwifruit harvesting using machine vision, convolutional neural networks, and robotic arms | en_NZ |
dc.type | Journal Article | |
dc.identifier.doi | 10.1016/j.biosystemseng.2019.03.007 | en_NZ |
dc.relation.isPartOf | Biosystems engineering | en_NZ |
pubs.begin-page | 140 | |
pubs.elements-id | 236648 | |
pubs.end-page | 156 | |
pubs.publication-status | Published | en_NZ |
pubs.volume | 181 | en_NZ |
dc.identifier.eissn | 1537-5129 | en_NZ |