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Cited by 406

The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.

Design, integration, and field evaluation of a robotic apple harvester

Silwal, Abhisesh, Davidson, Joseph R., Karkee, Manoj, Mo, Changki, Zhang, Qin, Lewis, Karen·2017·Journal of Field Robotics

Abstract

Every apple destined for the fresh market is picked by the human hand. Despite extensive research over the past four decades, there are no mechanical apple harvesters for the fresh market commercially available, which is a significant concern because of increasing uncertainty about the availability of manual labor and rising production costs. The highly unstructured orchard environment has been a major challenge to the development of commercially viable robotic harvesting systems. This paper reports the design and field evaluation of a robotic apple harvester. The approach adopted was to use a low‐cost system to assess required sensing, planning, and manipulation functionality in a modern orchard system with a planar canopy. The system was tested in a commercial apple orchard in Washington State. Workspace modifications and performance criteria are thoroughly defined and reported to help evaluate the approach and guide future enhancements. The machine vision system was accurate and had an average localization time of 1.5 s per fruit. The seven degree of freedom harvesting system successfully picked 127 of the 150 fruit attempted for an overall success rate of 84% with an average picking time of 6.0 s per fruit. Future work will include integration of additional sensing and obstacle detection for improved system robustness.

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Metadata

Authors
Silwal, A., Davidson, J. R., Karkee, M., Mo, C., Zhang, Q., & Lewis, K.
Venue
Journal of Field Robotics
Publisher
Wiley
Published
2017-04-28
Volume
34
Issue
6
Pages
1140-1159
ISSN
  • 1556-4959 (ISSN-L)
  • 1556-4967
OpenAlex
W2608799977
Cited by
406
References
30
FWCI
19.24
Type
journal article
Language
en
Open access
closed

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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