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
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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Cites
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Cited by
IoT, Big Data, and Artificial Intelligence in Agriculture and Food Industry
Misra, N. N., Dixit, Y., Al-Mallahi, A., Bhullar, M. S., Upadhyay, R., & Martynenko, A. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2998584 · Cited by 890
Zhang, B., Xie, Y., Zhou, J., Wang, K., & Zhang, Z. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105694 · Cited by 484
Robotic kiwifruit harvesting using machine vision, convolutional neural networks, and robotic arms
Williams, H. A. M., Jones, M. H., Nejati, M., Seabright, M. J., Bell, J., Penhall, N. D., Barnett, J. J., Duke, M. D., Scarfe, A. J., Ahn, H. S., et al. (2019) · Biosystems Engineering · doi:10.1016/j.biosystemseng.2019.03.007 · Cited by 346
Intelligent robots for fruit harvesting: recent developments and future challenges
Zhou, H., Wang, X., Au, W., Kang, H., & Chen, C. (2022) · Precision Agriculture · doi:10.1007/s11119-022-09913-3 · Cited by 316
Artificial Intelligence Technology in the Agricultural Sector: A Systematic Literature Review
Elbasi, E., Mostafa, N., AlArnaout, Z., Zreikat, A. I., Cina, E., Varghese, G., Shdefat, A., Topcu, A. E., Abdelbaki, W., Mathew, S., et al. (2023) · IEEE Access · doi:10.1109/access.2022.3232485 · Cited by 282
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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- Silwal, A., Davidson, J. R., Karkee, M., Mo, C., Zhang, Q., & Lewis, K. (2017). Design, integration, and field evaluation of a robotic apple harvester. Journal of Field Robotics. https://doi.org/10.1002/rob.21715
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
