The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Robotic kiwifruit harvesting using machine vision, convolutional neural networks, and robotic arms
Open accessAbstract
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.
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Cites
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Cited by
A Review of Convolutional Neural Network Applied to Fruit Image Processing
Naranjo-Torres, J., Mora, M., Hernández-García, R., Barrientos, R. J., Fredes, C., & Valenzuela, A. (2020) · Applied Sciences · doi:10.3390/app10103443 · Cited by 406
Metadata
- Authors
- 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., Lim, J., & MacDonald, B. A.
- Venue
- Biosystems Engineering
- Publisher
- Elsevier BV
- Published
- 2019-05
- Volume
- 181
- Pages
- 140-156
- ISSN
- 1537-5110 (ISSN-L)
- OpenAlex
- W2929667367
- Cited by
- 346
- References
- 27
- FWCI
- 22.00
- Type
- journal article
- Language
- en
- Open access
- green
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- 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., Lim, J., & MacDonald, B. A. (2019). Robotic kiwifruit harvesting using machine vision, convolutional neural networks, and robotic arms. Biosystems Engineering. https://doi.org/10.1016/j.biosystemseng.2019.03.007
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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.
