The DOI matches its Crossref record; 76 of the 76 references it cites were found in a registry or an index.
Intelligent robots for fruit harvesting: recent developments and future challenges
Open accessAbstract
Intelligent robots for fruit harvesting have been actively developed over the past decades to bridge the increasing gap between feeding a rapidly growing population and limited labour resources. Despite significant advancements in this field, widespread use of harvesting robots in orchards is yet to be seen. To identify the challenges and formulate future research and development directions, this work reviews the state-of-the-art of intelligent fruit harvesting robots by comparing their system architectures, visual perception approaches, fruit detachment methods and system performances. The potential reasons behind the inadequate performance of existing harvesting robots are analysed and a novel map of challenges and potential research directions is created, considering both environmental factors and user requirements.
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Cites
INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS
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DeepFruits: A Fruit Detection System Using Deep Neural Networks
Sa, I., Ge, Z., Dayoub, F., Upcroft, B., Perez, T., & McCool, C. (2016) · Sensors · doi:10.3390/s16081222 · Cited by 1,104
A Review of Imaging Techniques for Plant Phenotyping
Li, L., Zhang, Q., & Huang, D. (2014) · Sensors · doi:10.3390/s141120078 · Cited by 1,094
Australian Journal of Crop Science
(2016) · Australian Journal of Crop Science · doi:10.21475/ajcs · Cited by 1,050
IEEE Robotics and automation letters
(n.d.) · GSI Repository (German Federal Government) · Cited by 687
Harvesting Robots for High‐value Crops: State‐of‐the‐art Review and Challenges Ahead
Bac, C. W., Henten, E. J. V., Hemming, J., & Edan, Y. (2014) · Journal of Field Robotics · doi:10.1002/rob.21525 · Cited by 686
Deep learning – Method overview and review of use for fruit detection and yield estimation
Koirala, A., Walsh, K. B., Wang, Z., & McCarthy, C. (2019) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2019.04.017 · Cited by 581
Recognition and Localization Methods for Vision-Based Fruit Picking Robots: A Review
Tang, Y., Chen, M., Wang, C., Luo, L., Li, J., Lian, G., & Zou, X. (2020) · Frontiers in Plant Science · doi:10.3389/fpls.2020.00510 · Cited by 575
Sensors and systems for fruit detection and localization: A review
Gongal, A., Amatya, S., Karkee, M., Zhang, Q., & Lewis, K. (2015) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2015.05.021 · Cited by 572
Metadata
- Authors
- Zhou, H., Wang, X., Au, W., Kang, H., & Chen, C.
- Venue
- Precision Agriculture
- Published
- 2022-06-02
- Volume
- 23
- Issue
- 5
- Pages
- 1856-1907
- ISSN
- 1385-2256 (ISSN-L)
- 1573-1618
- OpenAlex
- W4287848462
- Cited by
- 316
- References
- 76
- FWCI
- 30.94
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Zhou, H., Wang, X., Au, W., Kang, H., & Chen, C. (2022). Intelligent robots for fruit harvesting: recent developments and future challenges. Precision Agriculture. https://doi.org/10.1007/s11119-022-09913-3
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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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