The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.
Application of consumer RGB-D cameras for fruit detection and localization in field: A critical review
Open accessAbstract
Abstract Fruit detection and localization are essential for future agronomic management of fruit crops such as yield prediction, yield mapping and automated harvesting. However, to perform robust and efficient fruit detection and localization in orchard is a challenging task under variable illumination, low-resolutions and heavy occlusion by neighboring fruits, foliage, or branches. Therefore, researches of fruit detection and localization by getting more information of objects are essential. RGB-D (Red, Green, Blue -Depth) cameras are promising sensors and widely used in fruit detection and localization given that they provide depth information and infrared information in addition to RGB information. After presenting a discussion on the advantages and disadvantages of RGB-D cameras with different depth measurement principles and application fields, this paper reviews various types of RGB-D sensor systems and image processing methods used for fruit detection and localization in the field. Finally, major challenges for the successful application of RGB-D camera-based machine vision system, and potential future directions for the research and development in this area are discussed.
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Cites
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Deep learning – Method overview and review of use for fruit detection and yield estimation
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Recognition and Localization Methods for Vision-Based Fruit Picking Robots: A Review
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Sensors and systems for fruit detection and localization: A review
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Self-citationImage Segmentation for Fruit Detection and Yield Estimation in Apple Orchards
Bargoti, S., & Underwood, J. P. (2017) · Journal of Field Robotics · doi:10.1002/rob.21699 · Cited by 495
A review of key techniques of vision-based control for harvesting robot
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Multi-class fruit-on-plant detection for apple in SNAP system using Faster R-CNN
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Self-citationLow-Cost 3D Systems: Suitable Tools for Plant Phenotyping
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Fast implementation of real-time fruit detection in apple orchards using deep learning
Kang, H., & Chen, C. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2019.105108 · Cited by 243
Fruit detection, segmentation and 3D visualisation of environments in apple orchards
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Vision-based navigation and guidance for agricultural autonomous vehicles and robots: A review
Bai, Y., Zhang, B., Xu, N., Zhou, J., Shi, J., & Diao, Z. (2023) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2022.107584 · Cited by 353
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
Review of Weed Detection Methods Based on Computer Vision
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Metadata
- Authors
- Fu, L., Gao, F., Wu, J., Li, R., Karkee, M., & Zhang, Q.
- Venue
- Computers and Electronics in Agriculture
- Publisher
- Elsevier BV
- Published
- 2020-10
- Volume
- 177
- Pages
- 105687
- ISSN
- 0168-1699 (ISSN-L)
- OpenAlex
- W3048025158
- Cited by
- 331
- References
- 57
- FWCI
- 22.25
- Type
- journal article
- Language
- en
- Open access
- bronze
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