Recognition and Localization Methods for Vision-Based Fruit Picking Robots: A Review

The utilization of machine vision and its associated algorithms improves the efficiency, functionality, intelligence, and remote interactivity of harvesting robots in complex agricultural environments. Machine vision and its associated emerging technology promise huge potential in advanced agricultu...

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Vydáno v:Frontiers in plant science Ročník 11; s. 520170
Hlavní autoři: Tang, Yunchao, Chen, Mingyou, Wang, Chenglin, Luo, Lufeng, Li, Jinhui, Lian, Guoping, Zou, Xiangjun
Médium: Journal Article
Jazyk:angličtina
Vydáno: Lausanne Frontiers Media SA 19.05.2020
Frontiers Media S.A
Témata:
3D reconstruction
Agricultural equipment
agricultural harvesting robotic
Agriculture
Algorithms
Artificial intelligence
Business metrics
classification
Crops
Deep learning
Fault tolerance
Fruits
Harvest
Harvesting
Image processing
Image reconstruction
Localization
Machine learning
Machine vision
Marking and tracking techniques
Occlusion
Picking
Plant Science
recognition
Robotics
Robots
Target recognition
Tracking
vision
Vision systems
ISSN:1664-462X, 1664-462X
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Shrnutí:The utilization of machine vision and its associated algorithms improves the efficiency, functionality, intelligence, and remote interactivity of harvesting robots in complex agricultural environments. Machine vision and its associated emerging technology promise huge potential in advanced agricultural applications. However, machine vision and its precise positioning still have many technical difficulties, making it difficult for most harvesting robots to achieve true commercial applications. This article reports the application and research progress of harvesting robots and vision technology in fruit picking. The potential applications of vision and quantitative methods of localization, target recognition, 3D reconstruction, and fault tolerance of complex agricultural environment are focused, and fault-tolerant technology designed for utilization with machine vision and robotic systems are also explored. The two main methods used in fruit recognition and localization are reviewed, including digital image processing technology and deep learning-based algorithms. The future challenges brought about by recognition and localization success rates are identified: target recognition in the presence of illumination changes and occlusion environments; target tracking in dynamic interference-laden environments, 3D target reconstruction, and fault tolerance of the vision system for agricultural robots. In the end, several open research problems specific to recognition and localization applications for fruit harvesting robots are mentioned, and the latest development and future development trends of machine vision are described.
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Edited by: Takumi Higaki, Kumamoto University, Japan
This article was submitted to Technical Advances in Plant Science, a section of the journal Frontiers in Plant Science
Reviewed by: Daniel Alexandre Neuwald, Competence Centre for Fruit Growing- Lake Constance, Germany; Itsuki Kunita, Research Institute for Electronic Science, Hokkaido University, Japan
ISSN:1664-462X
1664-462X
DOI:10.3389/fpls.2020.00510