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An Overview of the Application of Machine Vision in Recognition and Localization of Fruit and Vegetable Harvesting Robots

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  • Guangyu Hou

    (Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
    Science Island Branch, University of Science and Technology of China, Hefei 230026, China)

  • Haihua Chen

    (Institute of Computer Science, Chinese Academy of Sciences, Beijing 100190, China)

  • Mingkun Jiang

    (Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
    Science Island Branch, University of Science and Technology of China, Hefei 230026, China)

  • Runxin Niu

    (Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China)

Abstract

Intelligent agriculture imposes higher requirements on the recognition and localization of fruit and vegetable picking robots. Due to its unique visual information and relatively low hardware cost, machine vision is widely applied in the recognition and localization of fruit and vegetable picking robots. This article provides an overview of the application of machine vision in the recognition and localization of fruit and vegetable picking robots. Firstly, the advantages, disadvantages, and the roles of different visual sensors and machine vision algorithms in the recognition and localization of fruit and vegetable picking robots are introduced, including monocular cameras, stereo cameras, structured light cameras, multispectral cameras, image segmentation algorithms, object detection algorithms, and 3D reconstruction algorithms. Then, the current status and challenges faced by machine vision in the recognition and localization of fruit and vegetable picking robots are summarized. These challenges include the stability of fast recognition under complex background interference, stability of recognition under different lighting environments for the same crop, the reliance of recognition and localization on prior information in the presence of fruit overlap and occlusions caused by leaves and branches, and the uncertainty of picking caused by complex working environments. In current research on algorithms dealing with complex background interference and various occlusion disturbances, good results have been achieved. Different lighting environments have a significant impact on the recognition and positioning of fruits and vegetables, with a minimum accuracy of 59.2%. Finally, this article outlines future research directions to address these challenges.

Suggested Citation

  • Guangyu Hou & Haihua Chen & Mingkun Jiang & Runxin Niu, 2023. "An Overview of the Application of Machine Vision in Recognition and Localization of Fruit and Vegetable Harvesting Robots," Agriculture, MDPI, vol. 13(9), pages 1-31, September.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:9:p:1814-:d:1240031
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    References listed on IDEAS

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    1. Jinlong Wu & Sheng Zhang & Tianlong Zou & Lizhong Dong & Zhou Peng & Hongjun Wang & Mohammad Yaghoub Abdollahzadeh Jamalabadi, 2022. "A Dense Litchi Target Recognition Algorithm for Large Scenes," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-10, April.
    2. Peng Xu & Qian Tan & Yunpeng Zhang & Xiantao Zha & Songmei Yang & Ranbing Yang, 2022. "Research on Maize Seed Classification and Recognition Based on Machine Vision and Deep Learning," Agriculture, MDPI, vol. 12(2), pages 1-16, February.
    3. Wei Ji & Yu Pan & Bo Xu & Juncheng Wang, 2022. "A Real-Time Apple Targets Detection Method for Picking Robot Based on ShufflenetV2-YOLOX," Agriculture, MDPI, vol. 12(6), pages 1-18, June.
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    Cited by:

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    2. Chenggui Yang & Zhengda Cai & Mingjie Wu & Lijun Yun & Zaiqing Chen & Yuelong Xia, 2024. "Research on Detection Algorithm of Green Walnut in Complex Environment," Agriculture, MDPI, vol. 14(9), pages 1-22, August.

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