IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v12y2022i8p1240-d890375.html
   My bibliography  Save this article

An Overview of End Effectors in Agricultural Robotic Harvesting Systems

Author

Listed:
  • Eleni Vrochidou

    (HUMAIN-Lab, Department of Computer Science, School of Sciences, International Hellenic University (IHU), 65404 Kavala, Greece)

  • Viktoria Nikoleta Tsakalidou

    (HUMAIN-Lab, Department of Computer Science, School of Sciences, International Hellenic University (IHU), 65404 Kavala, Greece)

  • Ioannis Kalathas

    (HUMAIN-Lab, Department of Computer Science, School of Sciences, International Hellenic University (IHU), 65404 Kavala, Greece)

  • Theodoros Gkrimpizis

    (Laboratory of Viticulture, Faculty of Agriculture, Forestry and Natural Environment, School of Agriculture, Aristotle University of Thessaloniki (AUTh), 54124 Thessaloniki, Greece)

  • Theodore Pachidis

    (HUMAIN-Lab, Department of Computer Science, School of Sciences, International Hellenic University (IHU), 65404 Kavala, Greece)

  • Vassilis G. Kaburlasos

    (HUMAIN-Lab, Department of Computer Science, School of Sciences, International Hellenic University (IHU), 65404 Kavala, Greece)

Abstract

In recent years, the agricultural sector has turned to robotic automation to deal with the growing demand for food. Harvesting fruits and vegetables is the most labor-intensive and time-consuming among the main agricultural tasks. However, seasonal labor shortage of experienced workers results in low efficiency of harvesting, food losses, and quality deterioration. Therefore, research efforts focus on the automation of manual harvesting operations. Robotic manipulation of delicate products in unstructured environments is challenging. The development of suitable end effectors that meet manipulation requirements is necessary. To that end, this work reviews the state-of-the-art robotic end effectors for harvesting applications. Detachment methods, types of end effectors, and additional sensors are discussed. Performance measures are included to evaluate technologies and determine optimal end effectors for specific crops. Challenges and potential future trends of end effectors in agricultural robotic systems are reported. Research has shown that contact-grasping grippers for fruit holding are the most common type of end effectors. Furthermore, most research is concerned with tomato, apple, and sweet pepper harvesting applications. This work can be used as a guide for up-to-date technology for the selection of suitable end effectors for harvesting robots.

Suggested Citation

  • Eleni Vrochidou & Viktoria Nikoleta Tsakalidou & Ioannis Kalathas & Theodoros Gkrimpizis & Theodore Pachidis & Vassilis G. Kaburlasos, 2022. "An Overview of End Effectors in Agricultural Robotic Harvesting Systems," Agriculture, MDPI, vol. 12(8), pages 1-35, August.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:8:p:1240-:d:890375
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/12/8/1240/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/12/8/1240/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Vassilis G. Kaburlasos & Chris Lytridis & Eleni Vrochidou & Christos Bazinas & George A. Papakostas & Anna Lekova & Omar Bouattane & Mohamed Youssfi & Takashi Hashimoto, 2021. "Granule-Based-Classifier (GbC): A Lattice Computing Scheme Applied on Tree Data Structures," Mathematics, MDPI, vol. 9(22), pages 1-23, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Huimin Xu & Gaohong Yu & Chenyu Niu & Xiong Zhao & Yimiao Wang & Yijin Chen, 2023. "Design and Experiment of an Underactuated Broccoli-Picking Manipulator," Agriculture, MDPI, vol. 13(4), pages 1-18, April.
    2. Fu Zhang & Zijun Chen & Yafei Wang & Ruofei Bao & Xingguang Chen & Sanling Fu & Mimi Tian & Yakun Zhang, 2023. "Research on Flexible End-Effectors with Humanoid Grasp Function for Small Spherical Fruit Picking," Agriculture, MDPI, vol. 13(1), pages 1-18, January.
    3. Huawei Yang & Yinzeng Liu & Shaowei Wang & Huixing Qu & Ning Li & Jie Wu & Yinfa Yan & Hongjian Zhang & Jinxing Wang & Jianfeng Qiu, 2023. "Improved Apple Fruit Target Recognition Method Based on YOLOv7 Model," Agriculture, MDPI, vol. 13(7), pages 1-21, June.
    4. Łukasz Kuta & Piotr Komarnicki & Katarzyna Łakoma & Joanna Praska, 2023. "Tomato Fruit Quality as Affected by Ergonomic Conditions While Manually Harvested," Agriculture, MDPI, vol. 13(9), pages 1-18, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Qiangqiang Chen & Linjie He & Yanan Diao & Kunbin Zhang & Guoru Zhao & Yumin Chen, 2022. "A Novel Neighborhood Granular Meanshift Clustering Algorithm," Mathematics, MDPI, vol. 11(1), pages 1-15, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:12:y:2022:i:8:p:1240-:d:890375. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.