IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v11y2021i2p113-d491030.html
   My bibliography  Save this article

Weed Management and Economic Analysis of a Robotic Lawnmower: A Case Study in a Japanese Pear Orchard

Author

Listed:
  • Muhammad Zakaria Hossain

    (United Graduate School of Agricultural Science, Tokyo University of Agriculture and Technology, 3-8-1 Harumi-cho, Fuchu-shi, Tokyo 183-8538, Japan
    Farm Machinery and Postharvest Process Engineering Division, Bangladesh Agricultural Research Institute, Gazipur 1701, Bangladesh)

  • Masakazu Komatsuzaki

    (Center for International Field Agriculture Research & Education, Ibaraki University, Ami, Ibaraki 300-0393, Japan)

Abstract

The use of robots is increasing in agriculture, but there is a lack of suitable robotic technology for weed management in orchards. A robotic lawnmower (RLM) was installed, and its performance was studied between 2017 and 2019 in a pear orchard (1318 m 2 ) at Ibaraki University, Ami. We found that the RLM could control the weeds in an orchard throughout a year at a minimum height (average weed height, WH: 44 ± 15 mm, ± standard deviation (SD) and dry weed biomass, DWB: 103 ± 25 g m −2 ). However, the RLM experiences vibration problems while running over small pears (33 ± 8 mm dia.) during fruit thinning periods, which can stop blade mobility. During pear harvesting, fallen fruits (80 ± 12 mm dia.) strike the blade and become stuck within the chassis of the RLM; consequently, the machine stops frequently. We estimated the working performance of a riding mower (RM), brush cutter (BC), and a walking mower (WM) in a pear orchard and compared the mowing cost (annual ownership, repair and maintenance, energy, oil, and labor) with the RLM. The study reveals that the RLM performs better than other conventional mowers in a small orchard (0.33 ha). For a medium (0.66 ha) and larger (1 ha) orchard, the RLM is not more cost-effective than RM and WM. However, the existing RLM performed weed control well and showed promise for profitability in our research field. We believe that, if field challenges like fallen fruit and tree striking problems can be properly addressed, the RLM could be successfully used in many small orchards.

Suggested Citation

  • Muhammad Zakaria Hossain & Masakazu Komatsuzaki, 2021. "Weed Management and Economic Analysis of a Robotic Lawnmower: A Case Study in a Japanese Pear Orchard," Agriculture, MDPI, vol. 11(2), pages 1-16, February.
  • Handle: RePEc:gam:jagris:v:11:y:2021:i:2:p:113-:d:491030
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/11/2/113/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/11/2/113/
    Download Restriction: no
    ---><---

    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:11:y:2021:i:2:p:113-:d:491030. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.