IDEAS home Printed from https://ideas.repec.org/a/gam/jdataj/v7y2022i10p135-d924880.html
   My bibliography  Save this article

RIFIS: A Novel Rice Field Sidewalk Detection Dataset for Walk-Behind Hand Tractor

Author

Listed:
  • Padma Nyoman Crisnapati

    (Department of Mechatronics Engineering, Rajamangala University of Technology Thanyaburi, Pathum Thani 12110, Thailand)

  • Dechrit Maneetham

    (Department of Mechatronics Engineering, Rajamangala University of Technology Thanyaburi, Pathum Thani 12110, Thailand)

Abstract

Rice field sidewalk (RIFIS) identification plays a crucial role in enhancing the performance of agricultural computer applications, especially for rice farming, by dividing the image into areas of rice fields to be ploughed and the areas outside of rice fields. This division isolates the desired area and reduces computational costs for processing RIFIS detection in the automation of ploughing fields using hand tractors. Testing and evaluating the performance of the RIFIS detection method requires a collection of image data that includes various features of the rice field environment. However, the available agricultural image datasets focus only on rice plants and their diseases; a dataset that explicitly provides RIFIS imagery has not been found. This study presents an RIFIS image dataset that addresses this deficiency by including specific linear characteristics. In Bali, Indonesia, two geographically separated rice fields were selected. The initial data collected were from several videos, which were then converted into image sequences. Manual RIFIS annotations were applied to the image. This research produced a dataset consisting of 970 high-definition RGB images (1920 × 1080 pixels) and corresponding annotations. This dataset has a combination of 19 different features. By utilizing our dataset for detection, it can be applied not only for the time of rice planting but also for the time of rice harvest, and our dataset can be used for a variety of applications throughout the entire year.

Suggested Citation

  • Padma Nyoman Crisnapati & Dechrit Maneetham, 2022. "RIFIS: A Novel Rice Field Sidewalk Detection Dataset for Walk-Behind Hand Tractor," Data, MDPI, vol. 7(10), pages 1-16, September.
    • Handle: RePEc:gam:jdataj:v:7:y:2022:i:10:p:135-:d:924880
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2306-5729/7/10/135/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2306-5729/7/10/135/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Valda Rondelli & Bruno Franceschetti & Dario Mengoli, 2022. "A Review of Current and Historical Research Contributions to the Development of Ground Autonomous Vehicles for Agriculture," Sustainability, MDPI, vol. 14(15), pages 1-16, July.
    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. Padma Nyoman Crisnapati & Dechrit Maneetham, 2022. "Two-Dimensional Path Planning Platform for Autonomous Walk behind Hand Tractor," Agriculture, MDPI, vol. 12(12), pages 1-15, November.

    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. Valda Rondelli & Enrico Capacci & Bruno Franceschetti, 2022. "Evaluation of the Stability Behavior of an Agricultural Unmanned Ground Vehicle," Sustainability, MDPI, vol. 14(23), pages 1-13, November.
    2. Alexander V. Klokov & Egor Yu. Loktionov & Yuri V. Loktionov & Vladimir A. Panchenko & Elizaveta S. Sharaborova, 2023. "A Mini-Review of Current Activities and Future Trends in Agrivoltaics," Energies, MDPI, vol. 16(7), pages 1-18, March.

    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:jdataj:v:7:y:2022:i:10:p:135-:d:924880. 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.