IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0196302.html
   My bibliography  Save this article

A fully convolutional network for weed mapping of unmanned aerial vehicle (UAV) imagery

Author

Listed:
  • Huasheng Huang
  • Jizhong Deng
  • Yubin Lan
  • Aqing Yang
  • Xiaoling Deng
  • Lei Zhang

Abstract

Appropriate Site Specific Weed Management (SSWM) is crucial to ensure the crop yields. Within SSWM of large-scale area, remote sensing is a key technology to provide accurate weed distribution information. Compared with satellite and piloted aircraft remote sensing, unmanned aerial vehicle (UAV) is capable of capturing high spatial resolution imagery, which will provide more detailed information for weed mapping. The objective of this paper is to generate an accurate weed cover map based on UAV imagery. The UAV RGB imagery was collected in 2017 October over the rice field located in South China. The Fully Convolutional Network (FCN) method was proposed for weed mapping of the collected imagery. Transfer learning was used to improve generalization capability, and skip architecture was applied to increase the prediction accuracy. After that, the performance of FCN architecture was compared with Patch_based CNN algorithm and Pixel_based CNN method. Experimental results showed that our FCN method outperformed others, both in terms of accuracy and efficiency. The overall accuracy of the FCN approach was up to 0.935 and the accuracy for weed recognition was 0.883, which means that this algorithm is capable of generating accurate weed cover maps for the evaluated UAV imagery.

Suggested Citation

  • Huasheng Huang & Jizhong Deng & Yubin Lan & Aqing Yang & Xiaoling Deng & Lei Zhang, 2018. "A fully convolutional network for weed mapping of unmanned aerial vehicle (UAV) imagery," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-19, April.
    • Handle: RePEc:plo:pone00:0196302
    DOI: 10.1371/journal.pone.0196302
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0196302
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0196302&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0196302?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. José Manuel Peña & Jorge Torres-Sánchez & Ana Isabel de Castro & Maggi Kelly & Francisca López-Granados, 2013. "Weed Mapping in Early-Season Maize Fields Using Object-Based Analysis of Unmanned Aerial Vehicle (UAV) Images," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-1, October.
    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. Jinkang Jiao & Ying Zang & Chaowen Chen, 2024. "Key Technologies of Intelligent Weeding for Vegetables: A Review," Agriculture, MDPI, vol. 14(8), pages 1-41, August.
    2. Steven B Kim & Dong Sub Kim & Xiaoming Mo, 2021. "An image segmentation technique with statistical strategies for pesticide efficacy assessment," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-12, March.
    3. Nur Adibah Mohidem & Nik Norasma Che’Ya & Abdul Shukor Juraimi & Wan Fazilah Fazlil Ilahi & Muhammad Huzaifah Mohd Roslim & Nursyazyla Sulaiman & Mohammadmehdi Saberioon & Nisfariza Mohd Noor, 2021. "How Can Unmanned Aerial Vehicles Be Used for Detecting Weeds in Agricultural Fields?," Agriculture, MDPI, vol. 11(10), pages 1-27, October.

    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.

      More about this item

      Statistics

      Access and download statistics

      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:plo:pone00:0196302. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

      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.