IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v10y2020i9p416-d416694.html
   My bibliography  Save this article

Imaging Using Unmanned Aerial Vehicles for Agriculture Land Use Classification

Author

Listed:
  • Pei-Chun Chen

    (Department of Landscape Architecture, National Chiayi University, Chiayi 60004, Taiwan)

  • Yen-Cheng Chiang

    (Department of Landscape Architecture, National Chiayi University, Chiayi 60004, Taiwan)

  • Pei-Yi Weng

    (Department of Plant Industry, National Pingtung University of Science and Technology, Pingtung 91201, Taiwan)

Abstract

An unmanned aerial vehicle (UAV) was used to capture high-resolution aerial images of crop fields. Software-based image analysis was performed to classify land uses. The purpose was to help relevant agencies use aerial imaging in managing agricultural production. This study involves five townships in the Chianan Plain of Chiayi County, Taiwan. About 100 ha of farmland in each township was selected as a sample area, and a quadcopter and a handheld fixed-wing drone were used to capture visible-light images and multispectral images. The survey was carried out from August to October 2018 and aerial photographs were captured in clear and dry weather. This study used high-resolution images captured from a UAV to classify the uses of agricultural land, and then employed information from multispectral images and elevation data from a digital surface model. The results revealed that visible-light images led to low interpretation accuracy. However, multispectral images and elevation data increased the accuracy rate to nearly 90%. Accordingly, such images and data can effectively enhance the accuracy of land use classification. The technology can reduce costs that are associated with labor and time and can facilitate the establishment of a real-time mapping database.

Suggested Citation

  • Pei-Chun Chen & Yen-Cheng Chiang & Pei-Yi Weng, 2020. "Imaging Using Unmanned Aerial Vehicles for Agriculture Land Use Classification," Agriculture, MDPI, vol. 10(9), pages 1-14, September.
  • Handle: RePEc:gam:jagris:v:10:y:2020:i:9:p:416-:d:416694
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/10/9/416/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/10/9/416/
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Jerzy Chojnacki & Aleksandra Pachuta, 2021. "Impact of the Parameters of Spraying with a Small Unmanned Aerial Vehicle on the Distribution of Liquid on Young Cherry Trees," Agriculture, MDPI, vol. 11(11), pages 1-13, November.
    2. Barbara Dobosz & Dariusz Gozdowski & Jerzy Koronczok & Jan Žukovskis & Elżbieta Wójcik-Gront, 2023. "Evaluation of Maize Crop Damage Using UAV-Based RGB and Multispectral Imagery," Agriculture, MDPI, vol. 13(8), pages 1-14, August.
    3. Naif Al Mudawi & Asifa Mehmood Qureshi & Maha Abdelhaq & Abdullah Alshahrani & Abdulwahab Alazeb & Mohammed Alonazi & Asaad Algarni, 2023. "Vehicle Detection and Classification via YOLOv8 and Deep Belief Network over Aerial Image Sequences," Sustainability, MDPI, vol. 15(19), pages 1-19, October.
    4. Hanchao Liu & Yuan Qi & Wenwei Xiao & Haoxin Tian & Dehua Zhao & Ke Zhang & Junqi Xiao & Xiaoyang Lu & Yubin Lan & Yali Zhang, 2022. "Identification of Male and Female Parents for Hybrid Rice Seed Production Using UAV-Based Multispectral Imagery," Agriculture, MDPI, vol. 12(7), pages 1-16, July.
    5. Mohammad Fatin Fatihur Rahman & Shurui Fan & Yan Zhang & Lei Chen, 2021. "A Comparative Study on Application of Unmanned Aerial Vehicle Systems in Agriculture," Agriculture, MDPI, vol. 11(1), pages 1-26, January.

    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:10:y:2020:i:9:p:416-:d:416694. 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.