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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
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    Citations

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    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.

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