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Evaluation of Maize Crop Damage Using UAV-Based RGB and Multispectral Imagery

Author

Listed:
  • Barbara Dobosz

    (Department of Biometry, Institute of Agriculture, Warsaw University of Life Sciences, Nowoursynowska 159, 02-776 Warsaw, Poland)

  • Dariusz Gozdowski

    (Department of Biometry, Institute of Agriculture, Warsaw University of Life Sciences, Nowoursynowska 159, 02-776 Warsaw, Poland)

  • Jerzy Koronczok

    (Agrocom Polska, Strzelecka 47, 47-120 Żędowice, Poland)

  • Jan Žukovskis

    (Department of Business and Rural Development Management, Vytautas Magnus University, 53361 Kaunas, Lithuania)

  • Elżbieta Wójcik-Gront

    (Department of Biometry, Institute of Agriculture, Warsaw University of Life Sciences, Nowoursynowska 159, 02-776 Warsaw, Poland)

Abstract

The accurate evaluation of crop damage by wild animals is crucial for farmers when seeking compensation from insurance companies or other institutions. One of the game species that frequently cause crop damage in Europe is the wild boar, which often feeds on maize. Other game species, such as roe deer and red deer, can also cause significant crop damage. This study aimed to assess the accuracy of crop damage evaluation based on remote sensing data derived from unmanned aerial vehicles (UAVs), especially a digital surface model (DSM) based on RGB imagery and NDVI (normalized difference vegetation index) derived from multispectral imagery, at two growth stages of maize. During the first growth stage, when plants are in the intensive growth phase and green, crop damage evaluation was conducted using both DSM and NDVI. Each variable was separately utilized, and both variables were included in the classification and regression tree (CART) analysis, wherein crop damage was categorized as a binomial variable (with or without crop damage). In the second growth stage, which was before harvest when the plants had dried, only DSM was employed for crop damage evaluation. The results for both growth stages demonstrated high accuracy in detecting areas with crop damage, but this was primarily observed for areas larger than several square meters. The accuracy of crop damage evaluation was significantly lower for smaller or very narrow areas, such as the width of a single maize row. DSM proved to be more useful than NDVI in detecting crop damage as it can be applied at any stage of maize growth.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:8:p:1627-:d:1219840
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    References listed on IDEAS

    as
    1. Parthasarathy Velusamy & Santhosh Rajendran & Rakesh Kumar Mahendran & Salman Naseer & Muhammad Shafiq & Jin-Ghoo Choi, 2021. "Unmanned Aerial Vehicles (UAV) in Precision Agriculture: Applications and Challenges," Energies, MDPI, vol. 15(1), pages 1-19, December.
    2. Longfei Zhou & Xiaohe Gu & Shu Cheng & Guijun Yang & Meiyan Shu & Qian Sun, 2020. "Analysis of Plant Height Changes of Lodged Maize Using UAV-LiDAR Data," Agriculture, MDPI, vol. 10(5), pages 1-14, May.
    3. 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.
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