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New Methodology for Corn Stress Detection Using Remote Sensing and Vegetation Indices

Author

Listed:
  • Nikola Cvetković

    (Department of Operational Research and Statistics, Faculty of Organizational Sciences, University of Belgrade, 11000 Belgrade, Serbia)

  • Aleksandar Đoković

    (Department of Operational Research and Statistics, Faculty of Organizational Sciences, University of Belgrade, 11000 Belgrade, Serbia)

  • Milan Dobrota

    (Agremo Ltd., 11070 Belgrade, Serbia)

  • Milan Radojičić

    (Department of Operational Research and Statistics, Faculty of Organizational Sciences, University of Belgrade, 11000 Belgrade, Serbia)

Abstract

Since corn is the second most widespread crop globally and its production has an impact on all industries, from animal husbandry to sweeteners, modern agriculture meets the task of preserving yield quality and detecting corn stress. Application of remote sensing techniques enabled more efficient crop monitoring due to the ability to cover large areas and perform non-destructive and non-invasive measurements. By using vegetation indices, it is possible to effectively measure the status of surface vegetation and detect stress on the field. This study describes the methodology for corn stress detection using red-green-blue (RGB) imagery and vegetation indices. Using the Excess Green vegetation index and calculated vegetation index histogram for healthy crop, corn stress has been effectively detected. The obtained results showed higher than 89% accuracy on both experimental plots, confirming that the proposed methodology can be used for corn stress detection using images acquired only with the RGB sensor. The proposed method does not depend on the sensor used for image acquisition and vegetation index used for stress detection, so it can be used in various different setups.

Suggested Citation

  • Nikola Cvetković & Aleksandar Đoković & Milan Dobrota & Milan Radojičić, 2023. "New Methodology for Corn Stress Detection Using Remote Sensing and Vegetation Indices," Sustainability, MDPI, vol. 15(6), pages 1-12, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:5487-:d:1102665
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    References listed on IDEAS

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    1. Fahad Khan & Pratibha Pandey & Tarun Kumar Upadhyay, 2022. "Applications of Nanotechnology-Based Agrochemicals in Food Security and Sustainable Agriculture: An Overview," Agriculture, MDPI, vol. 12(10), pages 1-13, October.
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    Cited by:

    1. Chunyan Zhu & Rong Li & Jinming Luo & Xi Li & Juan Du & Jun Ma & Chaoping Hou & Weizhen Zeng, 2024. "Research on Evaluating the Characteristics of the Rural Landscape of Zhanqi Village, Chengdu, China, Based on Oblique Aerial Photography by Unmanned Aerial Vehicles," Sustainability, MDPI, vol. 16(12), pages 1-23, June.

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