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Unmanned Aerial Vehicle (UAV) and Spectral Datasets in South Africa for Precision Agriculture

Author

Listed:
  • Cilence Munghemezulu

    (Agricultural Research Council—Natural Resources and Engineering, Private Bag X79, Pretoria 0001, South Africa)

  • Zinhle Mashaba-Munghemezulu

    (Agricultural Research Council—Natural Resources and Engineering, Private Bag X79, Pretoria 0001, South Africa)

  • Phathutshedzo Eugene Ratshiedana

    (Agricultural Research Council—Natural Resources and Engineering, Private Bag X79, Pretoria 0001, South Africa)

  • Eric Economon

    (Agricultural Research Council—Natural Resources and Engineering, Private Bag X79, Pretoria 0001, South Africa)

  • George Chirima

    (Agricultural Research Council—Natural Resources and Engineering, Private Bag X79, Pretoria 0001, South Africa)

  • Sipho Sibanda

    (Agricultural Research Council—Natural Resources and Engineering, Private Bag X79, Pretoria 0001, South Africa)

Abstract

Remote sensing data play a crucial role in precision agriculture and natural resource monitoring. The use of unmanned aerial vehicles (UAVs) can provide solutions to challenges faced by farmers and natural resource managers due to its high spatial resolution and flexibility compared to satellite remote sensing. This paper presents UAV and spectral datasets collected from different provinces in South Africa, covering different crops at the farm level as well as natural resources. UAV datasets consist of five multispectral bands corrected for atmospheric effects using the PIX4D mapper software to produce surface reflectance images. The spectral datasets are filtered using a Savitzky–Golay filter, corrected for Multiplicative Scatter Correction (MSC). The first and second derivatives and the Continuous Wavelet Transform (CWT) spectra are also calculated. These datasets can provide baseline information for developing solutions for precision agriculture and natural resource challenges. For example, UAV and spectral data of different crop fields captured at spatial and temporal resolutions can contribute towards calibrating satellite images, thus improving the accuracy of the derived satellite products.

Suggested Citation

  • Cilence Munghemezulu & Zinhle Mashaba-Munghemezulu & Phathutshedzo Eugene Ratshiedana & Eric Economon & George Chirima & Sipho Sibanda, 2023. "Unmanned Aerial Vehicle (UAV) and Spectral Datasets in South Africa for Precision Agriculture," Data, MDPI, vol. 8(6), pages 1-14, May.
    • Handle: RePEc:gam:jdataj:v:8:y:2023:i:6:p:98-:d:1159692
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    References listed on IDEAS

    as
    1. Kathryn Elmer & Raymond J. Soffer & J. Pablo Arroyo-Mora & Margaret Kalacska, 2020. "ASDToolkit: A Novel MATLAB Processing Toolbox for ASD Field Spectroscopy Data," Data, MDPI, vol. 5(4), pages 1-15, October.
    2. 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.
    3. Maria C. Torres-Madronero & Manuel Goez & Manuel A. Guzman & Tatiana Rondon & Pablo Carmona & Camilo Acevedo-Correa & Santiago Gomez-Ortega & Mariana Durango-Flórez & Smith V. López & July Galeano & M, 2022. "Spectral Library of Maize Leaves under Nitrogen Deficiency Stress," Data, MDPI, vol. 8(1), pages 1-10, December.
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