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A Multispectral UAV Imagery Dataset of Wheat, Soybean and Barley Crops in East Kazakhstan

Author

Listed:
  • Almasbek Maulit

    (Laboratory of Digital Technologies and Modeling, Sarsen Amanzholov East Kazakhstan University, Ust-Kamenogorsk 070004, Kazakhstan)

  • Aliya Nugumanova

    (Big Data and Blockchain Technologies Research Innovation Center, Astana IT University, Astana 010000, Kazakhstan)

  • Kurmash Apayev

    (Department of Information Technologies, D. Serikbayev East Kazakhstan Technical University, Ust-Kamenogorsk 070000, Kazakhstan)

  • Yerzhan Baiburin

    (Laboratory of Digital Technologies and Modeling, Sarsen Amanzholov East Kazakhstan University, Ust-Kamenogorsk 070004, Kazakhstan)

  • Maxim Sutula

    (Laboratory of Biotechnology and Plant Breeding, National Center for Biotechnology, Astana 010000, Kazakhstan)

Abstract

This study introduces a dataset of crop imagery captured during the 2022 growing season in the Eastern Kazakhstan region. The images were acquired using a multispectral camera mounted on an unmanned aerial vehicle (DJI Phantom 4). The agricultural land, encompassing 27 hectares and cultivated with wheat, barley, and soybean, was subjected to five aerial multispectral photography sessions throughout the growing season. This facilitated thorough monitoring of the most important phenological stages of crop development in the experimental design, which consisted of 27 plots, each covering one hectare. The collected imagery underwent enhancement and expansion, integrating a sixth band that embodies the normalized difference vegetation index (NDVI) values in conjunction with the original five multispectral bands (Blue, Green, Red, Red Edge, and Near Infrared Red). This amplification enables a more effective evaluation of vegetation health and growth, rendering the enriched dataset a valuable resource for the progression and validation of crop monitoring and yield prediction models, as well as for the exploration of precision agriculture methodologies.

Suggested Citation

  • Almasbek Maulit & Aliya Nugumanova & Kurmash Apayev & Yerzhan Baiburin & Maxim Sutula, 2023. "A Multispectral UAV Imagery Dataset of Wheat, Soybean and Barley Crops in East Kazakhstan," Data, MDPI, vol. 8(5), pages 1-13, May.
    • Handle: RePEc:gam:jdataj:v:8:y:2023:i:5:p:88-:d:1144730
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    References listed on IDEAS

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    1. Luxon Nhamo & James Magidi & Adolph Nyamugama & Alistair D. Clulow & Mbulisi Sibanda & Vimbayi G. P. Chimonyo & Tafadzwanashe Mabhaudhi, 2020. "Prospects of Improving Agricultural and Water Productivity through Unmanned Aerial Vehicles," Agriculture, MDPI, vol. 10(7), pages 1-18, July.
    2. Sergio Vélez & Rubén Vacas & Hugo Martín & David Ruano-Rosa & Sara Álvarez, 2022. "High-Resolution UAV RGB Imagery Dataset for Precision Agriculture and 3D Photogrammetric Reconstruction Captured over a Pistachio Orchard ( Pistacia vera L.) in Spain," Data, MDPI, vol. 7(11), pages 1-11, November.
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