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

Almasbek Maulit (), Aliya Nugumanova (), Kurmash Apayev, Yerzhan Baiburin and Maxim Sutula
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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

Data, 2023, vol. 8, issue 5, 1-13

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.

Keywords: multispectral UAV imagery; NDVI; remote sensing; crop monitoring; yield prediction; precision agriculture (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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