{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T15:13:14Z","timestamp":1777129994814,"version":"3.51.4"},"reference-count":21,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,5,11]],"date-time":"2023-05-11T00:00:00Z","timestamp":1683763200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan","award":["AP09259379"],"award-info":[{"award-number":["AP09259379"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/data8050088","type":"journal-article","created":{"date-parts":[[2023,5,12]],"date-time":"2023-05-12T02:36:59Z","timestamp":1683859019000},"page":"88","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["A Multispectral UAV Imagery Dataset of Wheat, Soybean and Barley Crops in East Kazakhstan"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0519-3222","authenticated-orcid":false,"given":"Almasbek","family":"Maulit","sequence":"first","affiliation":[{"name":"Laboratory of Digital Technologies and Modeling, Sarsen Amanzholov East Kazakhstan University, Ust-Kamenogorsk 070004, Kazakhstan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5522-4421","authenticated-orcid":false,"given":"Aliya","family":"Nugumanova","sequence":"additional","affiliation":[{"name":"Big Data and Blockchain Technologies Research Innovation Center, Astana IT University, Astana 010000, Kazakhstan"}]},{"given":"Kurmash","family":"Apayev","sequence":"additional","affiliation":[{"name":"Department of Information Technologies, D. Serikbayev East Kazakhstan Technical University, Ust-Kamenogorsk 070000, Kazakhstan"}]},{"given":"Yerzhan","family":"Baiburin","sequence":"additional","affiliation":[{"name":"Laboratory of Digital Technologies and Modeling, Sarsen Amanzholov East Kazakhstan University, Ust-Kamenogorsk 070004, Kazakhstan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3153-6356","authenticated-orcid":false,"given":"Maxim","family":"Sutula","sequence":"additional","affiliation":[{"name":"Laboratory of Biotechnology and Plant Breeding, National Center for Biotechnology, Astana 010000, Kazakhstan"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Nhamo, L., Magidi, J., Nyamugama, A., Clulow, A.D., Sibanda, M., Chimonyo, V.G., and Mabhaudhi, T. (2020). Prospects of improving agricultural and water productivity through unmanned aerial vehicles. Agriculture, 10.","DOI":"10.3390\/agriculture10070256"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Hegarty-Craver, M., Polly, J., O\u2019Neil, M., Ujeneza, N., Rineer, J., Beach, R.H., and Temple, D.S. (2020). Remote crop mapping at scale: Using satellite imagery and UAV-acquired data as ground truth. Remote Sens., 12.","DOI":"10.3390\/rs12121984"},{"key":"ref_3","unstructured":"Shamshiri, R.R., Hameed, I.A., Balasundram, S.K., Ahmad, D., Weltzien, C., and Yamin, M. (2018). Agricultural Robots-Fundamentals and Applications, IntechOpen."},{"key":"ref_4","first-page":"192","article-title":"Implementation of drone technology for farm monitoring & pesticide spraying: A review","volume":"10","author":"Hafeez","year":"2022","journal-title":"Inf. Process. Agric."},{"key":"ref_5","first-page":"012022","article-title":"A review on the use of drones for precision agriculture","volume":"Volume 275","author":"Daponte","year":"2019","journal-title":"IOP Conference Series: Earth and Environmental Science"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Li, M., Shamshiri, R.R., Weltzien, C., and Schirrmann, M. (2022). Crop Monitoring Using Sentinel-2 and UAV Multispectral Imagery: A Comparison Case Study in Northeastern Germany. Remote Sens., 14.","DOI":"10.3390\/rs14174426"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Liu, Y., Hatou, K., Aihara, T., Kurose, S., Akiyama, T., Kohno, Y., and Omasa, K. (2021). A robust vegetation index based on different UAV RGB images to estimate SPAD values of naked barley leaves. Remote Sens., 13.","DOI":"10.3390\/rs13040686"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"107559","DOI":"10.1016\/j.compag.2022.107559","article-title":"Optimization of soil background removal to improve the prediction of wheat traits with UAV imagery","volume":"205","author":"Tarquis","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"830","DOI":"10.1007\/s11119-021-09863-2","article-title":"In-season variable rate nitrogen recommendation for wheat precision production supported by fixed-wing UAV imagery","volume":"23","author":"Zhang","year":"2022","journal-title":"Precis. Agric."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"V\u00e9lez, S., Vacas, R., Mart\u00edn, H., Ruano-Rosa, D., and \u00c1lvarez, S. (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, 7.","DOI":"10.3390\/data7110157"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"108575","DOI":"10.1016\/j.dib.2022.108575","article-title":"CoFly-WeedDB: A UAV image dataset for weed detection and species identification","volume":"45","author":"Krestenitis","year":"2022","journal-title":"Data Brief"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"108738","DOI":"10.1016\/j.dib.2022.108738","article-title":"Avo-AirDB: An avocado UAV Database for agricultural image segmentation and classification","volume":"45","author":"Amraoui","year":"2022","journal-title":"Data Brief"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"108848","DOI":"10.1016\/j.dib.2022.108848","article-title":"Dataset on UAV RGB videos acquired over a vineyard including bunch labels for object detection and tracking","volume":"46","author":"Valente","year":"2023","journal-title":"Data Brief"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"108876","DOI":"10.1016\/j.dib.2022.108876","article-title":"Dataset on unmanned aerial vehicle multispectral images acquired over a vineyard affected by Botrytis cinerea in northern Spain","volume":"46","author":"Valente","year":"2023","journal-title":"Data Brief"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"108841","DOI":"10.1016\/j.dib.2022.108841","article-title":"Datasets supporting the adoption of multifunctional cover crops related to soil water and nitrogen in water-limited environments","volume":"46","author":"Garba","year":"2023","journal-title":"Data Brief"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"107756","DOI":"10.1016\/j.dib.2021.107756","article-title":"Soybean images dataset for caterpillar and Diabrotica speciosa pest detection and classification","volume":"40","author":"Mignoni","year":"2022","journal-title":"Data Brief"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"108087","DOI":"10.1016\/j.dib.2022.108087","article-title":"Deep potato\u2013the hyperspectral imagery of potato cultivation with reference agronomic measurements dataset: Towards potato physiological features modeling","volume":"42","author":"Ruszczak","year":"2022","journal-title":"Data Brief"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"821","DOI":"10.5194\/essd-15-821-2023","article-title":"A 250 m annual alpine grassland AGB dataset over the Qinghai\u2013Tibet Plateau (2000\u20132019) in China based on in situ measurements, UAV photos, and MODIS data","volume":"15","author":"Zhang","year":"2023","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1038\/s41597-023-02010-8","article-title":"The H AI nich: A multidisciplinary vision data-set for a better understanding of the forest ecosystem","volume":"10","author":"Milz","year":"2023","journal-title":"Sci. Data"},{"key":"ref_20","unstructured":"Agisoft, L.L.C. (2021). Metashape, 1.8, Agisoft."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/sdata.2016.18","article-title":"The FAIR Guiding Principles for scientific data management and stewardship","volume":"3","author":"Wilkinson","year":"2016","journal-title":"Sci. Data"}],"container-title":["Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5729\/8\/5\/88\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:33:04Z","timestamp":1760124784000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5729\/8\/5\/88"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,11]]},"references-count":21,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["data8050088"],"URL":"https:\/\/doi.org\/10.3390\/data8050088","relation":{},"ISSN":["2306-5729"],"issn-type":[{"value":"2306-5729","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,11]]}}}