Long-Term Assessment of NDVI Dynamics in Winter Wheat ( Triticum aestivum ) Using a Small Unmanned Aerial Vehicle

Asparuh I. Atanasov, Gallina M. Mihova, Atanas Z. Atanasov () and Valentin Vlăduț ()
Additional contact information
Asparuh I. Atanasov: Department of Mechanics and Elements of Machines, Technical University of Varna, 9010 Varna, Bulgaria
Gallina M. Mihova: Agricultural Academy, Dobrudzha Agriculture Institute, 9521 General Toshevo, Bulgaria
Atanas Z. Atanasov: Department of Agricultural Machinery, Agrarian and Industrial Faculty, University of Ruse “Angel Kanchev”, 7017 Ruse, Bulgaria
Valentin Vlăduț: National Research—Development Institute for Machines and Installations Designed to Agriculture and Food Industry, 013813 Bucharest, Romania

Agriculture, 2025, vol. 15, issue 4, 1-26

Abstract: The application of reflective vegetation indices is crucial for advancing precision agriculture, particularly in monitoring crop growth and development. Among these indices, the Normalized Difference Vegetation Index (NDVI) is the most widely used due to its reliability in capturing vegetation dynamics. This study focuses on the applicability and temporal dynamics of the NDVI in monitoring winter wheat ( Triticum aestivum ) under the specific climatic conditions of Southern Dobrudja, Bulgaria. Using a Survey3W Camera RGN mounted on DJI unmanned aerial vehicles (Phantom 4 Pro and Mavic 2 Pro) at an altitude of 100 m, NDVI data were collected over a five-year period (2019–2024). Results reveal distinct NDVI trends, with maximum values reaching 0.56 during favorable conditions, and sharp declines during late spring frosts or drought periods. These NDVI variations correlate strongly with environmental factors, including precipitation and temperature fluctuations. For instance, during the 2019–2020 season, the NDVI decreased by 30% due to severe drought and high winter temperatures. In this study, vegetation indices, including the Soil-Adjusted Vegetation Index (SAVI) and the Enhanced Vegetation Index (EVI), were utilized to compare the results with the NDVI. The high-resolution UAV methodology demonstrated in this study proves highly effective for breeding and agronomic applications, offering precise data for optimizing wheat cultivation under variable agro-climatic conditions. These findings highlight the NDVI’s potential to enhance crop monitoring, yield prediction, and stress response management in winter wheat.

Keywords: precision agriculture; unmanned aerial vehicle (UAV); camera; multispectral camera; vegetation indices; NDVI; NIR; RGB (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/15/4/394/pdf (application/pdf)
https://www.mdpi.com/2077-0472/15/4/394/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:15:y:2025:i:4:p:394-:d:1590375

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().