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

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.

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Publication Details

Journal
Agriculture
Published
2025-02-13
DOI
https://doi.org/10.3390/agriculture15040394
Citations
5
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
8.58
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article

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

Gallina M. Mihova, Asparuh Atanasov, Atanas Z. Atanasov, V. Vlăduţ
5 citations
Agriculture
Remote Sensing in Agriculture
8.58
article

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

Gallina M. Mihova, Asparuh Atanasov, Atanas Z. Atanasov, V. Vlăduţ
article en
5 citations

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.

AgricultureVol. 15(4)
Technical University of Varna (BG), The National Institute of Research – Development for Machines and Installations Designed for Agriculture and Food Industry (RO), Agricultural Academy (BG), Angel Kanchev University of Ruse (BG)
Openalex Percentile: Top 3%
Remote Sensing in Agriculture
8.58
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