Research Article

UAV-based sunflower yield prediction using multispectral vegetation indices

Volume: 8 July 3, 2026

UAV-based sunflower yield prediction using multispectral vegetation indices

Abstract

Sunflower yield varies spatially and temporally depending on factors such as weather, altitude, seed variety, plant density, available water, nutrients, and sowing date. These are the primary factors influencing crop yield. With the use of unmanned aerial vehicles (UAVs), temporal resolution can be adjusted according to the user's needs, while spatial resolution depends on the capabilities of the sensor and flight altitude. In this study, vegetation indices derived from multispectral camera imagery were used for yield estimation through a linear regression model. Vegetation indices such as NDVI (Normalized Difference Vegetation Index), MCARI (Modified Chlorophyll Absorption in Reflectance Index), SAVI (Soil-Adjusted Vegetation Index), CIRE (Chlorophyll Index Red Edge), LCI (Leaf Chlorophyll Index), and GNDVI (Green Normalized Difference Vegetation Index) were calculated using green, red, red-edge, and near-infrared (NIR) bands. The values of these indices were obtained from UAV flights conducted on five different dates in the study area. In the regression model developed using the NDVI4 index value for the R-5 growth stage of the plant, yield estimates were obtained as 336.72 kg for Test Area 1, 381.77 kg for Test Area 2, and 400.62 kg for Test Area 3.

Keywords

References

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Details

Primary Language

English

Subjects

Remote Sensing

Journal Section

Research Article

Publication Date

July 3, 2026

Submission Date

October 27, 2025

Acceptance Date

December 19, 2025

Published in Issue

Year 2026 Volume: 8

APA
Erdoğan, A., Mutluoglu, Ö., & Gürsoy, Ö. (2026). UAV-based sunflower yield prediction using multispectral vegetation indices. Turkish Journal of Remote Sensing, 8. https://doi.org/10.51489/tuzal.1811280
AMA
1.Erdoğan A, Mutluoglu Ö, Gürsoy Ö. UAV-based sunflower yield prediction using multispectral vegetation indices. TJRS. 2026;8. doi:10.51489/tuzal.1811280
Chicago
Erdoğan, Alperen, Ömer Mutluoglu, and Önder Gürsoy. 2026. “UAV-Based Sunflower Yield Prediction Using Multispectral Vegetation Indices”. Turkish Journal of Remote Sensing 8 (July). https://doi.org/10.51489/tuzal.1811280.
EndNote
Erdoğan A, Mutluoglu Ö, Gürsoy Ö (July 1, 2026) UAV-based sunflower yield prediction using multispectral vegetation indices. Turkish Journal of Remote Sensing 8
IEEE
[1]A. Erdoğan, Ö. Mutluoglu, and Ö. Gürsoy, “UAV-based sunflower yield prediction using multispectral vegetation indices”, TJRS, vol. 8, July 2026, doi: 10.51489/tuzal.1811280.
ISNAD
Erdoğan, Alperen - Mutluoglu, Ömer - Gürsoy, Önder. “UAV-Based Sunflower Yield Prediction Using Multispectral Vegetation Indices”. Turkish Journal of Remote Sensing 8 (July 1, 2026). https://doi.org/10.51489/tuzal.1811280.
JAMA
1.Erdoğan A, Mutluoglu Ö, Gürsoy Ö. UAV-based sunflower yield prediction using multispectral vegetation indices. TJRS. 2026;8. doi:10.51489/tuzal.1811280.
MLA
Erdoğan, Alperen, et al. “UAV-Based Sunflower Yield Prediction Using Multispectral Vegetation Indices”. Turkish Journal of Remote Sensing, vol. 8, July 2026, doi:10.51489/tuzal.1811280.
Vancouver
1.Alperen Erdoğan, Ömer Mutluoglu, Önder Gürsoy. UAV-based sunflower yield prediction using multispectral vegetation indices. TJRS. 2026 Jul. 1;8. doi:10.51489/tuzal.1811280

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