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This research aims to determine the effectiveness of multispectral vegetation indices for early yield prediction in winter wheat varieties across different growth stages.

May 9, 2026AgronomyOpen Access

Yield Prediction of Winter Wheat (Triticum aestivum) Varieties Using UAV-Derived Multispectral Vegetation Indices Across Growth Stages

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AAAsparuh I. AtanasovTechnical University of Varna AAAsparuh I. AtanasovTechnical University of Varna AAAtanas AtanasovTechnical University of Varna

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Randomized trial investigates yield prediction in winter wheat using UAV multispectral data and indices.

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This research aims to determine the effectiveness of multispectral vegetation indices for early yield prediction in winter wheat varieties across different growth stages.
Data collected from nine winter wheat varieties using a UAV with multispectral sensors.
Five vegetation indices analyzed: NDVI, EVI2, SAVI (NIR-based) and MPRI, MGVRI (RGB-based).
Assessment of ground sampling distance and processing error conducted for data accuracy.
Strongest correlations between yield and NIR-based indices on 26 April, R2 values from 0.98 to 0.99.
RGB-based indices had the strongest prediction in early December, R2 = 0.99, weakest on 26 April, R2 = 0.38–0.40.
Predictive capabilities varied significantly across growth stages, indicating the importance of optimal temporal data selection.

Cite This Study

Atanasov et al. (2026) studied this question.

synapsesocial.com/papers/69fecfafb9154b0b828769ef https://doi.org/10.3390/agronomy16090939

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Yield Prediction of Winter Wheat (Triticum aestivum) Varieties Using UAV-Derived Multispectral Vegetation Indices Across Growth Stages

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Cite This Study

Yield Prediction of Winter Wheat (Triticum aestivum) Varieties Using UAV-Derived Multispectral Vegetation Indices Across Growth Stages

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Key Points

Cite This Study

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