May 23, 2024Open Access

Wheat Yield Estimation using Machine Learning Method Based on UAV Remote Sensing Data

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A simple average ensemble method achieved superior wheat yield prediction accuracy with an R2 of 0.733 and RMSE of 0.668 t ha-1, outperforming individual machine learning models.
Assessment of multi-sensor data fusion evaluated five machine learning algorithms using drone-based multispectral and thermal infrared imagery across 270 wheat hybrids.
The extreme gradient boosting model showed peak single-model accuracy, indicating that tiered ensemble methods improve decision-making during agricultural breeding selection.

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Field evaluation demonstrates improved yield prediction accuracy in hybrid wheat under varied irrigation, indicating the utility of multi-sensor drone data fusion.

Cite This Study

Yang et al. (2024) studied this question.

synapsesocial.com/papers/68e68ac7b6db643587612e89 https://doi.org/10.20944/preprints202405.0402.v2

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Implication

Field evaluation demonstrates improved yield prediction accuracy in hybrid wheat under varied irrigation, indicating the utility of multi-sensor drone data fusion.

Wheat Yield Estimation using Machine Learning Method Based on UAV Remote Sensing Data

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Implication

Cite This Study

Wheat Yield Estimation using Machine Learning Method Based on UAV Remote Sensing Data

Key Points

Discussion

Implication

Cite This Study

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