Combining multiple UAV-Based indicators for wheat yield estimation, a case study from Germany

ABSTRACT

Unmanned aircraft vehicles (UAV) are widely used for yield estimations in agricultural production. Many significant improvements have been made towards the usage of hyperspectral and thermal sensors. The practical application of these new techniques meanwhile has been limited by the cost of data collection and the complexities of data processing. The objective of this paper is to evaluate the effectiveness of wheat yield estimations based on integrating vegetation indices (VI), solar radiation and crop height (CH), all of which are characterized by lower cost of data collection and processing. The VIs, solar radiation and CH were calculated based on UAV-based multispectral images obtained from two separate plots in Southern Germany and validated with data from a third plot. We compare the individual and joint predictive performance of different VIs, CH, and solar radiation by contrasting the estimated yield with actual yield based on multiple linear regression and quantile regression. The best predictive power was found for a combined estimation with CH, solar radiation and a Normalized Difference Red-edge Index (R² = 0.75,  RMSE = 0.53). This combined estimation resulted in a 15–20% improvement in the prediction of wheat yield accuracy as compared with utilizing any of the indices separately.

KEYWORDS:

• Drone sensors
• crop height
• multiple linear regression
• quantile regression
• multiple indicators
• crop surface model

Acknowledgments

The authors express their gratitude to the team members of the project “Increasing Climate Resilience via Agricultural Insurance – Innovation Transfer for Sustainable Rural Development in Central Asia (KlimALEZ)”, implemented by the Leibniz-Institute for Agricultural Development in Transformation Economies (IAMO).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/22797254.2023.2294121.

Notes

¹ For data security reasons (private farms) we only gave the approximate location of the study area.

Additional information

Funding

This research was supported by the German Federal Ministry of Education and Research (BMBF) [01LZ1705A] and WGL Forschungsverbünde [D0093/3].