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