Wheat Yield Prediction Based on Unmanned Aerial Vehicles-Collected Red-Green-Blue Imagery

Unmanned aerial vehicles-collected (UAVs) digital red-green-blue (RGB) images provided a cost-effective method for precision agriculture applications regarding yield prediction. This study aims to fully explore the potential of UAV-collected RGB images in yield prediction of winter wheat by comparing it to multi-source observations, including thermal, structure, volumetric metrics, and ground-observed leaf area index (LAI) and chlorophyll content under the same level or across different levels of nitrogen fertilization. Color indices are vegetation indices calculated by the vegetation reflectance at visible bands (i.e., red, green, and blue) derived from RGB images. The results showed that some of the color indices collected at the jointing, flowering, and early maturity stages had high correlation (R-2 = 0.76-0.93) with wheat grain yield. They gave the highest prediction power (R-2 = 0.92-0.93) under four levels of nitrogen fertilization at the flowering stage. In contrast, the other measurements including canopy temperature, volumetric metrics, and ground-observed chlorophyll content showed lower correlation (R-2 = 0.52-0.85) to grain yield. In addition, thermal information as well as volumetric metrics generally had little contribution to the improvement of grain yield prediction when combining them with color indices derived from digital images. Especially, LAI had inferior performance to color indices in grain yield prediction within the same level of nitrogen fertilization at the flowering stage (R-2 = 0.00-0.40 and R-2 = 0.55-0.68), and color indices provided slightly better prediction of yield than LAI at the flowering stage (R-2 = 0.93, RMSE = 32.18 g/m(2) and R-2 = 0.89, RMSE = 39.82 g/m(2)) under all levels of nitrogen fertilization. This study highlights the capabilities of color indices in wheat yield prediction across genotypes, which also indicates the potential of precision agriculture application using many other flexible, affordable, and easy-to-handle devices such as mobile phones and near surface digital cameras in the future.

Automated summary

This study explores the potential of UAV-collected RGB images in predicting winter wheat yield by comparing them to various other observations. The results show that color indices derived from RGB images have high correlation with grain yield, outperforming measurements like canopy temperature and ground-observed chlorophyll content. Additionally, color indices provide better yield prediction compared to leaf area index under different nitrogen fertilization levels, highlighting their capabilities in wheat yield prediction across genotypes.