Introduction:Chlorophyll in wheat leaves is linked to grain yield, and measuring its reflectance allows predicting crop yield.
Objective: To evaluate the potential of GreenSeeker (GS) and SPAD502+ sensors, and spectral images acquired with an infrared camera fitted on an unmanned aerial vehicle (UAV) to estimate grain yield in wheat.
Methodology: Variable nitrogen concentrations were applied, and readings were taken with SPAD502+ and direct GS on wheat during the thickened sheath and heading phenological stages. Simultaneously, aerial images were captured with a multispectral camera to determine vegetation indices.
Results: The comparison between measured and estimated yields with optical sensors shows that GS presented the best fit, with a coefficient of determination (R2) of 0.86 in thickened sheaths. The fit with the green normalized difference vegetation index (GNDVI) was better in heading (R2 = 0.89).
Limitations of the study: Sensors with higher spectral resolution and earlier crop stages should be evaluated to estimate yield earlier.
Originality: The study shows the potential of optical sensors to estimate yield, thereby overcoming the time and economic resource limitations entailed in conventional methods.
Conclusions: GNDVI and GS are reliable, fast and non-destructive options to forecast wheat grain yield; however, UAVs allow scaling up crop monitoring and reducing the time to obtain field information.

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