The yield monitor is one of the most adopted precision agriculture technologies because it generates dense yield data to quantify the spatial variability of crop yield as a basis for site-specific management. However, yield monitor data has various errors that prevent proper interpretation and precise field management. The objective of this study was to evaluate the application of unmanned aerial systems (UAS) images in improving cotton yield monitor data. The study was conducted in a dryland field (~ 40 ha) in Garza County, Texas in 2020 and 2021. A yield monitor on a cotton stipper was applied to collect yield data. 44 hand-pick samples, each of 1 m2, were selected to determine the cotton yield. A UAS with a multispectral sensor was utilized to acquire high-resolution images before harvesting in two years. The measured yield data and UAS images data were used to build models for prediting and correcting cotton yield. The yield data from the monitor sensor was calibrated by UAS-derived yield. The accuracy of cotton data was compared to the ground-measured yield and raw yield monitor data. The new method integrates UAS images and yield monitor, providing an effective approach to estimate cotton yield for precision management.
