Typically, weed density is used to predict weed-induced yield loss, as it is easy and quick to quantify, even though it does not account for weed size and time of emergence relative to the crop. Weed–crop leaf area relations, while more difficult to measure, inherently account for differences in plant size, representing weed–crop interference more accurately than weed density alone. Unmanned aerial systems (UASs) may allow for efficient quantification of weed and crop leaf cover over a large scale. It was hypothesized that UAS imagery could be used to predict maize (Zea mays L.) yield loss based on weed–crop leaf cover ratios. A yield loss model for maize was evaluated for accuracy using 15- and 30-m-altitude aerial red–green–blue and four-band multispectral imagery collected at four North Carolina locations. The model consistently over- and underpredicted yield loss when observed yield loss was less than and greater than 3,000 kg ha–¹, respectively. Altitude and sensor type did not influence the accuracy of the prediction. A correction for the differences between predicted and observed yield loss was incorporated into the linear model to improve overall precision. The correction resulted in r² increasing from 0.17 to 0.97 and a reduction in root mean-square error from 705 kg ha–¹ to 219 kg ha–¹. The results indicated that UAS images can be used to develop predictive models for weed-induced yield loss before canopy closure, making it possible for growers to plan production and financial decisions before the end of the growing season.