Predicting maize yield loss with crop-weed leaf cover ratios determined with UAS imagery

Abstract

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 (UAS) 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 yield loss based on weed-crop leaf cover ratios. A yield loss model for maize (Zea mays L.) was evaluated for accuracy using 15- and 30-m altitude aerial RGB 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 3000 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 RMSE 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 prior to canopy closure, making it possible for growers to plan production and financial decisions before the end of the growing season.