Fractional green canopy cover (FGCC) is a key diagnostic variable that can be used to estimate canopy development, light interception, and evapotranspiration partitioning. Available image analysis tools for quantifying FGCC are time‐consuming or expensive and cannot analyze video. Our objective was to develop a simple, accurate, and rapid tool to analyze FGCC from images and videos. This tool, called Canopeo, was developed using Matlab and is based on color ratios of red to green (R/G) and blue to green (B/G) and an excess green index (2G–R–B). The output from this tool was compared to that from two software packages widely used to analyze FGCC, SamplePoint, and SigmaScan Pro. Canopeo's image processing speed was 20 to 130 times faster than SigmaScan and 75 to 2500 times faster than SamplePoint. Canopeo correctly classified 90% of pixels when compared to SamplePoint. Root mean squared difference (RMSD) values for Canopeo FGCC vs. FGCC determined by SamplePoint and SigmaScan ranged from 0.04 to 0.12, with an average RMSD of 0.073 across several sets of images of corn (Zea mays L.), forage sorghum [Sorghum bicolor (L.) Moench], bermuda grass [Cynodon dactylon (L.) Pers.], and switchgrass (Panicum virgatum L.). Analysis of video recordings of transects over crop canopies proved to be useful to minimize sampling error and to quantify FGCC spatial variability. This analysis was simple and rapid with Canopeo but not possible with SamplePoint or SigmaScan. The Canopeo app for Matlab and for iOS and Android mobile devices can be downloaded at www.canopeoapp.com.

Since 1980, average wheat (Triticum aestivum L.) yields have remained nearly stagnant in the southern Great Plains (SGP) and stagnant in the state of Oklahoma. Yield stagnation can sometimes be attributed to a relatively small gap between current and potential yields, but the magnitude of the yield gap for this region has not been well quantified. The objective of this study was to determine the wheat yield and production gaps in Oklahoma at state and county levels. This involved estimation of attainable yield (Y_a) using a frontier yield function and water‐limited potential yield (Y_p) using estimated transpiration and transpiration efficiency. Yield gap and production gap relative to Y_a and Y_p were calculated using grain yields and harvested area for 19 counties. Current average yield (Y_c) was 2.06 Mg ha⁻¹ at the state level, well below the maximum recorded yield at the plot level of 6.59 Mg ha⁻¹. The Y_p of current wheat varieties is far above Y_c in Oklahoma, and Y_c represents 74% of Y_a but only 30% of Y_p at state level. For growing season rainfall (GSRF) amount <250 mm wheat yields were often water‐limited. However, average GSRF was 471 mm, and yield was typically limited by factors other than GSRF amount. Production exhibited greater temporal variability than yield, and production gap may be a better indicator than yield gap for regions with highest potential to increase production. Low yields and yield stagnation in Oklahoma cannot be attributed to a small remaining yield gap, nor to inadequate GSRF amount.