Fraction green canopy cover (FGCC) is widely used to determine active vegetative land cover, as well as used in crop models such as Aquacrop. Nonetheless, current methods for measuring canopy cover are time consuming or expensive, and limited to images. The objective of this project was to create a simple, fast, and accurate tool capable of quantifying FGCC using digital images or video. Canopeo was developed using Matlab programming language and is based on threshold color ratios of red to green and blue to green in the RGB system. The user can specify and pre-visualize the effect of the selected thresholds to achieve optimal FGCC recognition. In addition, the user has control over noise reduction and neighborhood size parameters. The noise reduction value reduces error by eliminating isolated green pixels that are selected by the chosen threshold ratios but are not part of the canopy cover (e.g. shadow, small weeds). Canopeo was compared with two common software products (i.e. SigmaScan Pro, and SamplePoint) to analyze FGCC for several crops including corn (Zea mays L.), grain sorghum (Sorghum bicolor), bermuda grass [Cynodon dactylon (L.) Pers.], and switchgrass (Panicum virgatum L.). Root mean squared error (RMSE) values ranged from 0.021 to 0.098, with an average RMSE of 0.068. Canopeo resulted 100 times faster than SigmaScan and 1000 faster than SamplePoint. Canopeo resulted in a simple, fast, and accurate tool with the distinct ability to preview user-generated settings and analyze video files.