Abstract: Grassland vegetation coverage is an important parameter for characterizing vegetation condition in ecological research. With the popularization of digital devices, such as digital cameras, vegetation coverage can be measured in automatic or semi-automatic ways by analyzing the digital photographs using image processing platforms. Moreover, the rapid development and popularity of smart mobile devices (such as the iPhone/iPad) means that there is an opportunity to investigate the use of these devices for real-time collection, processing and analyzing of data such as vegetation cover. Five quadrats with different vegetation cover were selected and measured in different lighting conditions. Photographs were taken every 2 h from 6 AM to 6 PM. The RGB threshold value method, RGB decision tree method and HSV discriminant method were selected for calculating vegetation cover. When light intensity was constant, vegetation cover had no effect on the measurement precision of the RGB threshold value method and the HSV discriminant method, but greatly influenced the measurement precision of the RGB decision tree method. The RGB threshold value method and the HSV discriminant method were influenced by light condition; when light intensity increased cover estimates declined. However, there did not appear to be a good relationship between light intensity and cover estimates for the RGB threshold value method. Overall, the measurement precision of the RGB threshold value and HSV discriminant methods were higher than the RGB decision tree method but the latter could identify plant stems and flowers that were not green. Using the current green plant pixel identification methods combined with improved statistical analysis methods such as algorithms able to examine edge pixels further improvement of the precision of this technique could be achieved.