In order to accurately and rapidly obtain the vegetation coverage information of summer corn during the stages of fourleaf, jointing, heading and flowering, unmanned aerial vehicles (UAV) was used to obtain visible light images of corn field, and various vegetation indices extracted from visible light bands were analyzed and compared. Visibleband difference vegetation index (VDVI), excess green (EXG) and normalized greenblue difference index (NGBDI) were used to extract the corn vegetation coverage information of the four stages combined with supervised classification method. In the research process, targets in a single image of the experimental field were divided into soil and corn vegetation in the four stages of the corn. The VDVI pixel histograms of soil and corn classified by supervised classification method were counted respectively, and the intersection points of pixel histogram were used as the threshold of vegetation coverage extraction. Similarly, the threshold of corn vegetation coverage extraction corresponding to EXG and NGBDI was obtained. Finally, the corn vegetation coverage of the four stages was extracted by the three extraction thresholds. The errors of vegetation coverage extraction corresponding to the four growth stages of VDVI were 1.21%, 4.88%, 2.31% and 3.61%, respectively; EXG were 1.38%, 1.25%, 0.89% and 0.33%, respectively; and NGBDI were 1.61%, 3.31%, 1.99% and 3.25%, respectively. It was found that EXG had the best effect on vegetation coverage extraction during the four stages of corn. The value of threshold determined by the single image of the four corn growth stages was used as a fixed threshold, and the vegetation coverage was extracted from the panoramic image of the experimental field that had removed the single image which was used as determining threshold value, and the extraction effect was verified. The results showed that the variation of extraction error was small, indicating that the method using the supervised classification combined with the statistical histogram of visible vegetation index to determine the threshold value was better.