Maize is one of the most important food crops in China, which has the largest planting scale and the fastest growing trend. The growth of maize will directly affect its yield and quality. Therefore, through effective monitoring of the growth of maize can provide macro information for field management and yield estimation, and provide an important basis for decision-making by the relevant national departments. In this study, Unmanned Aerial Vehicle (UAV) equipped with an image sensor was used as a low-altitude remote sensing platform to obtain visible spectral remote sensing images of maize. First we made geometric and radiometric correction of maize canopy visible spectrum image by ENVI software, and then we made gray and enhancement processing of the color image. AP-HI algorithm was used to obtain the maize coverage information, for it has strong light adaptability to the complex background of farmland. The image was segmented by AP-HI algorithm and converted it into a binary image to remove the background of the land, water pipes, roads and residues in the image, so as to retain the binary image of maize. The road existed in the farmland of the image, which needed to be excluded when calculating the actual crop coverage. The road area appeared in the four boundaries and center of the image. The number of black pixels in the road area was counted and the road width was calculated according to the number of pixels, and then the road part was removed from the binary image. In the processed image, the white pixels are the non-crop area, and the black pixels are the maize planting area. In order to calculate the size of maize crops, the proportion of black pixels to total pixels in the binary image needed to be counted. The unit area was selected as 80×80 pixels, and the image was marked by blocks from top to bottom and left to right and got the number of blocks was 720. The unit area was scanned, and the proportion of the black pixels per unit area to the total number of pixels (6 400 pixels) was calculated. Until the 720 blocks were completely counted, the proportion of the number of black pixels to total pixels in the image could be calculated, which is the maize coverage. The relational model between coverage and Leaf Area Index (LAI) through canopy porosity was established to complete maize LAI inversion, so as to provide theoretical basis for monitoring maize growth. The results show that low altitude UAV visible spectrum remote sensing can be used as an effective method to extract crop coverage, which has a good prospect.