Abstract: Identification of plastic-mulched farmland using UAV image is quite few. This paper proposes a method of combining with texture features, image morphology algorithm and threshold segmentation algorithm to extract plastic-mulched farmland using UAV Red-Green-Blue (RGB) images. In order to test the performance of this method, this paper took Ludian County of Zhaotong City, Yunnan Province as the research area, and obtained 2 images in the research area as experimental data. The complexity of land cover type in the 2 images was different. In complex area, the main land cover types included vegetation, impervious layer (building and road), plastic-mulched farmland (mainly black plastic mulch with a small amount of white plastic mulch), and bare soil (containing the plastic residues of a previous year). In simple area, the land cover types were similar with those in complex area; however, all plastic-mulched farmland was covered by black plastic mulch and there were no plastic residues in bare soil. Firstly, we calculated the gray level co-occurrence matrix of 2 images in different window sizes (3×3, 5×5, 7×7, 9×9, 11×11, 13×13, 15×15), directions (0, 45°, 90° and 135°) and steps (1, 2 and 3) and extracted 8 texture features from each band of RGB images including mean, variance, synergy, contrast, dissimilarity, information entropy, second moment and correlation. Secondly, we combined the original RGB image with different texture features to make maximum likelihood classification and determined the best extraction parameters of the texture features by comparing the overall pixel accuracy, user accuracy and product accuracy of the plastic mulch in complex area. The best extraction parameters of texture features were the window size of 15×15, the direction of 0, and the step of 2, which were also used to extract texture features of the image in simple area. Thirdly, we selected the optimal texture combination based on importance evaluation of texture features using Random Forest Algorithm and combined them with original UAV RGB image to make maximum likelihood and get preliminary classification maps in both complex area and simple area. Fourthly, we recoded the preliminary classification maps into binary maps (1 refers to plastic mulch and 0 refers to the other land cover types) and made majority filtering to remove noises (such as the plastic residues of a previous year). Then, we used image morphology algorithms to convert the strip plastic mulch into the plastic-mulched farmland and set area threshold to extract plastic-mulched farmland distribution. The area thresholds were 35 m2 in complex area and 500 m2 in simple area. Finally, taking the digitized mulched farmland as references (ground truth data), the accuracy of the recognition results of mulched farmland was assessed by error matrix and area error. The results showed that the texture features extracted by the optimal parameters could greatly improve the classification accuracy. The image morphology algorithm and the threshold segmentation method could effectively extract the block-shaped plastic-mulched farmland. The overall accuracy, Kappa coefficient, product accuracy, user accuracy and area error were 94.84%, 0.89, 92.48%, 93.39%, 0.38% in complex area, and 96.74%, 0.93, 97.39%, 94.63%, 1.95% in simple area, respectively. Compared with step and direction, the size of window had greater influence on plastic mulch classification accuracy. Among 8 texture features, mean contributed most to extracting plastic mulch. The method of extracting plastic-mulched farmland based on the fusion of supervised classification and image morphology algorithm proposed in this paper can provide reference for the development of identification algorithm about plastic-mulched farmland.