Focused on the existing problems that there are few image datasets of citrus diseases and insect pests, the targets of diseases and pests are complex and scattered, and are difficult to realize automatic location and segmentation, a segmentation method of agricultural citrus disease and pest areas based on Superpixel Fast Fuzzy C-means Clustering (SFFCM) and Support Vector Machine (SVM) was proposed. This method made full use of the advantages of SFFCM algorithm, which was fast and robust, and integrated the characteristics of spatial information, meanwhile, it did not require manual selection of samples in image segmentation like the traditional SVM. Firstly, the improved SFFCM segmentation algorithm was used to pre-segment the image to be segmented to obtain the foreground and background regions. Then, the erosion and dilation operations in morphology were used to narrow these two areas, and the training samples were automatically selected for SVM model training. Finally, the trained SVM classifier was used to segment the entire image. Experimental results show that compared with the following three methods:Fast and Robust Fuzzy C-means Clustering (FRFCM), the original SFFCM and Edge Guidance Network (EGNet), the proposed method has the average recall of 0.937 1, average precision of 0.941 8 and the average accuracy of 0.930 3, all of which are better than those of the comparison methods.