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In order to solve the problems of low accuracy of cucumber leaf disease image segmentation in complex background, a new segmentation algorithm of cucumber leaf disease image based on saliency detection (SCLDSD) was proposed. The proposed algorithm mainly consists of two parts: saliency detection in cucumber disease image which is used to get the leaf extraction and image segmentation which is used to get cucumber leaf disease. The algorithm first used the superpixel segmentation method to divide the cucumber image into blocks, got the edge of cucumber leaf preferably, and proposed a new method to calculate the weights among different superpixels. Then the algorithm used Harris points and convex hull to select saliency seeds. After using manifold ranking to compute the saliency map, the threshold segmentation was adopted on the obtained saliency map to get the binary map. At last, the cucumber disease leaf and background of the original image were separated by adding the binary map to the original image. In order to obtain the disease parts, ExG was used to expand the disparity of green parts and lesion parts and then threshold was used to carry out the segmentation. Finally, the morphological operation was processed in order to obtain fuller lesion. The proposed algorithm was tested on common cucumber disease images. The experimental result shows that the algorithm effectively solves the redundant segmentation and its more accurate with the error rate less than 5% and the average execution time of the algorithm less than 4.000ms in segmentation. From the results it can be concluded that the algorithm verifies the feasibility and practicality of the saliency detection algorithm in processing of disease images. Meanwhile it lays the foundation for the subsequent establishment of the automatic identification system of cucumber disease.