Aiming at the issues that there may exist uneven illuminance conditions and complicated background on the disease spots images captured in real greenhouse field, this paper presented a segmenting method for greenhouse cucumber downy mildew images based on visual spectral and support vector machine. Firstly, a novel combination of the visible color features and its detection method were presented, based on which the support vector machine and SURF features were integrated to segment the disease spot from images. The combination of the visible color features combined ExR, H component of HSV color space and b* component of L*a*b* color space. Because ExR was very likely to be influenced by illuminance conditions, an ExR parameter was adopted to reduce the influence from illuminance conditions to the disease spots segmentation. On the basis of combination of the visible color features, initial segmentation results can be achieved by using RBF based SVM classifier. Then the initial segmentation results were further optimized by using SURF features to eliminate the background noises. Finally, the segmentation results were compared with K mean clustering, OTSU thresholding and decision tree. The results showed that the accuracy rate of OTSU+H*0.2, K-means+H+b*, DT+H+b* and proposed method were 19.44%, 40.19%, 16.27% and 7.37%, respectively. The accuracy rate of proposed method was obviously higher than that of the other three methods, which indicated that the proposed method can meet the data requirement of the following diagnosis for greenhouse cucumber downy mildew.