In this paper, a synthesis segmentation algorithm is designed for the real-time online diseased strawberry images in greenhouse. First, preprocess images to eliminate the impact of uneven illumination through the “top-hat” transform, and remove noise interference by median filtering. After comprehensively applying the methods of gray morphology, logical operation, OTSU and mean shift segmentation, we can obtain the complete strawberry fruit area of the image. Normalize the extracted eigenvalues, and use eigenvectors of part of the samples for training the BP neural network and support vector machine, the remaining samples were tested in two kinds of disease strawberry recognition model. Results show that support vector machines have a higher recognition rate than the BP neural network.