Accurate estimation of disease severity for downy mildew of greenhouse cucumber is a prerequisite for scientific disease control. It is of great significance to reduce the use of pesticides and to improve the quality of greenhouse cucumber, as well as farmers’ income. With the application of machine learning in the field of plant disease diagnosis, estimating the severity of plant diseases is gaining concerns. In order to increase the accuracy, this paper used the digital images of greenhouse cucumber downy mildew and machine learning methods to estimate the disease severity for downy mildew of greenhouse cucumber. A digital camera was used to collect images of greenhouse cucumber leaves with downy mildew, whose background were manually eliminated. An estimation model based on Convolutional Neural Network (CNN) was constructed with cucumber downy mildew leaf image as input. The initial symptom segmentation was achieved by using the combination of three visible color features (CVCF) and support vector machine. The segmentation results were optimized by using the speeded up robust feature (SURF) feature and morphological operation. After obtaining the segmentation image of cucumber downy mildew symptoms, the average and standard deviation of 15 color components in five color spaces of RGB, HSV, L*a*b*, YCbCr and HSI were extracted. On this basis, the gray level co-occurrence matrix was used to extract four texture features of each color component, including contrast, correlation, entropy and stability, resulting in 90 features. Pearson correlation analysis was used for feature selection. Shallow machine learning estimation models, including Support Vector Machine Regression and BP Neural Network, were constructed based on the image features with high correlation with the actual severity value of downy mildew of greenhouse cucumber. Based on the three estimation models, the disease severity for downy mildew of cucumber was estimated. The accuracy of the three estimation models was quantitatively evaluated by using Coefficient of Determination (R2) and Normalized Root-Mean-Squared Error (NRMSE). The results showed that there was a good linear relationship between the severity of downy mildew of greenhouse cucumber estimated by the model and the actual values. The model based on CNN achieved the best accuracy, whose R2 was 0.919 0 and NRMSE was 23.33%, followed by the model based on BPNN, with R2 being 0.890 8, NRMSE being 24.64%, while the model based on SVR was the last, with R2 being 0.890 1 and NRMSE being 31.08%. The evaluation results showed that by using the digital images of cucumber downy mildew and the convolution neural network estimation model, the disease severity for downy mildew of greenhouse cucumber could be accurately estimated, which could provide support to the scientific control of downy mildew of greenhouse cucumber and reduce the use of pesticides.