Abstract: Abstract: Cucumber is one of the most common vegetables in China, which is severely affected by various diseases, such as downy mildew and powdery mildew. The process of recognizing diseases is often time consuming, laborious and subjective. Most of disease damage evaluation and treatment are done by farmers in the field with guidance of plant pathologists. Incorrect diagnosis and pesticide over usage are very common. Therefore, a timely and accurate recognition method of cucumber diseases is in great demand. Convolutional neural network is one of the most popular and best performing methods for image recognition. Because convolutional neural network has been extensively applied to agriculture applications, it is feasible to use convolutional neural network as the pattern recognition method for plant disease recognition. Convolutional neural network can automatically learn appropriate features from training datasets instead of manual feature extraction. The efforts on feature extraction and optimization can be saved. This not only reduces the computation cost, but also increases the accuracy and efficiency of the recognition. In this study, the state of the art convolutional neural network and deep learning techniques were applied to the recognition of cucumber diseases using visible leaf symptoms. A disease recognition system for greenhouse cucumbers based on convolutional neural network was presented in this paper based on deep learning and image processing. The key point of effective identification and diagnosis of diseases was to acquire the disease information accurately. With the development of computer vision technology, segmenting the disease symptom images from leaf images was presently considered as the main route of disease information acquisition. Color was the most direct information to discriminate disease symptoms from the other parts in a single image captured under real field conditions. Disease images captured under real field conditions were suffering from uneven illumination and complicated background, which was a big challenge to achieve robust disease symptom image segmentation. The symptom images were segmented by a novel image processing method using color information and region growing. Firstly, combinations of color features (CCF) and its detection method were presented. The combinations of color features consisted of three color components, excess red index (ExR), H component of HSV color space and B component of CIELAB color space, which implemented powerful discrimination of disease symptoms from clutter background. Then an interactive region growing method based on the comprehensive color feature map was used to achieve disease symptom image segmentation from clutter background. Input datasets was built from the symptom images. In order to decrease the chance of overfitting, data augmentation method that was to rotate the original datasets by 90, 160, 180 and 270 degrees and flip horizontally and vertically was utilized to enlarge the input datasets, which produced 12 augmented datasets. With the augmented input datasets, the system achieved good classification performance. Experiments were conducted to test the performance of the system. Results showed that the symptom image segmentation method can achieve an overall accuracy of 97.29%, which indicated that the method was capable of obtaining accurate and robust segmentation under real field conditions. The system achieved an overall accuracy of 95.7%, 93.1% for downy mildew and 98.4% for powdery mildew respectively, which indicated that the disease recognition system was capable of recognizing cucumber downy mildew and powdery mildew.