Abstract: Cucumber disease leaf image segmentation is an important step in disease detection and disease type recognition. To overcome the shortcomings of the classical disease leaf segmentation methods, image semantic segmentation algorithm based on the Fully Convolution Networks (FCNs) had been widely used in the automatic segmentation of disease leaf images in the complex background. FCNs replaced the last three fully-connected layers with three convolutional layers so that the input image with any size could be accepted. FCNs classified images at the pixel level, resolving the problem of semantic segmentation at the semantic level. FCNs utilized the de-convolutional layer to upsample the feature map of the last convolutional layer and restored it to the same size of the input image so that each pixel could be generated. At the same time, the spatial information of the original input image was retained. Then, the pixel-by-pixel classification was carried out on the above feature maps. The disadvantages of FCNs were that 1) the segmented images by FCNs were still not precise enough. Although the result of 8 times sampling was much better than 32 times sampling, the result of upsampling was still blurred and smooth, and was insensitive to the details of the image; 2) Classification of each pixel did not fully consider the relationship between the pixels. The spatial regularization steps used in the usual segmentation methods based on pixel classification were neglected and lack of spatial consistency. Aiming at the low recognition accuracy problem of the traditional disease leaf image segmentation methods, the Multi-Scale Fusion Convolutional Neural Networks (MSF-CNNS) were proposed for cucumber disease leaf image segmentation. MSF-CNNs consisted of Encoder Networks (ENs) and Decoder Networks (DNs). ENs were composed of a multi-scale Convolutional Neural Networks to extract multi-scale information of images of disease leaves. DNs were a nine-point bilinear interpolation algorithm to restore the size and resolution of the input image. In the process of the model training, a transfer learning method with the gradual adjustment was used to accelerate the training speed and segmentation accuracy of the network model. The architecture of MSF-CNNs is similar to U-Net and SegNet, mainly including encoder networks and decoder networks. However, to extract the multi-scale information of the input image, a multilevel parallel structure was introduced into the encoding network, while a multi-scale connection was introduced into the decoding network. In the specific coding network, the multi-column parallel CNNs could be used to extract the multi-scale features of the image of crop disease leaves. In the decoding network, the size and resolution of the image were restored by introducing the nine-point bisector linear interpolation algorithm as the deconvolution interpolation method. In the structure of the overall network model, skip join was used to pass the characteristic information extracted from different convolutional layers, and batch normalization operation was introduced to alleviate the gradient dispersion phenomenon of the model. Segmentation experiments were carried out on the image database of cucumber disease leaves under the complex background and compared with the existing deep learning models, such as FCNs, SegNet, U-Net, and DenseNet. The results on the cucumber disease leaf image dataset validated that the proposed method met the needs of the cucumber disease leaf image segmentation in the complex environment, with pixel-classification accuracy of 92.38%, the average accuracy of 93.12%, mean intersection over the union of 91.36 and frequency weighted intersection over the union of 89.76%. Compared with FCNs, SegNet, U-NET, and DenseNet, the average accuracy of the proposed method is improved by 13.00%, 10.74%, 10.40%, 10.08%, and 6.40%, respectively. After using the progressive learning training method, the training time was reduced by 0.9 h. The results showed that the proposed method was effective for the image segmentation of the cucumber disease leaves in a complex environment, and could provide technical support for further research on cucumber disease detection and identification.