Mechanical harvesting requires agricultural robot to detect fruits automatically. However, effective and accurate detection of cucumber by computer vision system is still a challenge due to similarity between cucumber color and that of branches and leaves, shape irregularity and complex growing environment. To improve the practicability and accuracy of the automatic recognition models, this paper proposed a novel cucumber region detection method using multi-path convolutional neural network (MPCNN), combined with color component selection and support vector machine (SVM). In this method, the cucumber image was transformed into color space to obtain 15 color components and the weight information of relevant features was analyzed by I-RELIEF. In parallel, to remove part of the background area, the OSTU algorithm was applied to segment the G component and Maximally Stable Extrernal Regions (MSER) was used to obtain the mask image. In order to maximize the differences between cucumber and leaf, promoting the classification accuracy of SVM, the top three components of the weight were input into the deep learning module to extract and fuse features. In final, cucumber recognition was realized by combining SVM classification with mask image. The recognition results show that more than 90% pixels of cucumber images are correctly classified, and the misidentified pixels are less than 22%. The ratio between the two indicators is over 4, demonstrating the satisfactory performance of the proposed method and highlighting its promising applications in mechanical cucumber harvesting.