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Automatic navigation is the core elements of agricultural intelligence, and the machine vision based navigation route detection is the core content of automatic navigation system. An algorithm based on support vector machine was proposed to detect paddy field boundary. Support vector machine, instead of traditional image segmentation algorithms, was used to segment the paddy field image, and the robustness of boundary detection under different illumination conditions was improved. Superpixel segmentation algorithm was used to obtain superpixels instead of pixels for subsequent image processing. Superpixels reduced the computational complexity and provided a large number of samples for model training of support vector machine. A sufficient number of superpixel samples were selected for extracting color features and texture features to form a 19-dimensional feature vector. Color features were statistical properties in RGB and HSV color spaces, including R average, G average, B average, H average, S average, V average, H variance, S variance and V variance. Texture features included gradient amplitude mean and weighted gradient direction histogram. Then support vector machine model was trained and used to identify the paddy ridge field in the new picture. In order to judge the performance of the algorithm, the superpixel classification results and the actual manual labeling results were compared based on the 50 images containing paddy ridge field. The recognition F1-score can reach 90.7%. Finally, Hough detection was used to extract the boundary of the paddy ridge field. It took less than 0.8s on NVIDIA’s Jetson TX2 hardware platform by the algorithm and can meet the real-time requirement of agricultural machinery.