The sugarcane node is difficult to recognize due to the diversity and complexity of surface that different types of sugarcane have. To solve the problem, a sugarcane node recognition method suitable for different types of sugarcane was proposed based on machine vision. Firstly, by the iterative linear fitting algorithm, the target region was extracted from the original image and its slope angle to horizontal axis was estimated. According to the angle, the target was rotated to being nearly parallel to the horizontal axis. Secondly, Double-Density Dual Tree Complex Wavelet Transform (DD-DTCWT) was used to decompose the image, and the image was reconstructed by using the wavelet coefficients that were perpendicular or approximately perpendicular to the horizontal axis. Finally, the line detection algorithm was used to detect the image, and the lines near the sugarcane node were obtained. The recognition was realized by further verifying the density, length and mutual distances of the edge lines. Experimental results show that the complete recognition rate reaches 92%, the localization accuracy of about 80% of nodes is less than 16 pixels, and the localization accuracy of 95% nodes is less than 32 pixels. The proposed method realizes node recognition for different types of sugarcane under different background with high position accuracy.