Due to labor shortage, modern agriculture goes up on automation gradually, the planting of sugarcane is no exception. If the automatic planting machine is used, sugarcane seedlings should be prepared in advance. A sugarcane node is the main place where bud is grown from. The existing sugarcane node cutting machines rely on human judgement to determine the node locations. There are time-consuming and laborious to collect the sugarcane nodes. This study intends to use machine vision to identify sugarcane nodes for developing automatic machine. The two algorithms of R-CNN and FASTER R-CNN were used to identify sugarcane node and to compare their performance. The R-CNN algorithm is usually used for the identification of multiple targets, and its accuracy is less than FASTER R-CNN, but the processing speed is faster. In this study, 530 sugarcane photos (1300 nodes) were analyzed, 400 and 130 sugarcane photos were selected as the calibration and validation groups, respectively. The experimental results show that the processing time of the R-CNN can be completed within 0.02 sec with the identification rate of 97.9%, and the processing time and identification rate of the FASTER R-CNN are similar to those of the R-CNN. The both algorithms have good results, and can be applied to the development of automated sugarcane node cutting machines.