The precise identification and height positioning of the bifurcation points of sugarcane tips is one of the key technologies for achieving realtime control of sugarcane harvester cutters, and is also an important way to improve the mechanization level of sugarcane harvesting and reduce sugarcane impurity content. In response to the complex environment of sugarcane fields, significant changes in lighting, and mutual obstruction of sugarcane bifurcation points, the field investigations, on-site testing and analysis of the characteristics of sugarcane growth points, sugarcane bifurcation points, and their interrelationships were firstly conducted, statistical analysis of sugarcane bifurcation points in images was collected, and combined with on-site measurement and statistical analysis of the height of sugarcane bifurcation points, it was found that they all had obvious normal statistical characteristics. Secondly, a sugarcane tip bifurcation point recognition method was proposed based on improved YOLO v5s. In this method, monocular and binocular cameras were used to collect sugarcane image data in Fusui Agricultural Science Base of Guangxi University, and data preprocessing and labeling were carried out to build a data set of sugarcane tip bifurcation points. Then BiFPN feature fusion structure and CA attention mechanism were introduced into the backbone network of YOLO v5s to enhance the interaction and expression ability of different levels of features, and using GSConv convolution, Slim-Neck normal form design, and the Ghost module was introduced into the original model backbone network to replace the original ordinary convolution in Neck, in order to reduce the computational and parameter complexity of the model and improve its operational efficiency. Finally, the effectiveness and superiority of this method were verified through training and testing on on-site collected datasets. The experimental results showed that this method achieved an average accuracy of 92.3%, a recall rate of 89.3%, and a detection time of 19.3ms on the sugarcane tip bifurcation point dataset. Compared with the original YOLO v5s network, the average accuracy was improved by 5 percentage points, the recall rate was improved by 4 percentage points, the parameter quantity was reduced by 43%, the model size was reduced by 5.5MB, and the detection time was reduced by 0.7ms. Finally, based on the obvious normal statistical characteristics of sugarcane bifurcation points, this feature can be combined with binocular vision positioning algorithms to lay a theoretical and technical foundation for conducting research on feature recognition of sugarcane harvester cuttings, height positioning of cuttings, and real-time control.