In order to achieve accurate and efficient automated seed cutting in sugarcane intelligent transverse seed cutting workstation, a method based on improved YOLO v5s for identifying and locating the edge end of sugarcane stem node features was proposed for the characteristics of factory seed cutting tasks. Firstly, the camera was corrected for distortion by using the ZHANG Zhengyou camera calibration method, then the sugarcane stem node dataset was enhanced and the original YOLO v5s model was used for training and testing, and the results showed that the data enhancement can improve the detection accuracy to some extent. Then, to address the problems of low accuracy and high model complexity caused by small stem node feature targets, the backbone network of YOLO v5s was improved by introducing the coordinate attention module and Ghost lightweight structure before the SPPF module, and removing the P5 large target detection head in the Head network to obtain the improved sugarcane stem node detection model YOLO v5s-CA-BackboneGhost-p34. The test results showed that the model outperformed other mainstream algorithms and the original model with high accuracy and small size. Among them, mAP@0.5 and mAP@0.5∶0.95 were improved by 5.2 and 16.5 percentage points, respectively, and the model computation and size were reduced by 42% and 51%, respectively. Finally, in order to improve the detection speed and real-time performance, the model was deployed at the edge end, and the detection speed was accelerated by using TensorRT technology, and the model was completed on a sugarcane with transmission speed of 0.15m/s. The actual seed cutting test were completed on the smart transverse seed cutting workstation with transmission speed of 0.15m/s. The test results showed that the accelerated stem node detection speed reached 95f/s, the average error of real-time detection and positioning was about 2.4mm, the seed cutting qualification rate was 100%, and the leakage rate was 0.4%, which indicated that the model proposed was highly reliable and practical, and can provide effective technical support for the industrialization, intelligence and standardization of sugarcane transverse seed cutting workstation.