Stem and leaf angle detection is an important part of tobacco phenotype detection, which is of great significance in increasing yield and efficiency and disease prevention in tobacco farming. Aiming at the problems of low efficiency, long cycle time and inconvenience of manual stem and leaf angle detection in different environments, a lightweight tobacco stem and leaf angle detection model, FAL-YOLO, was designed and constructed.The algorithm builds the FAI backbone network structure to sufficiently reduce the amount of computation and feature redundancy, and increase the efficiency of using semantic information. The SAC detection head module, which integrates the spatial attention and channel attention SA attention modules, is designed to further reduce the number of parameters and improve the perception of stem and leaf angle features. GSConv lightweight convolution is introduced to reduce model complexity and the number of model parameters. The MPD-IoU loss function is introduced to improve the overall performance of the model. A self-constructed tobacco stem and leaf angle detection dataset is used to carry out the comparison and ablation experiments of the FAL-YOLO model. The experimental results show that the mAP of the FAL-YOLO model on the self-constructed dataset reaches 99.2%, compared with the YOLOV8-POSE model in the GFLOPs, the Params are reduced by 56.7% and 52%, respectively, and the improved model is capable of identifying the stem and leaf angles of tobacco plants faster and more accurately, which can support the wisdom of tobacco agricultural seed selection and breeding.