Abstract: To address the issues of low recognition accuracy and weak generalization ability for tobacco leaf curing status due to gradual color change and vein overlapping, a recognition model TOC-YOLO for tobacco leaf curing status in bulk curing barns based on the improved YOLOv11n was proposed. The model enhanced the extraction of tobacco leaf morphological features by introducing the VanillaNet module. In addition, by embedding the SENetV2 module and the BiFormer attention mechanism to strengthen the visual perception of color features and capture of spatial features of tobacco leaves, the model's ability to understand complex spatial structures and occlusion relationships was improved. The model adopted the HS-FPN module to achieve multi-scale feature fusion, enhancing its adaptability to the dynamic morphological changes of tobacco leaves during curing, and integrated the ASFF module to reduce the misjudgment rate of adjacent curing status. The results showed that: 1) In the test set, the bounding box precision (Bp), recall (R), F1 score and mAP_0.50-0.95 of the TOC-YOLO model were 96.9%, 96.1%, 96.5% and 98.2% respectively, and its comprehensive performance was superior to comparative models such as YOLOv11n. 2) Under the same experimental conditions, each of the improvement measure promoted the performance of the TOC-YOLO model, achieving favorable experimental results. 3) In the recognition of various curing status, the average F1 score of the TOC-YOLO model was 96.5%, and the average mAP_0.50-0.95 was 98.2%, indicating strong adaptability to different curing status. 4) The TOC-YOLO model exhibited excellent feature perception ability in key curing stages such as the late yellowing stage, late color-fixing stage and late stem-drying stage. 5) On the generalization test set, compared with the baseline YOLOv11n model, the Bp, R, F1 score and mAP_0.50-0.95 of the TOC-YOLO model were increased by 5.3, 2.5, 4.0 and 0.8 percentage points respectively, demonstrating strong cross-scenario generalization capability. This study provides technical support for the recognition of tobacco leaf curing status in bulk curing barns.