Abstract: To address the problems of low recognition accuracy and weak generalization ability for tobacco leaf curing status under the conditions of gradual color change and overlapping vein occlusion, this paper proposes TOC-YOLO, a recognition model for tobacco leaf curing status in bulk curing barns based on the improved YOLOv11n.The model enhances the extraction of tobacco leaf morphological features by introducing the VanillaNet module; embeds the SENetV2 module and the BiFormer attention mechanism to strengthen the perception of color features and capture of spatial features of tobacco leaves, improving the model’s ability to understand complex spatial structures and occlusion relationships; adopts the HS-FPN module to achieve multi-scale feature fusion, enhancing adaptability to the dynamic morphological changes of tobacco leaves during curing; and integrates the ASFF module to reduce the misjudgment rate of adjacent curing statuses.The results show that:1) In the test set, the bounding box precision (B_p), recall (R), F1-score and mAP_0.50~0.95 of the TOC-YOLO model are 96.9%, 96.1%, 96.5% and 98.2% respectively, and its comprehensive performance is superior to comparative models such as YOLOv11n. 2) Under the same experimental conditions, each improvement measure promotes the performance improvement of the TOC-YOLO model, achieving favorable experimental results.3) In the recognition of various curing statuses, the average F1-score of the TOC-YOLO model is 96.5%, and the average mAP_0.50~0.95 is 98.2%, indicating strong adaptability to different curing statuses.4) The TOC-YOLO model exhibits 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 B_p, R, F1-score and mAP_0.50~0.95 of the TOC-YOLO model are 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.