YOLO11n-Cucumber: An Efficient Model for Detecting Visual Indicators Associated with Selected Phenological Stages in Greenhouse-Grown Cucumber

Automated detection of visual indicator targets associated with selected phenological stages can support image-based monitoring of greenhouse cucumber. However, female flowers and young fruits are often small, partially occluded, and visually similar to adjacent developmental indicators, which complicates their detection in greenhouse scenes. In this study, YOLO11n-Cucumber was developed from YOLO11n for detecting seedling plants, female flowers, young fruits, and marketable fruits. The model incorporates an MDGC-GhostNetV3 backbone for feature extraction, a C2PSA-SCSA module for feature enhancement, and an MSF-CIoU loss for bounding-box regression. Experiments were conducted on a greenhouse cucumber dataset constructed in this study, comprising 3,849 images and 8,791 annotated instances. On the internal test set, YOLO11n-Cucumber achieved a precision of 86.4%, a recall of 84.2%, an mAP@0.5 of 86.8%, and an mAP@0.5:0.95 of 61.2%. Compared with YOLO11n, mAP@0.5 and mAP@0.5:0.95 increased by 2.3 and 4.1 percentage points, respectively, while floating-point operations (FLOPs) decreased from 6.4 G to 5.0 G. This reduction in FLOPs was accompanied by increases in the parameter count, from 2.6 M to 3.2 M, and in model size, from 5.6 MB to 6.8 MB. These results indicate that YOLO11n-Cucumber provides an improved accuracy–FLOPs trade-off under the greenhouse image-acquisition conditions of this study. Its generalizability across cultivars, greenhouse types, devices, and environmental conditions requires further evaluation.