YOLO11-FAL: An Improved YOLO11 Model for Tomato Flowering Stage Detection in Greenhouses

Accurate detection of tomato flowering stages is important for greenhouse crop management and automated pollination, but it remains challenging because tomato flowers are small, densely distributed, frequently occluded, and visually similar across adjacent developmental stages. To address these problems, this study proposes YOLO11-FAL, an improved object detection model based on YOLO11 for tomato flowering stage detection in greenhouse environments. The original C3k2 modules are replaced with C3k2_Faster to reduce redundant spatial computation and enhance local structural feature representation. An Attentional Scale Sequence Fusion (ASF) structure is introduced into the neck network to strengthen multi-scale feature interaction, and a Localization Quality Estimation Head (LQEHead) is incorporated to recalibrate classification confidence using bounding-box distribution information. Experiments were conducted on a self-constructed tomato flower dataset containing Bud, Anthesis, and Post-anthesis stages under varying illumination conditions. YOLO11-FAL achieved 92.00% Precision, 88.88% mAP@0.5, 62.21% mAP@0.75, and 56.72% mAP@0.5:0.95. The model contained 2.384 M parameters and achieved 49.58 FPS on an NVIDIA Jetson AGX Orin (NVIDIA Corporation, Santa Clara, CA, USA) using TensorRT FP16, with a measured onboard power consumption of 11.07 W. These results indicate that YOLO11-FAL provides a practical and efficient visual perception approach for greenhouse tomato flowering stage detection.