RSWNet: Lightweight Scale–Spatial–Frequency Feature Learning for UAV-Based Greenhouse Rose Monitoring

UAV-based greenhouse rose monitoring requires accurate detection of rose targets under substantial scale variation, foliage occlusion, background similarity, illumination changes, and motion-induced degradation while maintaining lightweight inference for practical real-time edge deployment. This study proposes RoseScaleWaveNet (RSWNet), a YOLO26n-based lightweight detector that coordinates scale, spatial context, and frequency information through three complementary modules: C3MKIR for multi-receptive-field feature extraction, SEASCPP for position-aware multi-scale contextual modeling, and MWAdaLF for frequency-decoupled cross-level feature fusion. Experiments on a multi-source greenhouse rose dataset annotated with two petal-opening classes show that RSWNet achieves 94.13% precision, 91.95% recall, 95.51% mAP@50, and 59.22% mAP@50:95 with only 2.30 M parameters and an inference speed of 185 FPS. Compared with YOLO26n, mAP@50 and mAP@50:95 are improved by 5.50 and 4.70 percentage points, respectively, while model complexity is slightly reduced. The proposed modules also yield an average mAP@50 gain of 4.24 percentage points when transferred to four representative YOLO architectures. Cross-domain evaluation on six external flower-related datasets further demonstrates consistent improvements under both zero-shot evaluation and dataset-specific retraining. Controlled tests under blur, noise, illumination variation, motion blur, and scale changes characterize robustness to the evaluated image perturbations. Separate benchmarks on desktop and laptop GPUs, CPU-only hardware, and Jetson Orin Nano quantify inference efficiency. These results support the potential of RSWNet for UAV-assisted greenhouse rose detection within the evaluated conditions.