STC-PFTF-Net: Structure-Guided Prior Fusion for Cover-Crop Canopy Delineation in Residue-Rich UAV Imagery

Delineating early cover-crop vegetation in unmanned aerial vehicle (UAV) imagery requires preserving slender leaves amid cast shadows, desiccated residue, and heterogeneous soil. Weak foliage contrast and leaf-like background edges make appearance and geometry jointly important. We propose the Shadow, Topology, and Chromatic-aware Prior Feature Topology Fusion Network (STC-PFTF-Net), which integrates structure-guided reconstruction with complementary appearance representations. A coherence-guided tangent–normal decomposition preserves tangential sampling displacement and attenuates displacement across local structures. Chromatic–photometric encoding, directional wavelet filtering, and local connectivity refinement support this reconstruction throughout the feature hierarchy. On the 32 validation tiles of the Residue-rich Cover-Crop UAV Dataset, drawn from an area spatially separated from training, STC-PFTF-Net achieves 77.71% foreground intersection over union (IoU), 50.88% Boundary IoU, 83.84% centerline Dice (clDice), and 87.35% F1. At a common decision threshold, the complete model also exceeds the comparison architectures in all four measures. Spatial analyses associate its higher boundary recall with vegetation–background transitions and identify low-greenness foliage as a distinct source of omission. These findings show the value of combining appearance information with explicit reconstruction geometry for fine-scale canopy delineation. With 2.12 million parameters, the model offers a compact representation for UAV-derived vegetation mapping and subsequent canopy-cover assessment.