UAV-Based Deep Learning for Weed Detection in Sugar Beet: A Case Study from Beni Mellal (Morocco) and Implications for Site-Specific Spraying

Herbicide overuse remains a major challenge in sugar beet production because of its environmental and economic impacts. This study addresses three key gaps in UAV-based weed mapping: the lack of leakage-aware benchmarks for North African sugar beet imagery, the limited controlled comparison of one-stage and two-stage detectors under identical experimental conditions, and the limited translation of detection outputs into decision-support layers for site-specific spraying. We develop a reproducible UAV-based deep learning pipeline and present a field case study from Beni Mellal, Morocco. Fast R-CNN, YOLOR, YOLOv7, and YOLOv5 were compared under a unified protocol using identical data partitions, input resolution, augmentation strategies, and evaluation metrics, with locally acquired RGB imagery, COCO-format annotations, and leakage-aware field/flight splits. Under the tested conditions, YOLOv5 achieved the strongest performance, with 97.82% precision, 83.05% recall, 91.61% mAP@0.5, and 72.63% mAP@0.5:0.95. Error analysis indicated that missed detections were mainly associated with small weeds, partial occlusion by sugar beet leaves, and visually similar broadleaf weeds. Detector outputs were further organized into weed-intensity maps and used in a pilot scan-guided spot-treatment workflow on the surveyed parcels. This pilot implementation demonstrates the feasibility of translating UAV detections into prescription layers, but it should not be interpreted as a complete multi-season agronomic or economic validation. The main contribution is therefore a leakage-aware, unified benchmarking protocol and a reproducible end-to-end workflow from UAV detections to field-ready prescription maps. Future work should quantify herbicide savings, treatment efficacy, yield response, economic return, edge-device throughput, and transferability across regions and seasons.