Benchmarking Object Detection and a Novel Self-Supervised Framework for Weed Detection in Soybean

Palmer amaranth is one of the most problematic weeds in soybean production in the United States and can cause major yield loss if not managed early. This study benchmarked eight object detection models for site-specific Palmer amaranth detection in soybean using high-resolution uncrewed aerial system (UAS) imagery, with the goal of supporting targeted herbicide application and reducing herbicide usage. The models YOLOv8m, YOLOv9m, YOLOv10m, YOLOv11m, Faster R-CNN, RetinaNet, RT-DETR, and a self-supervised Faster R-CNN variant were evaluated using five-fold cross-validation on 2064 annotated aerial RGB image tiles containing 5990 bounding-box instances across multiple growth stages and field conditions, with an additional 7615 unlabeled tiles used for self-supervised pretraining. All detectors followed an identical 150-epoch schedule with early stopping and were compared using Friedman with Iman–Davenport correction and post hoc Nemenyi tests. Detectors were assessed on three axes: detection accuracy (mAP and class-wise AP), operational spraying efficacy summarized by a threshold-independent weed coverage rate area under the curve (WCR-AUC), and computational deployment cost across batch sizes from 1 to 32. The YOLO models achieved the highest detection accuracy along with the lowest inference latency and memory use but showed weaker threshold-independent weed coverage; the two-stage Faster R-CNN models showed the opposite pattern. Weighing all three axes, YOLOv8m provided the most practical balance for real-time deployment. The study also introduced GeoCLR, a self-supervised pretraining framework that constructs positive pairs from UAS flight overlap rather than synthetic augmentation. GeoCLR produced more structured and class-discriminative features than ImageNet pretraining, and a detector fine-tuned on only half of the annotations recovered approximately 95% of full-data accuracy. Together, these results highlight the importance of operational metrics for practical model selection and show that self-supervised pretraining can reduce annotation effort for scalable precision agriculture.