Abstract
Weed competition in inter- and intra-row zones presents a substantial challenge to crop productivity, with intra-row weeds posing a particularly severe threat. Their proximity to crops and higher occlusion rates increase their negative impact on yields. This study examines the efficacy of advanced deep learning architectures-namely, Faster R-CNN, RT-DETR, and YOLOv11-in the accurate identification of weeds and crops within commercial maize fields. A comprehensive dataset was compiled under varied field conditions, focusing on three major weed species: Cyperus rotundus L., Echinochloa crus-galli L., and Solanum nigrum L. YOLOv11 demonstrated superior performance among the evaluated models, achieving a mean average precision (mAP) of 97.5% while operating in real-time at 34 frames per second (FPS). Faster R-CNN and RT-DETR models achieved a mAP of 91.9% and 97.2%, respectively, with processing capabilities of 11 and 27 FPS. Subsequent hardware evaluations identified YOLOv11m as the most viable solution for field deployment, demonstrating high precision with a mAP of 94.4% and lower energy consumption. The findings emphasize the feasibility of employing these advanced models for efficient inter- and intra-row weed management, particularly for early-stage weed detection with minimal crop interference. This study underscores the potential of integrating State-of-the-Art deep learning technologies into agricultural machinery to enhance weed control, reduce operational costs, and promote sustainable farming practices.