Integrating Machine Learning and Path Planning for UAS-based Weed Recognition and Site-specific Management in Turfgrass Systems

Abstract Annual bluegrass ( Poa annua L.) is an extremely problematic weed in turfgrass, posing a significant challenge for turfgrass management. Injudicious use of herbicides for controlling this weed has led to resistance issues and environmental concerns. Site-specific weed control offers an opportunity to achieve effective weed control with less herbicide use, but it requires the development of a pipeline for weed detection and localization, and a path planning algorithm. To achieve this, unmanned aerial system (UAS) based RGB imagery of P. annua plants in bermudagrass turf was collected at different weed growth stages at two locations in Texas: Deer Park and College Station. A CNN (YOLO11) and a transfer (RTDETRD) model were evaluated for weed detection. The results showed that the YOLO11n model achieved the highest F1-score (0.64) and mAP@0.50 (0.68), while the RTDETRD-x model achieved the lowest F1-score (0.52) and mAP@0.50 (0.51). The geo-transformation function transforms image coordinates into a world coordinate system with centimeter-level accuracy (mean error =1.5 cm). However, the precision of the transformation depends on the quality of the orthophoto georeferencing. Additionally, the path planning algorithm showed a significant reduction (37.7%) in travel distance compared to the original weed-model-derived distance. The research highlighted the potential of UAS-based imagery for weed detection and localization in turfgrass. Further improvements are needed to enhance model performance by modifying the model architecture (e.g., input image size, hyperparameters) and evaluating its robustness across different weed growth stages and turfgrass species.