Single-Class and Three-Class Detection of Foxtail Species Using YOLOv8, YOLO11, and YOLO26 Architectures

Automatic weed detection represents a tool of interest for precision agriculture and crop monitoring. In this work, the performance of YOLOv8, YOLO11, and YOLO26 was evaluated for the detection of the Foxtail species Cenchrus setaceus and Cenchrus alopecuroides (grouped into a single class), Cortaderia jubata, and Digitaria insularis, due to their presence in Colombia and the large number of images available for their training and evaluation. Images obtained from iNaturalist, Wikimedia Commons, Pl@ntNet and the authors’ own collection were used to construct a dataset of 3156 images. The models were evaluated based on the metrics of Precision, Recall, F1-score, mAP@50, mAP@50-95, and inference speed. The results showed the YOLO26 family remained competitive in each experiment, with the YOLO26s model standing out for both configurations. In addition, the quality and consistency of the dataset labels have a great influence on training efficiency. The results obtained demonstrated the potential of YOLO architectures for the automatic identification of Foxtail species.