Comparison of Deep Learning Models for Weed Detection and Classification in Wheat Fields Based on UAV Imagery

Weed infestation remains a major constraint in wheat production, highlighting the need for accurate and scalable monitoring approaches. Recent advances in unmanned aerial vehicle (UAV) and deep learning (DL) have created new opportunities for field-scale weed detection. This study evaluates the performance of three DL models (Mask R-CNN, YOLO26s-seg, and RT-DETR-L) for detecting common weed species in a 4-hectare rain-fed wheat field in Mongolia using UAV-acquired images. The following three weed species were identified as the most abundant under the dry environmental conditions of 2025: Linaria buriatica, Neneo pulla, and Artemisia scoparia. The models were evaluated using Precision, Recall, and F1-score. The detection F1-score for the L. buriatica, N. pulla and A. scoparia was 0.642, 0.568 and 0.163 for Mask R-CNN; 0.615, 0.655, 0.335 for YOLO26s-seg; and 0.693, 0.647, 0.275 for RT-DETR-L, respectively. Segmentation F1-score values were 0.659, 0.600, 0.179 for Mask R-CNN, whereas YOLO26s-seg achieved 0.613, 0.658, 0.326, respectively. RT-DETR-L achieved the best overall detection performance for the dominant species, while YOLO26s-seg showed the strongest detection of minority species, and Mask R-CNN produced the most accurate segmentation boundaries. Despite the imbalance in weed instances their morphological characteristics influenced the performance of the model. These findings demonstrate the feasibility of UAV imagery and DL models in precise weed management practices in Mongolia.