AUTHOR=Jin Xianjin , Zhang Jinheng , Wang Fei , Zhao Mengyan , Wang Yunshuang , Yang Jianping , Wu Jinfeng , Zhou Bing 

TITLE=PHRF-RTDETR: a lightweight weed detection method for upland rice based on RT-DETR

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1556275

DOI=10.3389/fpls.2025.1556275

ISSN=1664-462X

ABSTRACT=IntroductionWeed poses a greater threat to rice yield and quality in upland environments compared to paddy fields. Effective weed detection is a critical prerequisite for intelligent weed control technologies. However, the current weed detection methods for upland rice often struggle to achieve a balance between accuracy and lightweight design, significantly hindering the practical application and widespread adoption of intelligent weeding technologies in real-world agricultural scenarios. To address this issue, we enhanced the baseline model RT-DETR and proposed a lightweight weed detection model for upland rice, named PHRF-RTDETR.MethodsFirst, we propose a novel lightweight backbone network, termed PGRNet, to replace the original computationally intensive feature extraction network in RT-DETR. Second, we integrate HiLo, a mechanism excluding parameter growth, into the AIFI module to enhance the model’s capability of capturing multi-frequency features. Furthermore, the RepC3 block is optimized by incorporating the RetBlock structure, resulting in RetC3, which effectively balances feature fusion and computational efficiency. Finally, the conventional GIoU loss is replaced with the Focaler-WIoUv3 loss function to significantly improve the model’s generalization performance.ResultsThe experimental results show that PHRF-RTDETR achieves precision, recall, mAP50, and mAP50:95 scores of 92%, 85.6%, 88.2%, and 76.6%, respectively, with all metrics deviating by less than 1.7 percentage points from the baseline model in upland rice weed detection. In terms of lightweight indicators, PHRF-RTDETR achieved reductions in floating-point operations, parameter count, and model size by 59.3%, 53.7%, and 53.9%, respectively, compared to the baseline model. Compared with the traditional target detection models of Faster R-CNN and SSD, YOLO series models, and RT-DETR series models, the PHRF-RTDETR model effectively balances lightweight and accuracy performance for weed detection in upland rice.DiscussionOverall, the PHRF-RTDETR model demonstrates potential for implementation in the detection modules of intelligent weeding robots for upland rice systems, offering dual benefits of reducing agricultural production costs through labor efficiency and contributing to improved food security in drought-prone regions.