Weeds in paddy fields compete with rice for nutrients and cause pests and diseases, greatly affecting rice yield. Accurate weed detection is vital for implementing variable spraying with unmanned aerial vehicles (UAV) for weed control. Therefore, this paper presents an improved weed detection algorithm, YOLOv10n-FCDS (YOLOv10n with FasterNet, CGBlock, Dysample, and Structure of Lightweight Detection Head), using UAV images of Sagittaria trifolia in rice fields as the research object, to address challenges like the detection of small targets, obscured weeds and weeds similar to rice. We enhanced the YOLOv10n model by incorporating FasterNet as the backbone for better small target detection. CGBlock replaced standard convolution and SCDown modules to improve the detection ability of obscured weeds, while DySample enhanced discrimination between weeds and rice. Additionally, we proposed a lightweight detection head based on shared convolution and scale scaling, maintaining accuracy while reducing model parameters. Ablation studies revealed that YOLOv10n-FCDS achieved a 2.6% increase in mean average precision at intersection over union 50% for weed detection, reaching 87.4%. The model also improved small target detection (increasing mAP50 by 2.5%), obscured weed detection (increasing mAP50 by 2.8%), and similar weed detection (increasing mAP50 by 3.0%). In conclusion, YOLOv10n-FCDS enables effective weed detection, supporting variable spraying applications by UAVs in rice fields.

Barnyard grass is a major noxious weed in paddy fields. Accurate and efficient identification of barnyard grass is crucial for precision field management. However, existing deep learning models generally suffer from high parameter counts and computational complexity, limiting their practical application in field scenarios. Moreover, the morphological similarity, overlapping, and occlusion between barnyard grass and rice pose challenges for reliable detection in complex environments. To address these issues, this study constructed a barnyard grass detection dataset using high-resolution images captured by a drone equipped with a high-definition camera in rice experimental fields in Haicheng City, Liaoning Province. A lightweight field barnyard grass detection model, YOLOv8n-SSDW, was proposed to enhance detection precision and speed. Based on the baseline YOLOv8n model, a novel Separable Residual Coord Conv (SRCConv) was designed to replace the original convolution module, significantly reducing parameters while maintaining detection accuracy. The Spatio-Channel Enhanced Attention Module (SEAM) was introduced and optimized to improve sensitivity to barnyard grass edge features. Additionally, the lightweight and efficient Dysample upsampling module was incorporated to enhance feature map resolution. A new WIoU loss function was developed to improve bounding box classification and regression accuracy. Comprehensive performance analysis demonstrated that YOLOv8n-SSDW outperformed state-of-the-art models. Ablation studies confirmed the effectiveness of each improvement module. The final fused model achieved lightweight performance while improving detection accuracy, with a 2.2% increase in mAP_50, 3.8% higher precision, 0.6% higher recall, 10.6% fewer parameters, 9.8% lower FLOPs, and an 11.1% reduction in model size compared to the baseline. Field tests using drones combined with ground-based computers further validated the model’s robustness in real-world complex paddy environments. The results indicate that YOLOv8n-SSDW exhibits excellent accuracy and efficiency. This study provides valuable insights for barnyard grass detection in rice fields.

Uniform spraying by conventional plant protection drones often results in low herbicide utilization efficiency and environmental contamination, both of which are critical issues in agricultural production. To address these challenges, this study proposed a precision weed management system for maize fields that combines an improved YOLOv11n-OSAW detection model with DJI drones for variable-rate herbicide application. The YOLOv11n-OSAW model was enhanced with Omni-dimensional Dynamic Convolution (OD-Conv), the SEAM attention mechanism, a lightweight ADown module, and the Wise-IoU (WIoU) loss function, aiming to improve the detection accuracy of small and occluded weeds in maize fields. When the model was deployed on an uncrewed aerial vehicle (UAV) operating at 5 m altitude, it achieved mean Average Precision mAP@0.5 values of 97.8% and 97.0% for gramineous and broad-leaved weeds, respectively—representing increases of 2.9 and 1.6 percentage points over the baseline YOLOv11n model. Weed distribution maps generated from the detection results were used to develop site-specific herbicide prescription maps, guiding the drone to implement targeted spraying. Water-sensitive paper analysis verified that the system ensured effective droplet deposition and uniform coverage across different application rate areas. This integrated workflow, covering UAV image acquisition, weed detection, variable-rate application, and effect assessment, reduced herbicide consumption by 20.25% compared with conventional uniform spraying (450 L/ha) while maintaining excellent weed control efficiency and reducing environmental risks. The findings demonstrate that the proposed system provides a practical and sustainable solution for weed management in maize fields.