Smart rice disease detection is a key part of intelligent agriculture. To address issues like low efficiency, poor accuracy, and high costs in traditional methods, this paper introduces an enhanced lightweight version of the YOLOv11-RD algorithm, enhancing multi-scale feature extraction through the integration of the enhanced LSKAC attention mechanism and the SPPF module. It also lowers computational complexity and enhances local feature capture through the C3k2-CFCGLU block. The C3k2-CSCBAM block in the neck region reduces the training overhead and boosts target learning in complex backgrounds. Additionally, a lightweight 320 × 320 LSDECD detection head improves small-object detection. Experiments on a rice disease dataset extracted from agricultural operation videos demonstrate that, compared to YOLOv11n, the algorithm improves mAP50 and mAP50-95 by 2.7% and 11.5%, respectively, while reducing the model parameters by 4.58 M and the computational load by 1.1 G. The algorithm offers significant advantages in lightweight design and real-time performance, outperforming other classical object detection algorithms and providing an optimal solution for real-time field diagnosis.

Recognizing traffic signs is crucial for autonomous driving systems, as it significantly impacts their safety and dependability. However, challenges like the diminutive size of objects and intricate background environments limit the effectiveness of current object detection models. To improve small traffic sign detection, this research introduces an enhanced detection algorithm built on YOLOv10. First, a custom-designed layer for detecting small objects is integrated into the neck section of the network, enhancing the feature extraction process for these objects. Second, a refined downsampling module, called Triple-Branch Downsampling (TBD), utilizes a multi-branch structure and hybrid pooling strategy to boost feature extraction efficiency within the model. Finally, the loss function is optimized by integrating the Normalized Wasserstein Distance (NWD) and Wise-MPDIoU mechanisms, increasing the accuracy of bounding box matching and regression. The experimental findings indicate that the enhanced algorithm reaches a mAP@0.5 of 84.8%, marking a 4% increase over YOLOv10. The classification accuracy and recall are 73.4% and 82.9%, respectively. Moreover, the parameter count decreases by approximately 10%, while the computational complexity is reduced by around 5%.

Weed management is an important part of agricultural production and a key factor affecting crop yields. However, weeds in the field have characteristics such as mixed categories, high similarity in morphology with crops, and small detection targets, making detection quite difficult. To address these issues, this paper proposes an improved lightweight model, MBP-RTDETR, based on the RTDETR model. This is a real-time detection algorithm that reduces computational load while effectively enhancing the detection accuracy of small-sized weeds in complex farmland backgrounds. The key improvements include: (1) MSNet, a lightweight backbone network, integrates the MSInit module into the CSPNet architecture, effectively incorporating contextual information to provide enhanced multi-scale feature extraction at low computational cost. (2) The AIFI-SSA module simplifies multiplication into efficient binary masking and accumulation operations by leveraging the binary characteristics of peaks, reducing the number of model parameters while maintaining detection accuracy. (3) The PST-DET architecture replaces the conventional convolutional layers in the Feature Pyramid Network (FPN) with the PST module, thereby reducing computational complexity while preserving critical spatial information. Experimental results demonstrate that the proposed model achieves mAP@50 and mAP@50:95 scores of 93.7% and 75.2%, respectively, outperforming the baseline model across both evaluation metrics. Importantly, this performance gain is accompanied by significant efficiency improvements: the model reduces parameter count, computational complexity (GFLOPs), and model weight by 41.2%, 33.3%, and 41.0%, respectively. These enhancements collectively enable robust and computationally efficient detection of field weeds under complex real-world conditions.