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.

Mulberry (Morus spp.), as an economically significant crop in sericulture and medicinal applications, faces severe threats to leaf yield and quality from pest and disease infestations. Traditional detection methods relying on chemical pesticides and manual observation prove inefficient and unsustainable. Although computer vision and deep learning technologies offer new solutions, existing models exhibit limitations in natural environments, including low recognition rates for small targets, insufficient computational efficiency, poor adaptability to occlusions, and inability to accurately identify structural features such as leaf veins. We propose Mamba-YOLO-ML, an optimized model addressing three key challenges in vision-based detection: Phase-Modular Design (PMSS) with dual blocks enhancing multi-scale feature representation and SSM selective mechanisms and Mamba Block, Haar wavelet downsampling preserving critical texture details, and Normalized Wasserstein Distance loss improving small-target robustness. Visualization analysis of the detection performance on the test set using GradCAM revealed that the enhanced Mamba-YOLO-ML model demonstrates earlier and more effective focus on characteristic regions of different diseases compared with its predecessor. The improved model achieved superior detection accuracy with 78.2% mAP50 and 59.9% mAP50:95, outperforming YOLO variants and comparable Transformer-based models, establishing new state-of-the-art performance. Its lightweight architecture (5.6 million parameters, 13.4 GFLOPS) maintains compatibility with embedded devices, enabling real-time field deployment. This study provides an extensible technical solution for precision agriculture, facilitating sustainable mulberry cultivation through efficient pest and disease management.

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%.

Reliable operating-state recognition of gas pressure regulators is essential for pressure stability, operational safety, and supply continuity in urban natural gas distribution networks. However, SCADA pressure–flow signals from regulating stations are often affected by non-stationary noise, impulsive disturbances, limited fault-state samples, and short-window temporal fluctuations, which reduce the reliability of data-driven recognition. To address these issues, this study proposes K²-TLNet, a noise-state-guided fault-state recognition framework for gas pressure regulation processes. The framework integrates adaptive Kalman filtering, training-only KMeans-SMOTE, parallel temporal convolutional network–long short-term memory feature extraction, and a Noise-Aware Gated Fusion mechanism. Adaptive Kalman filtering is used to generate denoised pressure–flow sequences and extract innovation-residual-based noise descriptors. These descriptors guide the fusion module to adaptively balance local transient features from the temporal convolutional network and contextual temporal features from long short-term memory. A field-SCADA-background-based semi-synthetic dataset was constructed using real operating records and mechanism-informed fault-state emulation rules. Experimental results demonstrate that K²-TLNet achieves 98.50% accuracy and 98.20% Macro-F1, while maintaining strong robustness under Gaussian and impulsive noise disturbances.

Maritime UAV surveillance requires rapid, accurate identification of small surface targets amidst volatile sea states. Conventional detectors typically degrade under intense wave clutter, variable lighting, and edge computing constraints. To address these limitations, this paper presents YOLOv11-UAV, a compact framework optimized for real-time edge deployment. We introduce an SPPFLSC module integrating large separable kernel attention (LSKA-C) to extend the receptive field with minimal computational overhead. Additionally, an optimized C3k2-EVA block utilizing sparse decomposed large separable kernel attention (SDLSKA) improves feature representation and processing throughput. To refine localization for low-contrast objects, a high-resolution prediction head is integrated into the multi-scale pipeline. Quantitative evaluations on the SeaDronesSee benchmark demonstrate that YOLOv11-UAV yields substantial precision and recall gains, validating its efficacy for airborne maritime reconnaissance.

Mulberry (Morus alba L.) is an important perennial woody crop for sericulture, medicinal resource development, and ecological conservation. Accurate disease and pest detection in natural mulberry orchards remains challenging due to dense foliage, severe occlusion, varying illumination, and the presence of small lesions with weak texture features. To address these problems, this study proposes CLH-DETR, a lightweight detection framework improved from the Real-Time Detection Transformer (RT-DETR) for mulberry disease and pest detection under natural field conditions. The proposed model introduces four targeted improvements: Cross-Stage Partial Progressive Multi-Scale Feature Aggregation (CSP-PMSFA) is designed to strengthen multi-scale lesion feature extraction while reducing redundant computation, the Lesion Detail Enhancement Block (LDEB) is designed to enhance weak lesion details and responses related to lesion boundaries, Haar Wavelet Downsampling (HWD) is adopted to preserve texture and structural information during feature downsampling, and Focaler-ShapeIoU is introduced to improve bounding-box regression for irregular disease regions. Experiments on the Mulberry Disease Dataset show that CLH-DETR achieves 80.1% precision, 75.3% $mA{P}_{50}$, and 55.9% $mA{P}_{50:95}$, improving the RT-DETR baseline by 3.9, 2.3, and 2.2 percentage points, respectively. Meanwhile, the number of parameters decreases from 19.9 M to 13.4 M, and the computational cost is reduced from 57.1 G to 46.4 G FLOPs. Compared with representative YOLO- and DETR-based detectors, CLH-DETR provides a favorable balance between detection accuracy and model complexity. When deployed on an iPhone 16 Pro using CoreML with FP16 precision, the model achieved an average latency of 16.5 ms per image, corresponding to 60.6 FPS. This result indicates its potential for edge-assisted mulberry disease inspection under the tested hardware conditions.