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%.

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.

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.