Ning Jin

Precision weeding is crucial for maximizing crop yields and minimizing herbicide use. However, deploying standard deep learning models in agriculture faces challenges due to the high morphological diversity of weeds and the computational constraints of edge devices. Hence, this study proposes MCS-YOLO, a lightweight detection model based on the YOLOv8 architecture. First, a channel-level Mamba module is integrated into the backbone to model long-range feature dependencies and enhance global texture representation. The LMAB module employs parallel depthwise separable convolutions with varying receptive fields and coordinate attention to improve multi-scale weed discrimination. To mitigate feature blurring and misalignment during upsampling, the LCAU module adopts dynamic offset sampling beyond fixed interpolation methods. Finally, the SCS-Head integrates dual-branch depthwise separable convolution with channel shuffling to reduce parameter redundancy while preserving efficient feature expression. Experimental results on the Weed-Crop dataset demonstrate that MCS-YOLO achieves 76.4% mAP@50 and 38.3% mAP@50–95, outperforming YOLOv8s by 3.1% and 1.5%, respectively. Furthermore, the parameter count is reduced by 20.7%, from 11.13 M to 8.83 M, and GFLOPs are reduced by 39.6%, from 28.5 to 17.2. These results confirm that MCS-YOLO effectively balances a lightweight design with high detection accuracy, offering a viable solution for real-time weed detection and automated weeding on embedded agricultural platforms.

Aiming to address the problems of scale uncertainty, feature extraction difficulty, model training difficulty, poor real-time performance, and sample imbalance in low-resolution images for gender recognition, this study proposes an efficient pedestrian gender recognition model based on key area feature extraction and fusion. First, a discrete cosine transform (DCT)-based local super-resolution preprocessing algorithm is developed for facial image gender recognition. Then, a key area feature extraction and information fusion model is designed, using additional appearance features to assist in gender recognition and improve accuracy. The proposed model preprocesses images using the DCT image fusion and super-resolution methods, dividing pedestrian images into three regions: face, hair, and lower body (legs) regions. Features are separately extracted from each of the three image regions. Finally, a multi-region local gender recognition classifier is designed and trained, employing decision-level information fusion. The results of the three local classifiers are fused using a Bayesian computation-based fusion strategy to obtain the final recognition result of a pedestrian’s gender. This study uses surveillance video data to create a dataset for experimental comparison. Experimental results demonstrate the superiority of the proposed approach. The facial model (DCT-PFSR-CNN) achieved the best accuracy of 89% and an F1-Score of 0.88. Furthermore, the complete pedestrian model (MPGRM) attained an mAP of 0.85 and an AUC of 0.86, surpassing the strongest baseline (HDFL) by 2.4% in mAP and 2.3% in AUC. These results confirm the high application potential of the proposed method for gender recognition in real-world surveillance scenarios.