A lightweight model named as YOLOv8n-DSSW was developed based on YOLOv8n to effectively control the damage of weeds in maize seedlings， improve the accuracy of identifying weeds， and meet the requirements for mobile deployment.The C2f_Dual module was integrated to achieve initial lightweight.The SPDConv module was used to enhance the capability of identification for small targets and low-resolution images while further reducing the complexity of model.The SPPELAN module was incorporated to strengthen the perception of multi-scale feature.The regression loss function used Inner WIOU to assist in optimizing the accuracy of bounding box localization and allocating weights to anchor boxes of different qualities， thereby overall improving the accuracy and robustness of identification.The results showed that the proposed model achieved an increase of 3.4 percentage points in precision， 1.9 percentage points in recall， and 2.4 percentage points in mean average precision （mAP） in identifying weeds and seedling of maize.The weight of model was reduced by 15.9% and floating-point operations （FLOPs） decreased by 14.8%.This model maintained high performance of identification in complex field environments including weak features of weeds， overlapping weeds with maize seedlings， and similar morphology.Its compact size and computational efficiency make it suitable for the deployment in mobile devices for controlling weeds during the stage of seedling.