The foundation and premise of implementing precision weeding and intelligent agriculture is the real-time detection and precise identification of weeds in the corn seedling stage. A method for weed detection in straw-covered farmland suitable for the deployment of Jetson TX2 mobile terminal was proposed. This method addressed the issue that the surface environment of conservation tillage mode was complex, weeds were primarily covered by straw residues on the surface, and the detection speed of existing algorithms was not ideal. Building a corn seedling weed identification model by extracting and analyzing the highlevel semantic information from corn seedling weed photos by using deep learning technology. Based on the YOLO v5s model, the network model’s width was decreased to make minor adjustments that balance the model’s detection speed and accuracy. The network model was analyzed by using the TensorRT reasoning acceleration framework, and the integration of the dimensional tensor into the reasoning network allows for the reconstruction and optimization of the network structure while also lowering the computational demand for the model to operate. Each model was trained and tested before migrating and deploying it to the Jetson TX2 mobile platform. The test findings demonstrated that the lightweight enhanced YOLO v5ss, YOLO v5sm, and YOLO v5sl models, which had accuracy rates of 85.7%, 94%, and 95.3%, respectively. The detection speed were sequentially 80f/s, 79.36f/s, 81.97f/s. The YOLO v5sl model’s detection accuracy was 93.6% after Jetson TX2 embedded reasoning acceleration, and its average running time for a single frame image was 35.3ms, which was 77.8% faster than it was before acceleration. It can achieve the detection of corn seedlings while guaranteeing the accuracy of the detection. The real-time detection of weed targets provided technical support for precise weeding operations in fields with limited hardware resources.