Ginger diseases and pests have posed a serious threat to the yield in recent years. However, the artificial and mechanical application cannot fully meet the large-scale production at present, due to the slow overall progress and the low degree low of intelligence. This study aims to develop the intelligent application equipment of ginger for the high efficiency and accuracy of the intelligent vehicle. A lightweight model was proposed to realize the high-performance deployment of the ginger leaf disease and pest detection on mobile terminals using improved YOLOv5s. Ghost module of GhostNet was selected to replace the convolutional layers in the original YOLOv5s neural network, except the first layer. Ghost BottleNeck was used to replace the Resunit residual component in the original C3 concentrate-comprehensive convolution block. The lightweight of the network model was obtained to reduce the number of parameters and the amount of calculation. At the same time, the memory consumption was reduced in the model weight file. CA attention mechanism module was added after the C3 block in the feature fusion network, in order to improve the recognition and positioning accuracy. The reason was that the lightweight of the model caused the feature loss, when the neural network was used to extract the features of the image. The experimental results show that the number of parameters of the improved YOLOv5s model was 3.76×10⁶M, which was 52.0% of the original. The computational complexity was 8.4GFLOPs, which was 50.6% of the original. The size of the weight file was 7.79MB, which was 55.2% of the original. The average precision and average precision reached 80.5% and 83.8%, respectively, which were 1.3 and 1.5 percentage points higher than those of the original model. The improved model was greatly reduced the number of parameters, calculation amount and weight file size for the high detection accuracy, compared with Fast-RCNN, SSD, YOLOv4, YOLOv5s and Tea-YOLOv5s target detection models. The missed and false detection of image targets were also reduced, compared with the YOLOv5s model. And the improved network model was required less hardware conditions. The performance of the improved model was verified on mobile terminals. The Ginger-YOLOv5s model was deployed on the Jetson Orin NX development board, where the detection code was rewritten in C++. The model was accelerated using TensorRT high-performance operator, Int8 quantization processing, CUDA rewriting preprocessing and multi-thread processing. The final FPS reached 74.3, which was fully met the requirements of operation efficiency in the application machinery for the real-time detection of ginger leaf diseases and pests. The finding can provide the technical support for the migration and deployment of the model on the ginger application vehicle.