In ordered to facilitate the investigation of desert grassland plant species and their distribution in the whole Ningxia region, plant identification methods need to be studied. To address the problems of large number of parameters in YOLO v5s model, it is not easy to recognize plants in complex backgrounds, and a lightweight model of plant target recognition in complex backgrounds, YOLO v5s-CBD, was proposed. The improved model YOLO v5s-CBD introduced the BoTNet with Transformer module into the feature extraction network, to combine convolution and self-attention to improve the feeling field of the model. At the same time, coordinate attention was incorporated into the feature extraction network to effectively capture the relationship between channel and position and improve the feature extraction ability of the model. In terms of loss calculation, the SIoU function was introduced to calculate the regression loss to solve the problem of mismatch between the prediction box and the real box. Using depthwise separable convolution to reduce model volume. The experimental results showed that the model YOLO v5s-CBD infers a single image in only 8ms, a model volume of 8.9MB, a precision of 95.1%, a recall of 92.9%, a F1 value of 94.0%, and a mean average precision of 95.7% in a single Nvidia GTX A5000 GPU, and a mean average precision of 80.09% in the VOC dataset. Compared with YOLO v3-tiny, YOLO v4-tiny and YOLO v5s, the improved models reduced model volume and improved mean average precision. The model YOLO v5s-CBD had good robustness in both public dataset and Ningxia desert grassland plant dataset, faster inference speed and easy to deploy. It was applied in Ningxia desert grassland mobile plant image recognition APP and fixed ecological information observation platform, which can be used to investigate the species and distribution of desert grassland plants in the whole region of Ningxia, and long-term observation and tracking of Dashuikeng, Huangjichang, Mahuangshan and other places, Yanchi County, Ningxia.