Abstract: Intelligent control technology of operating speed based on feeding rate is an important means to optimize the efficiency and quality of combine harvester operations. Aiming at the obvious time delay of the traditional feeding rate automatic control technology and the inability to adapt to the actual situation in time when the feeding rate is adjusted. By analyzing the influencing factors of the feeding rate, an image-based deep learning method was used to carry out a research on the classification and recognition method of wheat plant density in the mature stage. By sensing crop density in advance, the operating parameters of the combine harvester can be automatically adjusted. Firstly, a multi-variety and multi-region mature stage wheat plant image dataset was constructed based on vehicle-mounted cameras and UAV images, and classified it into four categories: low density, medium density, high density, and very high density. Next, a density classification recognition model was built based on the lightweight MobileViT-XS network, and trained and tested the model by using the established dataset. Finally, it was compared with VGG16, GoogLeNet, and ResNet. The results showed that the overall recognition accuracy of the MobileViT-XS model reached 91.03%, and the inference time for a single image was 29.5 ms. Compared with VGG16 and ResNet networks, the overall recognition accuracy was 3.51 percentage points and 2.34 percentage points higher respectively. The MobileViT-XS model can effectively accomplish the classification recognition of wheat at different density levels, providing technical support for real-time prediction of wheat feeding rate.