Influenced by the microscopic sensors and irregular collection method, the pollen images are often disturbed by different degrees of noise and have rotation changes with different angles, which leads to generally low recognition accuracy. In order to solve the problem, a Dominant Gradient encoding based Local Binary Pattern (DGLBP) descriptor was proposed and applied to the recognition of pollen images. Firstly, the gradient magnitude of an image block in the dominant gradient direction was calculated. Secondly, the radial, angular and multiple gradient differences of the image block were calculated separately. Then, the binary coding was performed according to the gradient differences of each image block. The binary coding was assigned weights adaptively with reference to the texture distribution of each local region, and the texture feature histograms of pollen images in three directions were extracted. Finally, the texture feature histograms under different scales were fused, and the Euclidean distance was used to measure the similarity between images. The average correct recognition rates of DGLBP on datasets of Confocal and Pollenmonitor are 94.33% and 92.02% respectively, which are 8.9 percentage points and 8.6 percentage points higher on average than those of other compared pollen recognition methods, 18 percentage points and 18.5 percentage points higher on average than those of other improved LBP-based methods. The experimental results show that the proposed DGLBP descriptor is robust to noise and rotation change of pollen images, and has a better recognition effect.