In order to make the motion blurred images generated by inspection robots recognize efficiently and accurately during the inspection, a motion blurred crop pest image restoration method based on improved DeblurGAN-v2 was proposed. In order to extract important features of image effectively, the channel attention (CA) mechanism was integrated into the backbone grid of DeblurGAN-v2 to make the model pay more attention to detail features, and improve the restoration ability of motion blurred images. In addition, the spatial pyramid pooling (SPP) was used on the top layer of the original model feature extraction network to alleviate the negative impact of multi-scale changes on image restoration and improve the performance of the model on image restoration. The experimental results of the data set established based on the actual farmland environment show that the PSNR and SSIM indexes of the improved algorithm are 26.281 8 dB and 0.947 3 respectively, which are 8 and 7.2 percentage points higher than the original model. Compared with other mainstream models, the experimental results show that the proposed method has a better effect on the actual restoration of blurred images, and has practical application value to solve the problem of image restoration of crop pests with motion blur.