Abstract: Super-resolution reconstruction plays an important role in the field of intelligent agriculture. In view of the shortcomings in the generation accuracy and performance of the image super-resolution recently, a generation adversarial network based on the model of SRGAN was discussed and constructed. In this model, the convolution network edge detection loss was introduced into the loss function, and more details of the image were maintained in the generated high-resolution image. A series of experiments were carried out using sets of crop/weed field image data as test sets, compared with bicubic interpolation, SRGAN, ESRGAN and GAN model with depth residuals, the PSNR was 8.242 dB, 5.521 dB, 3.079 dB and 2.339 dB higher, the SSIM was 0.143, 0.089, 0.051 and 0.018 higher, and the recognition accuracy in AI was 10.6%, 3.5%, 3.9% and 2.7% higher, respectively. It provided ideas and methods for the related research of other images of weeds in the field, and prepared the preliminary data for application research of image classification of field weeds.