The sample pictures of coffee and citrus pests and diseases were collected,and an attention module was added to the super-resolution reconstruction network of the original SRGAN by using TensorFlow deep learning framework.The visual quality,peak sig-nal-to-noise ratio and structured similarity index of the reconstructed image were analyzed.The results showed that the peak signal-to-noise ratio of the designed model was improved by 2.23,and the structural similarity was enhanced by 7%,after comparing with the original SRGAN mode.Better visuals could be obtained in terms of detail texture,and the accuracy of the reconstructed image clas-sification was improved by about 4.42 percentage points.Therefore,the model designed could be used for the expansion of samples of plant pests and diseases with small sample properties.
super-resolution reconstructionattention mechanismpests and diseasespeak signal-to-noise ratio(PSNR)structur-al similarity(SSIM)
NETL
NSTL
万方数据
