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Many applications for the automated diagnosis of plant disease have beendeveloped based on the success of deep learning techniques. However, theseapplications often suffer from overfitting, and the diagnostic performance isdrastically decreased when used on test datasets from new environments. In thispaper, we propose LeafGAN, a novel image-to-image translation system with ownattention mechanism. LeafGAN generates a wide variety of diseased images viatransformation from healthy images, as a data augmentation tool for improvingthe performance of plant disease diagnosis. Thanks to its own attentionmechanism, our model can transform only relevant areas from images with avariety of backgrounds, thus enriching the versatility of the training images.Experiments with five-class cucumber disease classification show that dataaugmentation with vanilla CycleGAN cannot help to improve the generalization,i.e., disease diagnostic performance increased by only 0.7% from the baseline.In contrast, LeafGAN boosted the diagnostic performance by 7.4%. We alsovisually confirmed the generated images by our LeafGAN were much better qualityand more convincing than those generated by vanilla CycleGAN. The code isavailable publicly at: https://github.com/IyatomiLab/LeafGAN.

LeafGAN: An Effective Data Augmentation Method for Practical Plant Disease Diagnosis