  The collection of high-resolution training data is crucial in building robust
plant disease diagnosis systems, since such data have a significant impact on
diagnostic performance. However, they are very difficult to obtain and are not
always available in practice. Deep learning-based techniques, and particularly
generative adversarial networks (GANs), can be applied to generate high-quality
super-resolution images, but these methods often produce unexpected artifacts
that can lower the diagnostic performance. In this paper, we propose a novel
artifact-suppression super-resolution method that is specifically designed for
diagnosing leaf disease, called Leaf Artifact-Suppression Super Resolution
(LASSR). Thanks to its own artifact removal module that detects and suppresses
artifacts to a considerable extent, LASSR can generate much more pleasing,
high-quality images compared to the state-of-the-art ESRGAN model. Experiments
based on a five-class cucumber disease (including healthy) discrimination model
show that training with data generated by LASSR significantly boosts the
performance on an unseen test dataset by nearly 22% compared with the baseline,
and that our approach is more than 2% better than a model trained with images
generated by ESRGAN.
