  Automated plant diagnosis using images taken from a distance is often
insufficient in resolution and degrades diagnostic accuracy since the important
external characteristics of symptoms are lost. In this paper, we first propose
an effective pre-processing method for improving the performance of automated
plant disease diagnosis systems using super-resolution techniques. We
investigate the efficiency of two different super-resolution methods by
comparing the disease diagnostic performance on the practical original
high-resolution, low-resolution, and super-resolved cucumber images. Our method
generates super-resolved images that look very close to natural images with
4$\times$ upscaling factors and is capable of recovering the lost detailed
symptoms, largely boosting the diagnostic performance. Our model improves the
disease classification accuracy by 26.9% over the bicubic interpolation method
of 65.6% and shows a small gap (3% lower) between the original result of 95.5%.
