  In image-based plant diagnosis, clues related to diagnosis are often unclear,
and the other factors such as image backgrounds often have a significant impact
on the final decision. As a result, overfitting due to latent similarities in
the dataset often occurs, and the diagnostic performance on real unseen data
(e,g. images from other farms) is usually dropped significantly. However, this
problem has not been sufficiently explored, since many systems have shown
excellent diagnostic performance due to the bias caused by the similarities in
the dataset. In this study, we investigate this problem with experiments using
more than 50,000 images of cucumber leaves, and propose an anti-overfitting
pretreatment (AOP) for realizing practical image-based plant diagnosis systems.
The AOP detects the area of interest (leaf, fruit etc.) and performs brightness
calibration as a preprocessing step. The experimental results demonstrate that
our AOP can improve the accuracy of diagnosis for unknown test images from
different farms by 12.2% in a practical setting.
