  The flatfish is a major farmed species consumed globally in large quantities.
However, due to the densely populated farming environment, flatfish are
susceptible to injuries and diseases, making early disease detection crucial.
Traditionally, diseases were detected through visual inspection, but observing
large numbers of fish is challenging. Automated approaches based on deep
learning technologies have been widely used, to address this problem, but
accurate detection remains difficult due to the diversity of the fish and the
lack of the fish disease dataset. In this study, augments fish disease images
using generative adversarial networks and image harmonization methods. Next,
disease detectors are trained separately for three body parts (head, fins, and
body) to address individual diseases properly. In addition, a flatfish disease
image dataset called \texttt{FlatIMG} is created and verified on the dataset
using the proposed methods. A flash salmon disease dataset is also tested to
validate the generalizability of the proposed methods. The results achieved
12\% higher performance than the baseline framework. This study is the first
attempt to create a large-scale flatfish disease image dataset and propose an
effective disease detection framework. Automatic disease monitoring could be
achieved in farming environments based on the proposed methods and dataset.
