  We present a decision support system for managing water quality in prawn
ponds. The system uses various sources of data and deep learning models in a
novel way to provide 24-hour forecasting and anomaly detection of water quality
parameters. It provides prawn farmers with tools to proactively avoid a poor
growing environment, thereby optimising growth and reducing the risk of losing
stock. This is a major shift for farmers who are forced to manage ponds by
reactively correcting poor water quality conditions. To our knowledge, we are
the first to apply Transformer as an anomaly detection model, and the first to
apply anomaly detection in general to this aquaculture problem. Our technical
contributions include adapting ForecastNet for multivariate data and adapting
Transformer and the Attention model to incorporate weather forecast data into
their decoders. We attain an average mean absolute percentage error of 12% for
dissolved oxygen forecasts and we demonstrate two anomaly detection case
studies. The system is successfully running in its second year of deployment on
a commercial prawn farm.
