Deep Learning for Prawn Farming: Forecasting and Anomaly Detection

TL;DR

This paper introduces a novel deep learning-based decision support system for prawn pond water quality management, enabling 24-hour forecasting and anomaly detection to improve aquaculture outcomes.

Contribution

It is the first to apply Transformer models for anomaly detection in aquaculture water quality management and adapts existing models for multivariate and weather-integrated data.

Findings

Achieved 12% MAPE in dissolved oxygen forecasting

Successfully deployed the system in a commercial farm for over a year

Demonstrated effective anomaly detection through case studies

Abstract

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.