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.
翻译:我们为管理虾塘水质提供了一个决策支持系统,该系统以新颖的方式使用各种数据和深层学习模型,提供24小时的预报和对水质参数的异常探测,为虾农提供工具,积极主动地避免生长环境差,从而优化增长,减少鱼量流失的风险。这是农民被迫管理池塘的主要转变,他们不得不通过被动地纠正水质差的情况。据我们所知,我们首先将变换器作为一种异常现象检测模型,并首先对水产养殖问题普遍采用异常现象检测。我们的技术贡献包括调整预测网以适应多变数据,改造变换器和注意模型,以便将天气预报数据纳入它们的解密器。我们达到12%的平均绝对误差,用于溶氧预测,我们演示两个异常检测案例研究。该系统在商业虾农场部署的第二年成功运行。