Mortality is an important production and fish welfare indicator in aquaculture. Unusual mortality patterns can be associated with abiotic or/and biotic stresses on fish in recirculating aquaculture systems (RAS). Real or near real-time mortality tracking can provide valuable inputs to farm managers, to make informed RAS management decisions and address root causes in an effort to prevent mass mortality events. While traditional systems use infrequent human operator observation and tracking - often in conjunction with an underwater camera - the proposed tool (i.e., ‘MortCam’) augments this approach with Artificial Intelligence (AI) and Internet of Things (IoT) deployed at the Edge to provide round-the-clock mortality monitoring and trigger alerts when mortality thresholds are exceeded. MortCam consists of an imaging sensor integrated with an edge computing device, customized for underwater applications. MortCam was deployed in a 150 m³ circular dual-drain RAS tank at 0.6 m above the bottom drain plate to acquire the imagery data in both ambient and supplemental light conditions. The images were collected every fifteen minutes for 90 days. Acquired images were annotated either as ‘alive’ or ‘dead’ fish and split into training (70 %), validation (20 %), and test (10 %) datasets to train a custom YOLOv7 mortality detection model. The optimized mixed model achieved a mean average precision (mAP) and F1 score of 93.4 % and 0.89, respectively. Additionally, the model performed well in terms of mortality count and was found robust despite changes in the imaging conditions. The model was deployed on the MortCam to achieve round-the-clock autonomous mortality monitoring. The system reliably generated email and text alerts to notify fish production staff of unusual mortality events.

死亡率是水产养殖中重要的生产和鱼类福利指标。不寻常的死亡率模式可能与循环水产养殖系统 (RAS) 中鱼类受到的非生物或/和生物胁迫有关。实时或近乎实时的死亡率跟踪可以为农场管理者提供有价值的信息，以做出明智的 RAS 管理决策并解决根本原因，以防止大规模死亡事件。虽然传统系统很少使用人工操作员观察和跟踪——通常与水下摄像机结合使用——但建议的工具（即“MortCam”）通过部署在边缘的人工智能 (AI) 和物联网 (IoT) 增强了这种方法，以提供全天候死亡率监测并在超过死亡率阈值时触发警报。MortCam 由一个集成了边缘计算设备的成像传感器组成，专为水下应用而定制。MortCam 部署在 150 m^3个底部排水板上方 0.6 m 处的圆形双排水 RAS 罐，用于在环境光和补充光条件下获取图像数据。在 90 天内每 15 分钟收集一次图像。获取的图像被注释为“活”或“死”鱼，并分为训练 (70%)、验证 (20%) 和测试 (10%) 数据集，以训练自定义 YOLOv7 死亡率检测模型。优化后的混合模型的平均精度 (mAP) 和 F1 分数分别为 93.4% 和 0.89。此外，该模型在死亡率方面表现良好，并且尽管成像条件发生了变化，但仍稳健。该模型部署在 MortCam 上，实现全天候自主死亡率监测。该系统可靠地生成电子邮件和文本警报，以通知鱼类生产人员异常死亡事件。