MortCam: An Artificial Intelligence-aided fish mortality detection and alert system for recirculating aquaculture

Abstract

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.