A Deep Learning Architecture for Smart Fish Farm Management and Early Mortality Prediction

Abstract

The rapid growth of aquaculture has intensified the need for intelligent, data-driven systems capable of improving fish farm productivity, sustainability, and operational efficiency. Traditional manual management approaches remain limited by labor intensity, inconsistent monitoring, and delayed decision-making, especially in dynamic aquatic environments. This study presents a Deep Learning–based Fish Farming Management System that integrates Internet of Things (IoT) sensing technologies with a Bidirectional Long Short-Term Memory (BiLSTM) network to provide real-time monitoring, predictive analytics, and intelligent decision support. IoT-enabled sensors continuously capture water quality and behavioral parameters, which are transmitted to a centralized data-processing architecture for storage, analysis, and automated alerts.  The BiLSTM model primarily focuses on fish mortality classification, where it analyzes temporal patterns in environmental and behavioral data to predict mortality risk with high reliability. In addition, the system supports auxiliary predictive and recommendation functions, including anomaly detection and rule-assisted recommendations for feeding, aeration, and environmental adjustments, which are derived from learned temporal trends and domain constraints rather than independently optimized predictive models. Experimental results for the mortality classification task demonstrate outstanding performance, with the model achieving 96.55% accuracy, 96.97% precision, 96.67% recall, a Cohen’s Kappa score of 0.9482, and an average AUC of 0.9947, significantly outperforming benchmark models.