Early warning system for nocardiosis in largemouth bass (Micropterus salmoides) based on multimodal information fusion

Abstract

The high-density culture of the recirculating aquaculture system (RAS) makes bacterial and parasitic diseases in fish likely to spread within these systems. Therefore, preventing and controlling the occurrence of fish diseases in RAS is crucial for the future development of aquaculture. However, the current circulating water system lacks early warning measures to prevent and control diseases, resulting in poor accuracy in early warning and detection of diseases. To tackle this challenge, this study introduces an early warning system for largemouth bass (Micropterus salmoides) disease, which is based on You Only Look Once vision 8 (YOLOv8), ByteTrack, Long-Short-Term-Memory (LSTM), and Fuzzy Inference System (FIS). The system utilizes the water quality, surface characteristics, and behavioral traits of diseased fish to predict and prevent disease outbreaks. The system achieved an accuracy of 79.33% for identifying infected body surface features, 80.65% for identifying diseased water quality, and 81.08% for predicting diseased behavior. The experimental results indicate that the early warning system is highly reliable and effective, achieving integrated disease identification accuracy as high as 94.08%. This study enhances the accuracy of early disease warning in fish disease conditions, achieving early warning of nocardiosis in largemouth bass. The study provides crucial technical support for the sustainable and high-quality development of the aquaculture industry.