Fish Disease Classification Through Deep Fusion of Visual and Environmental Data

The study’s significant contribution is the development of a multimodal deep-learning framework that integrates fish images with 72-hour environmental time-series data for seven-class fish-health classification. It introduces a dual-branch CNN–BiGRU architecture and demonstrates that multimodal fusion can outperform image-only and sensor-only approaches, achieving 98.36% accuracy and greater robustness when visual symptoms are ambiguous.