A Comprehensive Review of Fish Disease Detection Systems Using Machine Learning and Deep Learning Techniques

Abstract

Fish diseases significantly impact aquaculture productivity, causing substantial economic losses and threatening food security. Traditional diagnostic methods are time-consuming, labor-intensive, and unsuitable for real-time monitoring. Recent advances in artificial intelligence, particularly machine learning and deep learning, have enabled automated fish disease detection systems based on image analysis. However, most existing approaches rely solely on visual data and overlook environmental factors such as water quality, which play a crucial role in disease development. This paper presents a concise review of fish disease detection techniques, including machine learning, deep learning, and IoT-based monitoring systems. It identifies key limitations such as lack of multimodal integration and limited real-time applicability. To address these challenges, a novel Multimodal Explainable Fish Disease Detection Network (MEFD-Net) is proposed, integrating image data, sensor data, and temporal modeling. The proposed approach improves accuracy, robustness, and interpretability, making it suitable for smart aquaculture systems.